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TensorFlow

100 announcements (!) from Google Cloud Next '17

By | machinelearning, TensorFlow

San Francisco — What a week! Google Cloud Next ‘17 has come to the end, but really, it’s just the beginning. We welcomed 10,000+ attendees including customers, partners, developers, IT leaders, engineers, press, analysts, cloud enthusiasts (and skeptics). Together we engaged in 3 days of keynotes, 200+ sessions, and 4 invitation-only summits. Hard to believe this was our first show as all of Google Cloud with GCP, G Suite, Chrome, Maps and Education. Thank you to all who were here with us in San Francisco this week, and we hope to see you next year.

If you’re a fan of video highlights, we’ve got you covered. Check out our Day 1 keynote (in less than 4 minutes) and Day 2 keynote (in under 5!).

One of the common refrains from customers and partners throughout the conference was “Wow, you’ve been busy. I can’t believe how many announcements you’ve had at Next!” So we decided to count all the announcements from across Google Cloud and in fact we had 100 (!) announcements this week.

For the list lovers amongst you, we’ve compiled a handy-dandy run-down of our announcements from the past few days:

Google Cloud is excited to welcome two new acquisitions to the Google Cloud family this week, Kaggle and AppBridge.

1Kaggle – Kaggle is one of the world’s largest communities of data scientists and machine learning enthusiasts. Kaggle and Google Cloud will continue to support machine learning training and deployment services in addition to offering the community the ability to store and query large datasets.

2AppBridge – Google Cloud acquired Vancouver-based AppBridge this week, which helps you migrate data from on-prem file servers into G Suite and Google Drive.

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Google Cloud brings a suite of new security features to Google Cloud Platform and G Suite designed to help safeguard your company’s assets and prevent disruption to your business: 

3Identity-Aware Proxy (IAP) for Google Cloud Platform (Beta) – Identity-Aware Proxy lets you provide access to applications based on risk, rather than using a VPN. It provides secure application access from anywhere, restricts access by user, identity and group, deploys with integrated phishing resistant Security Key and is easier to setup than end-user VPN.

4Data Loss Prevention (DLP) for Google Cloud Platform (Beta) – Data Loss Prevention API lets you scan data for 40+ sensitive data types, and is used as part of DLP in Gmail and Drive. You can find and redact sensitive data stored in GCP, invigorate old applications with new sensitive data sensing “smarts” and use predefined detectors as well as customize your own.

5Key Management Service (KMS) for Google Cloud Platform (GA) – Key Management Service allows you to generate, use, rotate, and destroy symmetric encryption keys for use in the cloud.

6Security Key Enforcement (SKE) for Google Cloud Platform (GA) – Security Key Enforcement allows you to require security keys be used as the 2-Step verification factor for enhanced anti-phishing security whenever a GCP application is accessed.

7Vault for Google Drive (GA) – Google Vault is the eDiscovery and archiving solution for G Suite. Vault enables admins to easily manage their G Suite data lifecycle and search, preview and export the G Suite data in their domain. Vault for Drive enables full support for Google Drive content, including Team Drive files.

8Google-designed security chip, Titan – Google uses Titan to establish hardware root of trust, allowing us to securely identify and authenticate legitimate access at the hardware level. Titan includes a hardware random number generator, performs cryptographic operations in the isolated memory, and has a dedicated secure processor (on-chip).

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New GCP data analytics products and services help organizations solve business problems with data, rather than spending time and resources building, integrating and managing the underlying infrastructure:

9BigQuery Data Transfer Service (Private Beta) – BigQuery Data Transfer Service makes it easy for users to quickly get value from all their Google-managed advertising datasets. With just a few clicks, marketing analysts can schedule data imports from Google Adwords, DoubleClick Campaign Manager, DoubleClick for Publishers and YouTube Content and Channel Owner reports.

10Cloud Dataprep (Private Beta) – Cloud Dataprep is a new managed data service, built in collaboration with Trifacta, that makes it faster and easier for BigQuery end-users to visually explore and prepare data for analysis without the need for dedicated data engineer resources.

11New Commercial Datasets – Businesses often look for datasets (public or commercial) outside their organizational boundaries. Commercial datasets offered include financial market data from Xignite, residential real-estate valuations (historical and projected) from HouseCanary, predictions for when a house will go on sale from Remine, historical weather data from AccuWeather, and news archives from Dow Jones, all immediately ready for use in BigQuery (with more to come as new partners join the program).

12Python for Google Cloud Dataflow in GA – Cloud Dataflow is a fully managed data processing service supporting both batch and stream execution of pipelines. Until recently, these benefits have been available solely to Java developers. Now there’s a Python SDK for Cloud Dataflow in GA.

13Stackdriver Monitoring for Cloud Dataflow (Beta) – We’ve integrated Cloud Dataflow with Stackdriver Monitoring so that you can access and analyze Cloud Dataflow job metrics and create alerts for specific Dataflow job conditions.

14Google Cloud Datalab in GA – This interactive data science workflow tool makes it easy to do iterative model and data analysis in a Jupyter notebook-based environment using standard SQL, Python and shell commands.

15Cloud Dataproc updates – Our fully managed service for running Apache Spark, Flink and Hadoop pipelines has new support for restarting failed jobs (including automatic restart as needed) in beta, the ability to create single-node clusters for lightweight sandbox development, in beta, GPU support, and the cloud labels feature, for more flexibility managing your Dataproc resources, is now GA.

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New GCP databases and database features round out a platform on which developers can build great applications across a spectrum of use cases:

16Cloud SQL for Postgre SQL (Beta) – Cloud SQL for PostgreSQL implements the same design principles currently reflected in Cloud SQL for MySQL, namely, the ability to securely store and connect to your relational data via open standards.

17Microsoft SQL Server Enterprise (GA) – Available on Google Compute Engine, plus support for Windows Server Failover Clustering (WSFC) and SQL Server AlwaysOn Availability (GA).

18Cloud SQL for MySQL improvements – Increased performance for demanding workloads via 32-core instances with up to 208GB of RAM, and central management of resources via Identity and Access Management (IAM) controls.

19Cloud Spanner – Launched a month ago, but still, it would be remiss not to mention it because, hello, it’s Cloud Spanner! The industry’s first horizontally scalable, globally consistent, relational database service.

20SSD persistent-disk performance improvements – SSD persistent disks now have increased throughput and IOPS performance, which are particularly beneficial for database and analytics workloads. Read these docs for complete details about persistent-disk performance.

21Federated query on Cloud Bigtable – We’ve extended BigQuery’s reach to query data inside Cloud Bigtable, the NoSQL database service for massive analytic or operational workloads that require low latency and high throughput (particularly common in Financial Services and IoT use cases).

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New GCP Cloud Machine Learning services bolster our efforts to make machine learning accessible to organizations of all sizes and sophistication:

22.  Cloud Machine Learning Engine (GA) – Cloud ML Engine, now generally available, is for organizations that want to train and deploy their own models into production in the cloud.

23Cloud Video Intelligence API (Private Beta) – A first of its kind, Cloud Video Intelligence API lets developers easily search and discover video content by providing information about entities (nouns such as “dog,” “flower”, or “human” or verbs such as “run,” “swim,” or “fly”) inside video content.

24Cloud Vision API (GA) – Cloud Vision API reaches GA and offers new capabilities for enterprises and partners to classify a more diverse set of images. The API can now recognize millions of entities from Google’s Knowledge Graph and offers enhanced OCR capabilities that can extract text from scans of text-heavy documents such as legal contracts or research papers or books.

25Machine learning Advanced Solution Lab (ASL) – ASL provides dedicated facilities for our customers to directly collaborate with Google’s machine-learning experts to apply ML to their most pressing challenges.

26. Cloud Jobs API – A powerful aid to job search and discovery, Cloud Jobs API now has new features such as Commute Search, which will return relevant jobs based on desired commute time and preferred mode of transportation.

27Machine Learning Startup Competition – We announced a Machine Learning Startup Competition in collaboration with venture capital firms Data Collective and Emergence Capital, and with additional support from a16z, Greylock Partners, GV, Kleiner Perkins Caufield & Byers and Sequoia Capital.

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New GCP pricing continues our intention to create customer-friendly pricing that’s as smart as our products; and support services that are geared towards meeting our customers where they are:

28Compute Engine price cuts – Continuing our history of pricing leadership, we’ve cut Google Compute Engine prices by up to 8%.

29Committed Use Discounts – With Committed Use Discounts, customers can receive a discount of up to 57% off our list price, in exchange for a one or three year purchase commitment paid monthly, with no upfront costs.

30Free trial extended to 12 months – We’ve extended our free trial from 60 days to 12 months, allowing you to use your $300 credit across all GCP services and APIs, at your own pace and schedule. Plus, we’re introduced new Always Free products — non-expiring usage limits that you can use to test and develop applications at no cost. Visit the Google Cloud Platform Free Tier page for details.

