Google Cloud Platform

Google Cloud Machine Learning family grows with new API, editions and pricing

Google Cloud Machine Learning is one of our fastest growing products areas. Since we first announced our machine learning offerings earlier this year, we’ve released a steady stream of new APIs, tools and services to help you harness the power of machine learning. We’ve seen machine learning transform users’ experiences, accelerate business operations by solving problems that have existed for decades and delight us with novel applications.

The key to creating a culture of innovation is having the right team, technology and strategy in place. To further these efforts, we’re excited to announce the creation of a new Google Cloud Machine Learning group that will be focused exclusively on delivering cloud-based machine learning solutions to all businesses. Fei-Fei Li and Jia Li, two world-renowned researchers in the subject of machine intelligence, will lead this new group.

Building a centralized team within Google Cloud will accelerate our ability to deliver machine learning products and services to enterprise customers in every industry. Today also marks an exciting next step in Google Cloud’s product commitment to make machine learning more accessible for all businesses. We’re excited to introduce:

  • A brand new Cloud Machine Learning API to help people find careers
  • New hardware options to accelerate your machine learning workloads
  • Improved efficiencies and expanded features for our Cloud Translation, Cloud Vision and Cloud Natural Language APIs

Introducing Google Cloud Jobs API

Machine learning presents new opportunities to solve some pretty difficult business problems. Since so much of what every business achieves depends on great employees, how can we help there? What if we could use machine learning to change the nature of finding jobs and hiring people? We think we can.

Hiring is one of the hardest things organizations do. Part of the difficulty comes from a lack of industry standards to define and describe occupations and how they align to specific skills. Over the past year, Google has developed a new machine-learning model that has the potential to greatly improve the recruitment efforts of any company. We call this the Google Cloud Jobs API. It provides businesses with Google-strength capabilities to find, match and recommend relevant jobs to candidates.

In order to provide the most relevant recommendations to job seekers, Cloud Jobs API uses machine learning to understand how job titles and skills relate to one another and what job content, location, and seniority are the closest match to a jobseeker’s preferences. You can learn more about how it works here.

The API is intended for job boards, career sites and applicant tracking systems. Early adopters of Cloud Jobs API are Jibe, Dice and CareerBuilder.

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“Large enterprises have come to expect that integrating new solutions takes month or years, and these long implementation cycles are a major roadblock in delivering innovation. Jibe was able to seamlessly deploy the Google Jobs API as a turnkey machine learning solution for one of our customer's career sites in a matter of 3 weeks, and we expect that implementation time to shrink for future customers.” - Joe Essenfeld, Founder and CEO at Jibe

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Dice, a career website that serves opportunities for technology and engineering professionals, is a launch tester of the API to help job candidates browse over 80,000 tech job listings. Tech jobs tend to be complex and skill specific. For example, if a tech professional enters "front-end engineer" in a job search without using typical Boolean standards, search results will also return UI engineer, UI developer, web developer, and UX engineer. Complicated, right? By using the API, Dice will be able to better understand a candidate’s background and preferences and match the tech pro to the right roles.

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CareerBuilder, using a prototype that they created with Cloud Jobs API in just 48 hours, found improved, more accurate results when compared to its existing search algorithm. In one test, CareerBuilder chose a top 100 term, “part time,” and compared results using the Google Cloud