31Engineering Support – Our new Engineering Support offering is a role-based subscription model that allows us to match engineer to engineer, to meet you where your business is, no matter what stage of development you’re in. It has 3 tiers:

  • Development engineering support – ideal for developers or QA engineers that can manage with a response within four to eight business hours, priced at $100/user per month.
  • Production engineering support provides a one-hour response time for critical issues at $250/user per month.
  • On-call engineering support pages a Google engineer and delivers a 15-minute response time 24×7 for critical issues at $1,500/user per month.

32Cloud.google.com/community site – Google Cloud Platform Community is a new site to learn, connect and share with other people like you, who are interested in GCP. You can follow along with tutorials or submit one yourself, find meetups in your area, and learn about community resources for GCP support, open source projects and more.

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New GCP developer platforms and tools reinforce our commitment to openness and choice and giving you what you need to move fast and focus on great code.

33Google AppEngine Flex (GA) – We announced a major expansion of our popular App Engine platform to new developer communities that emphasizes openness, developer choice, and application portability.

34Cloud Functions (Beta) – Google Cloud Functions has launched into public beta. It is a serverless environment for creating event-driven applications and microservices, letting you build and connect cloud services with code.

35Firebase integration with GCP (GA) – Firebase Storage is now Google Cloud Storage for Firebase and adds support for multiple buckets, support for linking to existing buckets, and integrates with Google Cloud Functions.

36Cloud Container Builder – Cloud Container Builder is a standalone tool that lets you build your Docker containers on GCP regardless of deployment environment. It’s a fast, reliable, and consistent way to package your software into containers as part of an automated workflow.

37. Community Tutorials (Beta)  – With community tutorials, anyone can now submit or request a technical how-to for Google Cloud Platform.

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Secure, global and high-performance, we’ve built our cloud for the long haul. This week we announced a slew of new infrastructure updates. 

38. New data center region: California – This new GCP region delivers lower latency for customers on the West Coast of the U.S. and adjacent geographic areas. Like other Google Cloud regions, it will feature a minimum of three zones, benefit from Google’s global, private fibre network, and offer a complement of GCP services.

39. New data center region: Montreal – This new GCP region delivers lower latency for customers in Canada and adjacent geographic areas. Like other Google Cloud regions, it will feature a minimum of three zones, benefit from Google’s global, private fibre network, and offer a complement of GCP services.

40. New data center region: Netherlands – This new GCP region delivers lower latency for customers in Western Europe and adjacent geographic areas. Like other Google Cloud regions, it will feature a minimum of three zones, benefit from Google’s global, private fibre network, and offer a complement of GCP services.

41. Google Container Engine – Managed Nodes – Google Container Engine (GKE) has added Automated Monitoring and Repair of your GKE nodes, letting you focus on your applications while Google ensures your cluster is available and up-to-date.

42. 64 Core machines + more memory – We have doubled the number of vCPUs you can run in an instance from 32 to 64 and up to 416GB of memory per instance.

43. Internal Load balancing (GA) – Internal Load Balancing, now GA, lets you run and scale your services behind a private load balancing IP address which is accessible only to your internal instances, not the internet.

44. Cross-Project Networking (Beta) – Cross-Project Networking (XPN), now in beta, is a virtual network that provides a common network across several Google Cloud Platform projects, enabling simple multi-tenant deployments.

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In the past year, we’ve launched 300+ features and updates for G Suite and this week we announced our next generation of collaboration and communication tools.

45. Team Drives (GA for G Suite Business, Education and Enterprise customers) – Team Drives help teams simply and securely manage permissions, ownership and file access for an organization within Google Drive.

46. Drive File Stream (EAP) – Drive File Stream is a way to quickly stream files directly from the cloud to your computer With Drive File Steam, company data can be accessed directly from your laptop, even if you don’t have much space on your hard drive.

47. Google Vault for Drive (GA for G Suite Business, Education and Enterprise customers) – Google Vault for Drive now gives admins the governance controls they need to manage and secure all of their files, including employee Drives and Team Drives. Google Vault for Drive also lets admins set retention policies that automatically keep what’s needed and delete what’s not.

48. Quick Access in Team Drives (GA) – powered by Google’s machine intelligence, Quick Access helps to surface the right information for employees at the right time within Google Drive. Quick Access now works with Team Drives on iOS and Android devices, and is coming soon to the web.

49. Hangouts Meet (GA to existing customers) – Hangouts Meet is a new video meeting experience built on the Hangouts that can run 30-person video conferences without accounts, plugins or downloads. For G Suite Enterprise customers, each call comes with a dedicated dial-in phone number so that team members on the road can join meetings without wifi or data issues.

50. Hangouts Chat (EAP) – Hangouts Chat is an intelligent communication app in Hangouts with dedicated, virtual rooms that connect cross-functional enterprise teams. Hangouts Chat integrates with G Suite apps like Drive and Docs, as well as photos, videos and other third-party enterprise apps.

51. @meet – @meet is an intelligent bot built on top of the Hangouts platform that uses natural language processing and machine learning to automatically schedule meetings for your team with Hangouts Meet and Google Calendar.

52. Gmail Add-ons for G Suite (Developer Preview) – Gmail Add-ons provide a way to surface the functionality of your app or service directly in Gmail. With Add-ons, developers only build their integration once, and it runs natively in Gmail on web, Android and iOS.

53. Edit Opportunities in Google Sheets – with Edit Opportunities in Google Sheets, sales reps can sync a Salesforce Opportunity List View to Sheets to bulk edit data and changes are synced automatically to Salesforce, no upload required.

54. Jamboard – Our whiteboard in the cloud goes GA in May! Jamboard merges the worlds of physical and digital creativity. It’s real time collaboration on a brilliant scale, whether your team is together in the conference room or spread all over the world.

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Building on the momentum from a growing number of businesses using Chrome digital signage and kiosks, we added new management tools and APIs in addition to introducing support for Android Kiosk apps on supported Chrome devices. 

55. Android Kiosk Apps for Chrome – Android Kiosk for Chrome lets users manage and deploy Chrome digital signage and kiosks for both web and Android apps. And with Public Session Kiosks, IT admins can now add a number of Chrome packaged apps alongside hosted apps.

56. Chrome Kiosk Management Free trial – This free trial gives customers an easy way to test out Chrome for signage and kiosk deployments.

57. Chrome Device Management (CDM) APIs for Kiosks – These APIs offer programmatic access to various Kiosk policies. IT admins can schedule a device reboot through the new APIs and integrate that functionality directly in a third- party console.

58. Chrome Stability API – This new API allows Kiosk app developers to improve the reliability of the application and the system.

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Attendees at Google Cloud Next ‘17 heard stories from many of our valued customers:

59. Colgate – Colgate-Palmolive partnered with Google Cloud and SAP to bring thousands of employees together through G Suite collaboration and productivity tools. The company deployed G Suite to 28,000 employees in less than six months.

60. Disney Consumer Products & Interactive (DCPI) – DCPI is on target to migrate out of its legacy infrastructure this year, and is leveraging machine learning to power next generation guest experiences.

61. eBay – eBay uses Google Cloud technologies including Google Container Engine, Machine Learning and AI for its ShopBot, a personal shopping bot on Facebook Messenger.

62. HSBC – HSBC is one of the world’s largest financial and banking institutions and making a large investment in transforming its global IT. The company is working closely with Google to deploy Cloud DataFlow, BigQuery and other data services to power critical proof of concept projects.

63. LUSH – LUSH migrated its global e-commerce site from AWS to GCP in less than six weeks, significantly improving the reliability and stability of its site. LUSH benefits from GCP’s ability to scale as transaction volume surges, which is critical for a retail business. In addition, Google’s commitment to renewable energy sources aligns with LUSH’s ethical principles.

64. Oden Technologies – Oden was part of Google Cloud’s startup program, and switched its entire platform to GCP from AWS. GCP offers Oden the ability to reliably scale while keeping costs low, perform under heavy loads and consistently delivers sophisticated features including machine learning and data analytics.

65. Planet – Planet migrated to GCP in February, looking to accelerate their workloads and leverage Google Cloud for several key advantages: price stability and predictability, custom instances, first-class Kubernetes support, and Machine Learning technology. Planet also announced the beta release of their Explorer platform.

66. Schlumberger – Schlumberger is making a critical investment in the cloud, turning to GCP to enable high-performance computing, remote visualization and development velocity. GCP is helping Schlumberger deliver innovative products and services to its customers by using HPC to scale data processing, workflow and advanced algorithms.

67. The Home Depot – The Home Depot collaborated with GCP’s Customer Reliability Engineering team to migrate HomeDepot.com to the cloud in time for Black Friday and Cyber Monday. Moving to GCP has allowed the company to better manage huge traffic spikes at peak shopping times throughout the year.

68. Verizon – Verizon is deploying G Suite to more than 150,000 of its employees, allowing for collaboration and flexibility in the workplace while maintaining security and compliance standards. Verizon and Google Cloud have been working together for more than a year to bring simple and secure productivity solutions to Verizon’s workforce.

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We brought together Google Cloud partners from our growing ecosystem across G Suite, GCP, Maps, Devices and Education. Our partnering philosophy is driven by a set of principles that emphasize openness, innovation, fairness, transparency and shared success in the cloud market. Here are some of our partners who were out in force at the show:

69. Accenture – Accenture announced that it has designed a mobility solution for Rentokil, a global pest control company, built in collaboration with Google as part of the partnership announced at Horizon in September.

70. Alooma – Alooma announced the integration of the Alooma service with Google Cloud SQL and BigQuery.

71. Authorized Training Partner Program – To help companies scale their training offerings more quickly, and to enable Google to add other training partners to the ecosystem, we are introducing a new track within our partner program to support their unique offerings and needs.

72. Check Point – Check Point® Software Technologies announced Check Point vSEC for Google Cloud Platform, delivering advanced security integrated with GCP as well as their joining of the Google Cloud Technology Partner Program.

73. CloudEndure – We’re collaborating with CloudEndure to offer a no cost, self-service migration tool for Google Cloud Platform (GCP) customers.

74. Coursera – Coursera announced that it is collaborating with Google Cloud Platform to provide an extensive range of Google Cloud training course. To celebrate this announcement  Coursera is offering all NEXT attendees a 100% discount for the GCP fundamentals class.

75. DocuSign – DocuSign announced deeper integrations with Google Docs.

76. Egnyte – Egnyte announced an enhanced integration with Google Docs that will allow our joint customers to create, edit, and store Google Docs, Sheets and Slides files right from within the Egnyte Connect.

77. Google Cloud Global Partner Awards – We recognized 12 Google Cloud partners that demonstrated strong customer success and solution innovation over the past year: Accenture, Pivotal, LumApps, Slack, Looker, Palo Alto Networks, Virtru, SoftBank, DoIT, Snowdrop Solutions, CDW Corporation, and SYNNEX Corporation.

78. iCharts – iCharts announced additional support for several GCP databases, free pivot tables for current Google BigQuery users, and a new product dubbed “iCharts for SaaS.”

79. Intel – In addition to the progress with Skylake, Intel and Google Cloud launched several technology initiatives and market education efforts covering IoT, Kubernetes and TensorFlow, including optimizations, a developer program and tool kits.

80. Intuit – Intuit announced Gmail Add-Ons, which are designed to integrate custom workflows into Gmail based on the context of a given email.

81. Liftigniter – Liftigniter is a member of Google Cloud’s startup program and focused on machine learning personalization using predictive analytics to improve CTR on web and in-app.

82. Looker – Looker launched a suite of Looker Blocks, compatible with Google BigQuery Data Transfer Service, designed to give marketers the tools to enhance analysis of their critical data.

83. Low interest loans for partners – To help Premier Partners grow their teams, Google announced that capital investment are available to qualified partners in the form of low interest loans.

84. MicroStrategy – MicroStrategy announced an integration with Google Cloud SQL for PostgreSQL and Google Cloud SQL for MySQL.

85. New incentives to accelerate partner growth – We are increasing our investments in multiple existing and new incentive programs; including, low interest loans to help Premier Partners grow their teams, increasing co-funding to accelerate deals, and expanding our rebate programs.

86. Orbitera Test Drives for GCP Partners – Test Drives allow customers to try partners’ software and generate high quality leads that can be passed directly to the partners’ sales teams. Google is offering Premier Cloud Partners one year of free Test Drives on Orbitera.

87. Partner specializations – Partners demonstrating strong customer success and technical proficiency in certain solution areas will now qualify to apply for a specialization. We’re launching specializations in application development, data analytics, machine learning and infrastructure.

88. Pivotal – GCP announced Pivotal as our first CRE technology partner. CRE technology partners will work hand-in-hand with Google to thoroughly review their solutions and implement changes to address identified risks to reliability.

89. ProsperWorks – ProsperWorks announced Gmail Add-Ons, which are designed to integrate custom workflows into Gmail based on the context of a given email.

90. Qwiklabs – This recent acquisition will provide Authorized Training Partners the ability to offer hands-on labs and comprehensive courses developed by Google experts to our customers.

91. Rackspace – Rackspace announced a strategic relationship with Google Cloud to become its first managed services support partner for GCP, with plans to collaborate on a new managed services offering for GCP customers set to launch later this year.

92. Rocket.Chat – Rocket.Chat, a member of Google Cloud’s startup program, is adding a number of new product integrations with GCP including Autotranslate via Translate API, integration with Vision API to screen for inappropriate content, integration to NLP API to perform sentiment analysis on public channels, integration with GSuite for authentication and a full move of back-end storage to Google Cloud Storage.

93. Salesforce – Salesforce announced Gmail Add-Ons, which are designed to integrate custom workflows into Gmail based on the context of a given email.

94. SAP – This strategic partnership includes certification of SAP HANA on GCP, new G Suite integrations and future collaboration on building machine learning features into intelligent applications like conversational apps that guide users through complex workflows and transactions.

95. Smyte – Smyte participated in the Google Cloud startup program and protects millions of actions a day on websites and mobile applications. Smyte recently moved from self-hosted Kubernetes to Google Container Engine (GKE).

96. Veritas – Veritas expanded its partnership with Google Cloud to provide joint customers with 360 Data Management capabilities. The partnership will help reduce data storage costs, increase compliance and eDiscovery readiness and accelerate the customer’s journey to Google Cloud Platform.

97. VMware Airwatch – Airwatch provides enterprise mobility management solutions for Android and continues to drive the Google Device ecosystem to enterprise customers.

98. Windows Partner Program– We’re working with top systems integrators in the Windows community to help GCP customers take full advantage of Windows and .NET apps and services on our platform.

99. Xplenty – Xplenty announced the addition of two new services from Google Cloud into their available integrations: Google Cloud Spanner and Google Cloud SQL for PostgreSQL.

100. Zoomdata – Zoomdata announced support for Google’s Cloud Spanner and PostgreSQL on GCP, as well as enhancements to the existing Zoomdata Smart Connector for Google BigQuery. With these new capabilities Zoomdata offers deeply integrated and optimized support for Google Cloud Platform’s Cloud Spanner, PostgreSQL, Google BigQuery, and Cloud DataProc services.

We’re thrilled to have so many new products and partners that can help all of our customers grow. And as our final announcement for Google Cloud Next ’17 — please save the date for Next 2018: June 4–6 in San Francisco.

I guess that makes it 101. 🙂



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Magenta returns to Moogfest

By | machinelearning, ML, TensorFlow

Magenta was first announced to the public
nearly one year ago at Moogfest, a yearly music
festival in Durham, NC that brings together together artists, futurist thinkers,
inventors, entrepreneurs, designers, engineers, scientists, and musicians to
explore emerging sound technologies.

This year we will be returning
to continue the conversation, share what we’ve built in the last year, and help
you make music with Magenta.

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Data Science Weekly – Issue 173

By | machinelearning, TensorFlow

Data Science Weekly – Issue 173

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Curated news, articles and jobs related to Data Science. 
Keep up with all the latest developments

Issue #173

March 16 2017

Editor Picks

 

  • Why Pi Matters
    So it’s fair to ask: Why do mathematicians care so much about pi? Is it some kind of weird circle fixation? Hardly. The beauty of pi, in part, is that it puts infinity within reach. Even young children get this. The digits of pi never end and never show a pattern. They go on forever, seemingly at random—except that they can’t possibly be random, because they embody the order inherent in a perfect circle. This tension between order and randomness is one of the most tantalizing aspects of pi…
  • Voice and the uncanny valley of AI
    Voice is a Big Deal in tech this year. Amazon has probably sold 10m Echos, you couldn't move for Alexa partnerships at CES, Google has made its own and, it seems, this is the new platform. There are a couple of different causes for this explosion, and, also, a couple of problems. To begin, the causes…

 


 

A Message from this week's Sponsor:

 

 


 

Data Science Articles & Videos

 

  • DeepMind’s New Blockchain-Style System Will Track Health-Care Records
    Alphabet’s artificial intelligence outfit, DeepMind, plans to build a blockchain-style system that will carefully track how every shred of patient data is used. The company, which is rapidly expanding its health-care initiatives, has announced that it will build a tool that it calls Verifiable Data Audit during the course of this year. The idea: allow hospitals, and potentially even patients, to see exactly who is using health-care records, and for what purpose…
  • Possession Sketches: Mapping NBA Strategies
    We present Possession Sketches, a new machine learning method for organizing and exploring a database of basketball player-tracks. Our method organizes basketball possessions by offensive structure. We ϐirst develop a model for populating a dictionary of short, repeated, and spatially registered actions. Each action corresponds to an interpretable type of player movement. We examine statistical patterns in these actions, and show how they can be used to describe individual player behavior…
  • SciPy’s new LowLevelCallable is a game-changer
    Higher-order functions, ie functions that take other functions as input, enable powerful, concise, elegant expressions of various algorithms. Unfortunately, these have been hampered in Python for large-scale data processing because of Python’s function call overhead. SciPy’s latest update goes a long way towards redressing this…
  • Neural Network Architectures
    Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network architecture. I wanted to revisit the history of neural network design in the last few years and in the context of Deep Learning….
  • ICLR 2017 vs arxiv-sanity
    I thought it would be fun to cross-reference the ICLR 2017 (a popular Deep Learning conference) decisions (which fall into 4 categories: oral, poster, workshop, reject) with the number of times each paper was added to someone’s library on arxiv-sanity…
  • Complexity and Strategy
    I struggled with how to think about complexity through much of my career, especially during the ten years I spent leading Office development. Modeling complexity impacted how we planned major releases, our technical strategy as we moved to new platforms, how we thought about the impact of new technologies, how we competed with Google Apps, how we thought about open source and throughout “frank and open” discussions with Bill Gates on our long term technical strategy for building the Office applications…
  • Bayesian Ranking for Rated Items
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  • 2017 Data Visualization Survey Results
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Jobs

 

 


 

Training & Resources

 

  • Self-Organising Maps: An Introduction
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Books

 

  • Data Smart: Using Data Science to Transform Information into Insight

    "The best single book on Data Science today. I handle the data analysis and BI for the delivery side of a huge internet-based retail company, and have been a fan of Foreman's since his "Analytics Made Skeezy" blog days. His explanations are clear, his examples are to the point, and throughout it all, he is results-oriented."...

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.

 


 
P.S. Interested in reaching fellow readers of this newsletter? Consider sponsoring! Email us for details 🙂 – All the best, Hannah & Sebastian

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100 announcements (!) from Google Cloud Next '17

By | machinelearning, TensorFlow

San Francisco — What a week! Google Cloud Next ‘17 has come to the end, but really, it’s just the beginning. We welcomed 10,000+ attendees including customers, partners, developers, IT leaders, engineers, press, analysts, cloud enthusiasts (and skeptics). Together we engaged in 3 days of keynotes, 200+ sessions, and 4 invitation-only summits. Hard to believe this was our first show as all of Google Cloud with GCP, G Suite, Chrome, Maps and Education. Thank you to all who were here with us in San Francisco this week, and we hope to see you next year.

If you’re a fan of video highlights, we’ve got you covered. Check out our Day 1 keynote (in less than 4 minutes) and Day 2 keynote (in under 5!).

One of the common refrains from customers and partners throughout the conference was “Wow, you’ve been busy. I can’t believe how many announcements you’ve had at Next!” So we decided to count all the announcements from across Google Cloud and in fact we had 100 (!) announcements this week.

For the list lovers amongst you, we’ve compiled a handy-dandy run-down of our announcements from the past few days:

Google Cloud is excited to welcome two new acquisitions to the Google Cloud family this week, Kaggle and AppBridge.

1Kaggle – Kaggle is one of the world’s largest communities of data scientists and machine learning enthusiasts. Kaggle and Google Cloud will continue to support machine learning training and deployment services in addition to offering the community the ability to store and query large datasets.

2AppBridge – Google Cloud acquired Vancouver-based AppBridge this week, which helps you migrate data from on-prem file servers into G Suite and Google Drive.

100-announcements-4

Google Cloud brings a suite of new security features to Google Cloud Platform and G Suite designed to help safeguard your company’s assets and prevent disruption to your business: 

3Identity-Aware Proxy (IAP) for Google Cloud Platform (Beta) – Identity-Aware Proxy lets you provide access to applications based on risk, rather than using a VPN. It provides secure application access from anywhere, restricts access by user, identity and group, deploys with integrated phishing resistant Security Key and is easier to setup than end-user VPN.

4Data Loss Prevention (DLP) for Google Cloud Platform (Beta) – Data Loss Prevention API lets you scan data for 40+ sensitive data types, and is used as part of DLP in Gmail and Drive. You can find and redact sensitive data stored in GCP, invigorate old applications with new sensitive data sensing “smarts” and use predefined detectors as well as customize your own.

5Key Management Service (KMS) for Google Cloud Platform (GA) – Key Management Service allows you to generate, use, rotate, and destroy symmetric encryption keys for use in the cloud.

6Security Key Enforcement (SKE) for Google Cloud Platform (GA) – Security Key Enforcement allows you to require security keys be used as the 2-Step verification factor for enhanced anti-phishing security whenever a GCP application is accessed.

7Vault for Google Drive (GA) – Google Vault is the eDiscovery and archiving solution for G Suite. Vault enables admins to easily manage their G Suite data lifecycle and search, preview and export the G Suite data in their domain. Vault for Drive enables full support for Google Drive content, including Team Drive files.

8Google-designed security chip, Titan – Google uses Titan to establish hardware root of trust, allowing us to securely identify and authenticate legitimate access at the hardware level. Titan includes a hardware random number generator, performs cryptographic operations in the isolated memory, and has a dedicated secure processor (on-chip).

100-announcements-7

New GCP data analytics products and services help organizations solve business problems with data, rather than spending time and resources building, integrating and managing the underlying infrastructure:

9BigQuery Data Transfer Service (Private Beta) – BigQuery Data Transfer Service makes it easy for users to quickly get value from all their Google-managed advertising datasets. With just a few clicks, marketing analysts can schedule data imports from Google Adwords, DoubleClick Campaign Manager, DoubleClick for Publishers and YouTube Content and Channel Owner reports.

10Cloud Dataprep (Private Beta) – Cloud Dataprep is a new managed data service, built in collaboration with Trifacta, that makes it faster and easier for BigQuery end-users to visually explore and prepare data for analysis without the need for dedicated data engineer resources.

11New Commercial Datasets – Businesses often look for datasets (public or commercial) outside their organizational boundaries. Commercial datasets offered include financial market data from Xignite, residential real-estate valuations (historical and projected) from HouseCanary, predictions for when a house will go on sale from Remine, historical weather data from AccuWeather, and news archives from Dow Jones, all immediately ready for use in BigQuery (with more to come as new partners join the program).

12Python for Google Cloud Dataflow in GA – Cloud Dataflow is a fully managed data processing service supporting both batch and stream execution of pipelines. Until recently, these benefits have been available solely to Java developers. Now there’s a Python SDK for Cloud Dataflow in GA.

13Stackdriver Monitoring for Cloud Dataflow (Beta) – We’ve integrated Cloud Dataflow with Stackdriver Monitoring so that you can access and analyze Cloud Dataflow job metrics and create alerts for specific Dataflow job conditions.

14Google Cloud Datalab in GA – This interactive data science workflow tool makes it easy to do iterative model and data analysis in a Jupyter notebook-based environment using standard SQL, Python and shell commands.

15Cloud Dataproc updates – Our fully managed service for running Apache Spark, Flink and Hadoop pipelines has new support for restarting failed jobs (including automatic restart as needed) in beta, the ability to create single-node clusters for lightweight sandbox development, in beta, GPU support, and the cloud labels feature, for more flexibility managing your Dataproc resources, is now GA.

100-announcements-9

New GCP databases and database features round out a platform on which developers can build great applications across a spectrum of use cases:

16Cloud SQL for Postgre SQL (Beta) – Cloud SQL for PostgreSQL implements the same design principles currently reflected in Cloud SQL for MySQL, namely, the ability to securely store and connect to your relational data via open standards.

17Microsoft SQL Server Enterprise (GA) – Available on Google Compute Engine, plus support for Windows Server Failover Clustering (WSFC) and SQL Server AlwaysOn Availability (GA).

18Cloud SQL for MySQL improvements – Increased performance for demanding workloads via 32-core instances with up to 208GB of RAM, and central management of resources via Identity and Access Management (IAM) controls.

19Cloud Spanner – Launched a month ago, but still, it would be remiss not to mention it because, hello, it’s Cloud Spanner! The industry’s first horizontally scalable, globally consistent, relational database service.

20SSD persistent-disk performance improvements – SSD persistent disks now have increased throughput and IOPS performance, which are particularly beneficial for database and analytics workloads. Read these docs for complete details about persistent-disk performance.

21Federated query on Cloud Bigtable – We’ve extended BigQuery’s reach to query data inside Cloud Bigtable, the NoSQL database service for massive analytic or operational workloads that require low latency and high throughput (particularly common in Financial Services and IoT use cases).

100-announcements-11

New GCP Cloud Machine Learning services bolster our efforts to make machine learning accessible to organizations of all sizes and sophistication:

22.  Cloud Machine Learning Engine (GA) – Cloud ML Engine, now generally available, is for organizations that want to train and deploy their own models into production in the cloud.

23Cloud Video Intelligence API (Private Beta) – A first of its kind, Cloud Video Intelligence API lets developers easily search and discover video content by providing information about entities (nouns such as “dog,” “flower”, or “human” or verbs such as “run,” “swim,” or “fly”) inside video content.

24Cloud Vision API (GA) – Cloud Vision API reaches GA and offers new capabilities for enterprises and partners to classify a more diverse set of images. The API can now recognize millions of entities from Google’s Knowledge Graph and offers enhanced OCR capabilities that can extract text from scans of text-heavy documents such as legal contracts or research papers or books.

25Machine learning Advanced Solution Lab (ASL) – ASL provides dedicated facilities for our customers to directly collaborate with Google’s machine-learning experts to apply ML to their most pressing challenges.

26. Cloud Jobs API – A powerful aid to job search and discovery, Cloud Jobs API now has new features such as Commute Search, which will return relevant jobs based on desired commute time and preferred mode of transportation.

27Machine Learning Startup Competition – We announced a Machine Learning Startup Competition in collaboration with venture capital firms Data Collective and Emergence Capital, and with additional support from a16z, Greylock Partners, GV, Kleiner Perkins Caufield & Byers and Sequoia Capital.

100-announcements-10

New GCP pricing continues our intention to create customer-friendly pricing that’s as smart as our products; and support services that are geared towards meeting our customers where they are:

28Compute Engine price cuts – Continuing our history of pricing leadership, we’ve cut Google Compute Engine prices by up to 8%.

29Committed Use Discounts – With Committed Use Discounts, customers can receive a discount of up to 57% off our list price, in exchange for a one or three year purchase commitment paid monthly, with no upfront costs.

30Free trial extended to 12 months – We’ve extended our free trial from 60 days to 12 months, allowing you to use your $300 credit across all GCP services and APIs, at your own pace and schedule. Plus, we’re introduced new Always Free products — non-expiring usage limits that you can use to test and develop applications at no cost. Visit the Google Cloud Platform Free Tier page for details.

31Engineering Support – Our new Engineering Support offering is a role-based subscription model that allows us to match engineer to engineer, to meet you where your business is, no matter what stage of development you’re in. It has 3 tiers:

  • Development engineering support – ideal for developers or QA engineers that can manage with a response within four to eight business hours, priced at $100/user per month.
  • Production engineering support provides a one-hour response time for critical issues at $250/user per month.
  • On-call engineering support pages a Google engineer and delivers a 15-minute response time 24×7 for critical issues at $1,500/user per month.

32Cloud.google.com/community site – Google Cloud Platform Community is a new site to learn, connect and share with other people like you, who are interested in GCP. You can follow along with tutorials or submit one yourself, find meetups in your area, and learn about community resources for GCP support, open source projects and more.

100-announcements-8

New GCP developer platforms and tools reinforce our commitment to openness and choice and giving you what you need to move fast and focus on great code.

33Google AppEngine Flex (GA) – We announced a major expansion of our popular App Engine platform to new developer communities that emphasizes openness, developer choice, and application portability.

34Cloud Functions (Beta) – Google Cloud Functions has launched into public beta. It is a serverless environment for creating event-driven applications and microservices, letting you build and connect cloud services with code.

35Firebase integration with GCP (GA) – Firebase Storage is now Google Cloud Storage for Firebase and adds support for multiple buckets, support for linking to existing buckets, and integrates with Google Cloud Functions.

36Cloud Container Builder – Cloud Container Builder is a standalone tool that lets you build your Docker containers on GCP regardless of deployment environment. It’s a fast, reliable, and consistent way to package your software into containers as part of an automated workflow.

37. Community Tutorials (Beta)  – With community tutorials, anyone can now submit or request a technical how-to for Google Cloud Platform.

100-announcements-9

Secure, global and high-performance, we’ve built our cloud for the long haul. This week we announced a slew of new infrastructure updates. 

38. New data center region: California – This new GCP region delivers lower latency for customers on the West Coast of the U.S. and adjacent geographic areas. Like other Google Cloud regions, it will feature a minimum of three zones, benefit from Google’s global, private fibre network, and offer a complement of GCP services.

39. New data center region: Montreal – This new GCP region delivers lower latency for customers in Canada and adjacent geographic areas. Like other Google Cloud regions, it will feature a minimum of three zones, benefit from Google’s global, private fibre network, and offer a complement of GCP services.

40. New data center region: Netherlands – This new GCP region delivers lower latency for customers in Western Europe and adjacent geographic areas. Like other Google Cloud regions, it will feature a minimum of three zones, benefit from Google’s global, private fibre network, and offer a complement of GCP services.

41. Google Container Engine – Managed Nodes – Google Container Engine (GKE) has added Automated Monitoring and Repair of your GKE nodes, letting you focus on your applications while Google ensures your cluster is available and up-to-date.

42. 64 Core machines + more memory – We have doubled the number of vCPUs you can run in an instance from 32 to 64 and up to 416GB of memory per instance.

43. Internal Load balancing (GA) – Internal Load Balancing, now GA, lets you run and scale your services behind a private load balancing IP address which is accessible only to your internal instances, not the internet.

44. Cross-Project Networking (Beta) – Cross-Project Networking (XPN), now in beta, is a virtual network that provides a common network across several Google Cloud Platform projects, enabling simple multi-tenant deployments.

100-announcements-16

In the past year, we’ve launched 300+ features and updates for G Suite and this week we announced our next generation of collaboration and communication tools.

45. Team Drives (GA for G Suite Business, Education and Enterprise customers) – Team Drives help teams simply and securely manage permissions, ownership and file access for an organization within Google Drive.

46. Drive File Stream (EAP) – Drive File Stream is a way to quickly stream files directly from the cloud to your computer With Drive File Steam, company data can be accessed directly from your laptop, even if you don’t have much space on your hard drive.

47. Google Vault for Drive (GA for G Suite Business, Education and Enterprise customers) – Google Vault for Drive now gives admins the governance controls they need to manage and secure all of their files, including employee Drives and Team Drives. Google Vault for Drive also lets admins set retention policies that automatically keep what’s needed and delete what’s not.

48. Quick Access in Team Drives (GA) – powered by Google’s machine intelligence, Quick Access helps to surface the right information for employees at the right time within Google Drive. Quick Access now works with Team Drives on iOS and Android devices, and is coming soon to the web.

49. Hangouts Meet (GA to existing customers) – Hangouts Meet is a new video meeting experience built on the Hangouts that can run 30-person video conferences without accounts, plugins or downloads. For G Suite Enterprise customers, each call comes with a dedicated dial-in phone number so that team members on the road can join meetings without wifi or data issues.

50. Hangouts Chat (EAP) – Hangouts Chat is an intelligent communication app in Hangouts with dedicated, virtual rooms that connect cross-functional enterprise teams. Hangouts Chat integrates with G Suite apps like Drive and Docs, as well as photos, videos and other third-party enterprise apps.

51. @meet – @meet is an intelligent bot built on top of the Hangouts platform that uses natural language processing and machine learning to automatically schedule meetings for your team with Hangouts Meet and Google Calendar.

52. Gmail Add-ons for G Suite (Developer Preview) – Gmail Add-ons provide a way to surface the functionality of your app or service directly in Gmail. With Add-ons, developers only build their integration once, and it runs natively in Gmail on web, Android and iOS.

53. Edit Opportunities in Google Sheets – with Edit Opportunities in Google Sheets, sales reps can sync a Salesforce Opportunity List View to Sheets to bulk edit data and changes are synced automatically to Salesforce, no upload required.

54. Jamboard – Our whiteboard in the cloud goes GA in May! Jamboard merges the worlds of physical and digital creativity. It’s real time collaboration on a brilliant scale, whether your team is together in the conference room or spread all over the world.

100-announcements-17

Building on the momentum from a growing number of businesses using Chrome digital signage and kiosks, we added new management tools and APIs in addition to introducing support for Android Kiosk apps on supported Chrome devices. 

55. Android Kiosk Apps for Chrome – Android Kiosk for Chrome lets users manage and deploy Chrome digital signage and kiosks for both web and Android apps. And with Public Session Kiosks, IT admins can now add a number of Chrome packaged apps alongside hosted apps.

56. Chrome Kiosk Management Free trial – This free trial gives customers an easy way to test out Chrome for signage and kiosk deployments.

57. Chrome Device Management (CDM) APIs for Kiosks – These APIs offer programmatic access to various Kiosk policies. IT admins can schedule a device reboot through the new APIs and integrate that functionality directly in a third- party console.

58. Chrome Stability API – This new API allows Kiosk app developers to improve the reliability of the application and the system.

100-announcements-2

Attendees at Google Cloud Next ‘17 heard stories from many of our valued customers:

59. Colgate – Colgate-Palmolive partnered with Google Cloud and SAP to bring thousands of employees together through G Suite collaboration and productivity tools. The company deployed G Suite to 28,000 employees in less than six months.

60. Disney Consumer Products & Interactive (DCPI) – DCPI is on target to migrate out of its legacy infrastructure this year, and is leveraging machine learning to power next generation guest experiences.

61. eBay – eBay uses Google Cloud technologies including Google Container Engine, Machine Learning and AI for its ShopBot, a personal shopping bot on Facebook Messenger.

62. HSBC – HSBC is one of the world’s largest financial and banking institutions and making a large investment in transforming its global IT. The company is working closely with Google to deploy Cloud DataFlow, BigQuery and other data services to power critical proof of concept projects.

63. LUSH – LUSH migrated its global e-commerce site from AWS to GCP in less than six weeks, significantly improving the reliability and stability of its site. LUSH benefits from GCP’s ability to scale as transaction volume surges, which is critical for a retail business. In addition, Google’s commitment to renewable energy sources aligns with LUSH’s ethical principles.

64. Oden Technologies – Oden was part of Google Cloud’s startup program, and switched its entire platform to GCP from AWS. GCP offers Oden the ability to reliably scale while keeping costs low, perform under heavy loads and consistently delivers sophisticated features including machine learning and data analytics.

65. Planet – Planet migrated to GCP in February, looking to accelerate their workloads and leverage Google Cloud for several key advantages: price stability and predictability, custom instances, first-class Kubernetes support, and Machine Learning technology. Planet also announced the beta release of their Explorer platform.

66. Schlumberger – Schlumberger is making a critical investment in the cloud, turning to GCP to enable high-performance computing, remote visualization and development velocity. GCP is helping Schlumberger deliver innovative products and services to its customers by using HPC to scale data processing, workflow and advanced algorithms.

67. The Home Depot – The Home Depot collaborated with GCP’s Customer Reliability Engineering team to migrate HomeDepot.com to the cloud in time for Black Friday and Cyber Monday. Moving to GCP has allowed the company to better manage huge traffic spikes at peak shopping times throughout the year.

68. Verizon – Verizon is deploying G Suite to more than 150,000 of its employees, allowing for collaboration and flexibility in the workplace while maintaining security and compliance standards. Verizon and Google Cloud have been working together for more than a year to bring simple and secure productivity solutions to Verizon’s workforce.

100-announcements-3

We brought together Google Cloud partners from our growing ecosystem across G Suite, GCP, Maps, Devices and Education. Our partnering philosophy is driven by a set of principles that emphasize openness, innovation, fairness, transparency and shared success in the cloud market. Here are some of our partners who were out in force at the show:

69. Accenture – Accenture announced that it has designed a mobility solution for Rentokil, a global pest control company, built in collaboration with Google as part of the partnership announced at Horizon in September.

70. Alooma – Alooma announced the integration of the Alooma service with Google Cloud SQL and BigQuery.

71. Authorized Training Partner Program – To help companies scale their training offerings more quickly, and to enable Google to add other training partners to the ecosystem, we are introducing a new track within our partner program to support their unique offerings and needs.

72. Check Point – Check Point® Software Technologies announced Check Point vSEC for Google Cloud Platform, delivering advanced security integrated with GCP as well as their joining of the Google Cloud Technology Partner Program.

73. CloudEndure – We’re collaborating with CloudEndure to offer a no cost, self-service migration tool for Google Cloud Platform (GCP) customers.

74. Coursera – Coursera announced that it is collaborating with Google Cloud Platform to provide an extensive range of Google Cloud training course. To celebrate this announcement  Coursera is offering all NEXT attendees a 100% discount for the GCP fundamentals class.

75. DocuSign – DocuSign announced deeper integrations with Google Docs.

76. Egnyte – Egnyte announced an enhanced integration with Google Docs that will allow our joint customers to create, edit, and store Google Docs, Sheets and Slides files right from within the Egnyte Connect.

77. Google Cloud Global Partner Awards – We recognized 12 Google Cloud partners that demonstrated strong customer success and solution innovation over the past year: Accenture, Pivotal, LumApps, Slack, Looker, Palo Alto Networks, Virtru, SoftBank, DoIT, Snowdrop Solutions, CDW Corporation, and SYNNEX Corporation.

78. iCharts – iCharts announced additional support for several GCP databases, free pivot tables for current Google BigQuery users, and a new product dubbed “iCharts for SaaS.”

79. Intel – In addition to the progress with Skylake, Intel and Google Cloud launched several technology initiatives and market education efforts covering IoT, Kubernetes and TensorFlow, including optimizations, a developer program and tool kits.

80. Intuit – Intuit announced Gmail Add-Ons, which are designed to integrate custom workflows into Gmail based on the context of a given email.

81. Liftigniter – Liftigniter is a member of Google Cloud’s startup program and focused on machine learning personalization using predictive analytics to improve CTR on web and in-app.

82. Looker – Looker launched a suite of Looker Blocks, compatible with Google BigQuery Data Transfer Service, designed to give marketers the tools to enhance analysis of their critical data.

83. Low interest loans for partners – To help Premier Partners grow their teams, Google announced that capital investment are available to qualified partners in the form of low interest loans.

84. MicroStrategy – MicroStrategy announced an integration with Google Cloud SQL for PostgreSQL and Google Cloud SQL for MySQL.

85. New incentives to accelerate partner growth – We are increasing our investments in multiple existing and new incentive programs; including, low interest loans to help Premier Partners grow their teams, increasing co-funding to accelerate deals, and expanding our rebate programs.

86. Orbitera Test Drives for GCP Partners – Test Drives allow customers to try partners’ software and generate high quality leads that can be passed directly to the partners’ sales teams. Google is offering Premier Cloud Partners one year of free Test Drives on Orbitera.

87. Partner specializations – Partners demonstrating strong customer success and technical proficiency in certain solution areas will now qualify to apply for a specialization. We’re launching specializations in application development, data analytics, machine learning and infrastructure.

88. Pivotal – GCP announced Pivotal as our first CRE technology partner. CRE technology partners will work hand-in-hand with Google to thoroughly review their solutions and implement changes to address identified risks to reliability.

89. ProsperWorks – ProsperWorks announced Gmail Add-Ons, which are designed to integrate custom workflows into Gmail based on the context of a given email.

90. Qwiklabs – This recent acquisition will provide Authorized Training Partners the ability to offer hands-on labs and comprehensive courses developed by Google experts to our customers.

91. Rackspace – Rackspace announced a strategic relationship with Google Cloud to become its first managed services support partner for GCP, with plans to collaborate on a new managed services offering for GCP customers set to launch later this year.

92. Rocket.Chat – Rocket.Chat, a member of Google Cloud’s startup program, is adding a number of new product integrations with GCP including Autotranslate via Translate API, integration with Vision API to screen for inappropriate content, integration to NLP API to perform sentiment analysis on public channels, integration with GSuite for authentication and a full move of back-end storage to Google Cloud Storage.

93. Salesforce – Salesforce announced Gmail Add-Ons, which are designed to integrate custom workflows into Gmail based on the context of a given email.

94. SAP – This strategic partnership includes certification of SAP HANA on GCP, new G Suite integrations and future collaboration on building machine learning features into intelligent applications like conversational apps that guide users through complex workflows and transactions.

95. Smyte – Smyte participated in the Google Cloud startup program and protects millions of actions a day on websites and mobile applications. Smyte recently moved from self-hosted Kubernetes to Google Container Engine (GKE).

96. Veritas – Veritas expanded its partnership with Google Cloud to provide joint customers with 360 Data Management capabilities. The partnership will help reduce data storage costs, increase compliance and eDiscovery readiness and accelerate the customer’s journey to Google Cloud Platform.

97. VMware Airwatch – Airwatch provides enterprise mobility management solutions for Android and continues to drive the Google Device ecosystem to enterprise customers.

98. Windows Partner Program– We’re working with top systems integrators in the Windows community to help GCP customers take full advantage of Windows and .NET apps and services on our platform.

99. Xplenty – Xplenty announced the addition of two new services from Google Cloud into their available integrations: Google Cloud Spanner and Google Cloud SQL for PostgreSQL.

100. Zoomdata – Zoomdata announced support for Google’s Cloud Spanner and PostgreSQL on GCP, as well as enhancements to the existing Zoomdata Smart Connector for Google BigQuery. With these new capabilities Zoomdata offers deeply integrated and optimized support for Google Cloud Platform’s Cloud Spanner, PostgreSQL, Google BigQuery, and Cloud DataProc services.

We’re thrilled to have so many new products and partners that can help all of our customers grow. And as our final announcement for Google Cloud Next ’17 — please save the date for Next 2018: June 4–6 in San Francisco.

I guess that makes it 101. 🙂



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XLA – TensorFlow, compiled

By | machinelearning, TensorFlow

By the XLA team within Google, in collaboration with the TensorFlow team

One of the design goals and core strengths of TensorFlow is its flexibility. TensorFlow was designed to be a flexible and extensible system for defining arbitrary data flow graphs and executing them efficiently in a distributed manner using heterogenous computing devices (such as CPUs and GPUs).


But flexibility is often at odds with performance. While TensorFlow aims to let you define any kind of data flow graph, it’s challenging to make all graphs execute efficiently because TensorFlow optimizes each op separately. When an op with an efficient implementation exists or when each op is a relatively heavyweight operation, all is well; otherwise, the user can still compose this op out of lower-level ops, but this composition is not guaranteed to run in the most efficient way.


This is why we’ve developed XLA (Accelerated Linear Algebra), a compiler for TensorFlow. XLA uses JIT compilation techniques to analyze the TensorFlow graph created by the user at runtime, specialize it for the actual runtime dimensions and types, fuse multiple ops together and emit efficient native machine code for them – for devices like CPUs, GPUs and custom accelerators (e.g. Google’s TPU).

Fusing composable ops for increased performance

Consider the tf.nn.softmax op, for example. It computes the softmax activations of its parameter as follows:


Softmax can be implemented as a composition of primitive TensorFlow ops (exponent, reduction, elementwise division, etc.):

softmax = exp(logits) / reduce_sum(exp(logits), dim)


This could potentially be slow, due to the extra data movement and materialization of temporary results that aren’t needed outside the op. Moreover, on co-processors like GPUs such a decomposed implementation could result in multiple “kernel launches” that make it even slower.


XLA is the secret compiler sauce that helps TensorFlow optimize compositions of primitive ops automatically. Tensorflow, augmented with XLA, retains flexibility without sacrificing runtime performance, by analyzing the graph at runtime, fusing ops together and producing efficient machine code for the fused subgraphs.


For example, a decomposed implementation of softmax as shown above would be optimized by XLA to be as fast as the hand-optimized compound op.


More generally, XLA can take whole subgraphs of TensorFlow operations and fuse them into efficient loops that require a minimal number of kernel launches. For example:


Many of the operations in this graph can be fused into a single element-wise loop. Consider a single element of the bias vector being added to a single element from the matmul result, for example. The result of this addition is a single element that can be compared with 0 (for ReLU). The result of the comparison can be exponentiated and divided by the sum of exponents of all inputs, resulting in the output of softmax. We don’t really need to create the intermediate arrays for matmul, add, and ReLU in memory.

s[j] = softmax[j](ReLU(bias[j] + matmul_result[j]))


A fused implementation can compute the end result within a single element-wise loop, without allocating needless memory. In more advanced scenarios, these operations can even be fused into the matrix multiplication.

XLA helps TensorFlow retain its flexibility while eliminating performance concerns.


On internal benchmarks, XLA shows up to 50% speedups over TensorFlow without XLA on Nvidia GPUs. The biggest speedups come, as expected, in models with long sequences of elementwise operations that can be fused to efficient loops. However, XLA should still be considered experimental, and some benchmarks may experience slowdowns.

In this talk from TensorFlow Developer Summit, Chris Leary and Todd Wang describe how TensorFlow can make use of XLA, JIT, AOT, and other compilation techniques to minimize execution time and maximize computing resources.


Extreme specialization for executable size reduction


In addition to improved performance, TensorFlow models can benefit from XLA for restricted-memory environments (such as mobile devices) due to the executable size reduction it provides. tfcompile is a tool that leverages XLA for ahead-of-time compilation (AOT) – a whole graph is compiled to XLA, which then emits tight machine code that implements the ops in the graph. Coupled with a minimal runtime this scheme provides considerable size reductions.


For example, given a 3-deep, 60-wide stacked LSTM model on android-arm, the original TF model size is 2.6 MB (1 MB runtime + 1.6 MB graph); when compiled with XLA, the size goes down to 600 KB.


This size reduction is achieved by the full specialization of the model implied by its static compilation. When the model runs, the full power and flexibility of the TensorFlow runtime is not required – only the ops implementing the actual graph the user is interested in are compiled to native code. That said, the performance of the code emitted by the CPU backend of XLA is still far from optimal; this part of the project requires more work.


Support for alternative backends and devices


To execute TensorFlow graphs on a new kind of computing device today, one has to re-implement all the TensorFlow ops (kernels) for the new device. Depending on the device, this can be a very significant amount of work.


By design, XLA makes supporting new devices much easier by adding custom backends. Since TensorFlow can target XLA, one can add a new device backend to XLA and thus enable it to run TensorFlow graphs. XLA provides a significantly smaller implementation surface for new devices, since XLA operations are just the primitives (recall that XLA handles the decomposition of complex ops on its own). We’ve documented the process for adding a custom backend to XLA on this page. Google uses this mechanism to target TPUs from XLA.


Conclusion and looking forward


XLA is still in early stages of development. It is showing very promising results for some use cases, and it is clear that TensorFlow can benefit even more from this technology in the future. We decided to release XLA to TensorFlow Github early to solicit contributions from the community and to provide a convenient surface for optimizing TensorFlow for various computing devices, as well as retargeting the TensorFlow runtime and models to run on new kinds of hardware.



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Google Cloud supports $3M in grant credits for the NSF BIGDATA program

By | machinelearning, TensorFlow

Google Cloud Platform (GCP) serves more than one billion end-users, and we continue to seek ways to give researchers access to these powerful tools. Through the National Science Foundation’s BIGDATA grants program, we’re offering researchers $3M in Google Cloud Platform credits to use the same infrastructure, analytics and machine learning that we use to drive innovation at Google.

About the BIGDATA grants

The National Science Foundation (NSF) recently announced its flagship research program on big data, Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering (BIGDATA). The BIGDATA program encourages experimentation with datasets at scale. Google will provide cloud credits to qualifying NSF-funded projects, giving researchers access to the breadth of services on GCP, from scalable data management (Google Cloud Storage, Google Cloud Bigtable, Google Cloud Datastore), to analysis (Google BigQuery, Google Cloud Dataflow, Google Cloud Dataproc, Google Cloud Datalab, Google Genomics) to machine learning (Google Cloud Machine Learning, TensorFlow).

This collaboration combines NSF’s experience in managing diverse research portfolios with Google’s proven track record in secure and intelligent cloud computing and data science. NSF is accepting proposals from March 15, 2017 through March 22, 2017.  All proposals that meet NSF requirements will be reviewed through NSF’s merit review process.

GCP in action at Stanford University

To get an idea of the potential impact of GCP, consider Stanford University’s Center of Genomics and Personalized Medicine, where scientists work with data at a massive scale. Director Mike Snyder and his lab have been involved in a number of large efforts, from ENCODE to the Million Veteran Program. Snyder and his colleagues turned to Google Genomics, which gives scientists access to GCP to help secure, store, process, explore and share biological datasets. With the costs of cloud computing dropping significantly and demand for ever-larger genomics studies growing, Snyder thinks fewer labs will continue relying on local infrastructure.

“We’re entering an era where people are working with thousands or tens of thousands or even million genome projects, and you’re never going to do that on a local cluster very easily,” he says. “Cloud computing is where the field is going.”

“What you can do with Google Genomics — and you can’t do in-house — is run 1,000 genomes in parallel,” says Somalee Datta, bioinformatics director of Stanford University’s Center of Genomics. “From our point of view, it’s almost infinite resources.”



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When computers learn to swear: Using machine learning for better online conversations

By | machinelearning, TensorFlow

Imagine trying to have a conversation with your friends about the news you read this morning, but every time you said something, someone shouted in your face, called you a nasty name or accused you of some awful crime. You’d probably leave the conversation. Unfortunately, this happens all too frequently online as people try to discuss ideas on their favorite news sites but instead get bombarded with toxic comments.  

Seventy-two percent of American internet users have witnessed harassment online and nearly half have personally experienced it. Almost a third self-censor what they post online for fear of retribution. According to the same report, online harassment has affected the lives of roughly 140 million people in the U.S., and many more elsewhere.

This problem doesn’t just impact online readers. News organizations want to encourage engagement and discussion around their content, but find that sorting through millions of comments to find those that are trolling or abusive takes a lot of money, labor, and time. As a result, many sites have shut down comments altogether. But they tell us that isn’t the solution they want. We think technology can help.

Today, Google and Jigsaw are launching Perspective, an early-stage technology that uses machine learning to help identify toxic comments. Through an API, publishers—including members of the Digital News Initiative—and platforms can access this technology and use it for their sites.

How it works

Perspective reviews comments and scores them based on how similar they are to comments people said were “toxic” or likely to make someone leave a conversation. To learn how to spot potentially toxic language, Perspective examined hundreds of thousands of comments that had been labeled by human reviewers. Each time Perspective finds new examples of potentially toxic comments, or is provided with corrections from users, it can get better at scoring future comments.

Publishers can choose what they want to do with the information they get from Perspective. For example, a publisher could flag comments for its own moderators to review and decide whether to include them in a conversation. Or a publisher could provide tools to help their community understand the impact of what they are writing—by, for example, letting the commenter see the potential toxicity of their comment as they write it. Publishers could even just allow readers to sort comments by toxicity themselves, making it easier to find great discussions hidden under toxic ones.

We’ve been testing a version of this technology with The New York Times, where an entire team sifts through and moderates each comment before it’s posted—reviewing an average of 11,000 comments every day. That’s a lot of comments. As a result the Times has comments on only about 10 percent of its articles. We’ve worked together to train models that allows Times moderators to sort through comments more quickly, and we’ll work with them to enable comments on more articles every day.

Where we go from here

Perspective joins the TensorFlow library and the Cloud Machine Learning Platform as one of many new machine learning resources Google has made available to developers. This technology is still developing. But that’s what’s so great about machine learning—even though the models are complex, they’ll improve over time. When Perspective is in the hands of publishers, it will be exposed to more comments and develop a better understanding of what makes certain comments toxic.

While we improve the technology, we’re also working to expand it. Our first model is designed to spot toxic language, but over the next year we’re keen to partner and deliver new models that work in languages other than English as well as models that can identify other perspectives, such as when comments are unsubstantial or off-topic.

In the long run, Perspective is about more than just improving comments. We hope we can help improve conversations online.



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Data Science Weekly – Issue 170

By | machinelearning, TensorFlow

Data Science Weekly – Issue 170

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Curated news, articles and jobs related to Data Science. 
Keep up with all the latest developments

Issue #170

Feb 23 2017

Editor Picks

 

 


 

A Message from this week's Sponsor:

 

 

  • Harness the business power of big data.

    How far could you go with the right experience and education? Find out. At Capitol Technology University. Earn your PhD Management & Decision Sciences — in as little as three years — in convenient online classes. Banking, healthcare, energy and business all rely on insightful analysis. And business analytics spending will grow to $89.6 billion in 2018. This is a tremendous opportunity — and Capitol’s PhD program will prepare you for it. Learn more now.

 


 

Data Science Articles & Videos

 

  • Tracking my movements on the football pitch with Fitbit
    I mostly run and play football, and when it comes to track your movements while jogging, my brand new Fitbit Surge does the job almost perfectly. I decided to test its effectiveness on the football field, so I wore it during a game in Paris. Fitbit allows you to export your data in a .TCX format. I did it, and then imported it in Google Earth to check whether the GPS was accurate or not…
  • Feel The Kern – Generating Proportional Fonts with AI
    A about a year ago I read two blog posts about generating fonts with deep learning; one by Erik Bernhardsson and TJ Torres at StitchFix… So why not just take what Erik and TJ have made and simply use that to generate new fonts? Because their models are lacking something: Even though they manage to capture the styles of individual characters very well, they do not incorporate the styling found between pairs of characters, namely the intended spacing in between them, known as kerning…
  • Learning from A.I. Duet
    Google Creative Lab just released A.I. Duet, an interactive experiment which lets you play a music duet with the computer. You no longer need code or special equipment to play along with a Magenta music generation model. Just point your browser at A.I. Duet and use your laptop keyboard or a MIDI keyboard to make some music…
  • Twitter researchers offer clues for why Trump won
    Two University of Rochester researchers are out with a new study about why the 2016 Presidential election turned out the way it did. Professor Jiebo Luo and PhD candidate Yu Wang conducted an extensive 14-month study of each candidate’s Twitter followers and arrived at some very interesting results…
  • Learning to generate one-sentence biographies from Wikidata
    We investigate the generation of one sentence Wikipedia biographies from facts derived from Wikidata slot-value pairs. We train a recurrent neural network sequence-to-sequence model with attention to select facts and generate textual summaries. These automated 1-sentence "biographies" from Wikidata, are preferred by readers over Wikipedia's 1st sentence in 40% of cases…
  • Deep Nets Don't Learn Via Memorization
    We use empirical methods to argue that deep neural networks (DNNs) do not achieve their performance by memorizing training data, in spite of overlyexpressive model architectures. Instead, they learn a simple available hypothesis that fits the finite data samples…
  • Playing SNES in the Retro Learning Environment
    Mastering a video game requires skill, tactics and strategy. While these attributes may be acquired naturally by human players, teaching them to a computer program is a far more challenging task. As a result, the Arcade Learning Environment (ALE) has become a commonly used benchmark environment allowing algorithms to trainon various Atari 2600 games. In this paper we introduce a new learning environment, the Retro Learning Environment — RLE, that can run games on the Super Nintendo Entertainment System (SNES), Sega Genesis and several other gaming consoles…

 


 

Jobs

 

  • Data Scientist – SeatGeek – NYC

    SeatGeek operates a unique business model in a complicated, opaque market. Many of the hardest problems we face have never been tackled at scale and do not have clear questions, let alone answers. Moving forward requires critical thinking, rapid prototyping, and intellectual dexterity…

 


 

Training & Resources

 

  • Text mining in R: a tutorial
    At the end of this tutorial, you’ll have developed the skills to read in large files with text and derive meaningful insights you can share from that analysis. You’ll have learned how to do text mining in R, an essential data mining tool…

  • Experiment with Dask and TensorFlow
    This post briefly describes potential interactions between Dask and TensorFlow and then goes through a concrete example using them together for distributed training with a moderately complex architecture…

 


 

Books

 

 


 
P.S. Interested in reaching fellow readers of this newsletter? Consider sponsoring! Email us for details 🙂 – All the best, Hannah & Sebastian

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When computers learn to swear: Using machine learning for better online conversations

By | machinelearning, TensorFlow

Imagine trying to have a conversation with your friends about the news you read this morning, but every time you said something, someone shouted in your face, called you a nasty name or accused you of some awful crime. You’d probably leave the conversation. Unfortunately, this happens all too frequently online as people try to discuss ideas on their favorite news sites but instead get bombarded with toxic comments.  

Seventy-two percent of American internet users have witnessed harassment online and nearly half have personally experienced it. Almost a third self-censor what they post online for fear of retribution. According to the same report, online harassment has affected the lives of roughly 140 million people in the U.S., and many more elsewhere.

This problem doesn’t just impact online readers. News organizations want to encourage engagement and discussion around their content, but find that sorting through millions of comments to find those that are trolling or abusive takes a lot of money, labor, and time. As a result, many sites have shut down comments altogether. But they tell us that isn’t the solution they want. We think technology can help.

Today, Google and Jigsaw are launching Perspective, an early-stage technology that uses machine learning to help identify toxic comments. Through an API, publishers—including members of the Digital News Initiative—and platforms can access this technology and use it for their sites.

How it works

Perspective reviews comments and scores them based on how similar they are to comments people said were “toxic” or likely to make someone leave a conversation. To learn how to spot potentially toxic language, Perspective examined hundreds of thousands of comments that had been labeled by human reviewers. Each time Perspective finds new examples of potentially toxic comments, or is provided with corrections from users, it can get better at scoring future comments.

Publishers can choose what they want to do with the information they get from Perspective. For example, a publisher could flag comments for its own moderators to review and decide whether to include them in a conversation. Or a publisher could provide tools to help their community understand the impact of what they are writing—by, for example, letting the commenter see the potential toxicity of their comment as they write it. Publishers could even just allow readers to sort comments by toxicity themselves, making it easier to find great discussions hidden under toxic ones.

We’ve been testing a version of this technology with The New York Times, where an entire team sifts through and moderates each comment before it’s posted—reviewing an average of 11,000 comments every day. That’s a lot of comments. As a result the Times has comments on only about 10 percent of its articles. We’ve worked together to train models that allows Times moderators to sort through comments more quickly, and we’ll work with them to enable comments on more articles every day.

Where we go from here

Perspective joins the TensorFlow library and the Cloud Machine Learning Platform as one of many new machine learning resources Google has made available to developers. This technology is still developing. But that’s what’s so great about machine learning—even though the models are complex, they’ll improve over time. When Perspective is in the hands of publishers, it will be exposed to more comments and develop a better understanding of what makes certain comments toxic.

While we improve the technology, we’re also working to expand it. Our first model is designed to spot toxic language, but over the next year we’re keen to partner and deliver new models that work in languages other than English as well as models that can identify other perspectives, such as when comments are unsubstantial or off-topic.

In the long run, Perspective is about more than just improving comments. We hope we can help improve conversations online.



Source link

Google Cloud supports $3M in grant credits for the NSF BIGDATA program

By | machinelearning, TensorFlow

Google Cloud Platform (GCP) serves more than one billion end-users, and we continue to seek ways to give researchers access to these powerful tools. Through the National Science Foundation’s BIGDATA grants program, we’re offering researchers $3M in Google Cloud Platform credits to use the same infrastructure, analytics and machine learning that we use to drive innovation at Google.

About the BIGDATA grants

The National Science Foundation (NSF) recently announced its flagship research program on big data, Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering (BIGDATA). The BIGDATA program encourages experimentation with datasets at scale. Google will provide cloud credits to qualifying NSF-funded projects, giving researchers access to the breadth of services on GCP, from scalable data management (Google Cloud Storage, Google Cloud Bigtable, Google Cloud Datastore), to analysis (Google BigQuery, Google Cloud Dataflow, Google Cloud Dataproc, Google Cloud Datalab, Google Genomics) to machine learning (Google Cloud Machine Learning, TensorFlow).

This collaboration combines NSF’s experience in managing diverse research portfolios with Google’s proven track record in secure and intelligent cloud computing and data science. NSF is accepting proposals from March 15, 2017 through March 22, 2017.  All proposals that meet NSF requirements will be reviewed through NSF’s merit review process.

GCP in action at Stanford University

To get an idea of the potential impact of GCP, consider Stanford University’s Center of Genomics and Personalized Medicine, where scientists work with data at a massive scale. Director Mike Snyder and his lab have been involved in a number of large efforts, from ENCODE to the Million Veteran Program. Snyder and his colleagues turned to Google Genomics, which gives scientists access to GCP to help secure, store, process, explore and share biological datasets. With the costs of cloud computing dropping significantly and demand for ever-larger genomics studies growing, Snyder thinks fewer labs will continue relying on local infrastructure.

“We’re entering an era where people are working with thousands or tens of thousands or even million genome projects, and you’re never going to do that on a local cluster very easily,” he says. “Cloud computing is where the field is going.”

“What you can do with Google Genomics — and you can’t do in-house — is run 1,000 genomes in parallel,” says Somalee Datta, bioinformatics director of Stanford University’s Center of Genomics. “From our point of view, it’s almost infinite resources.”



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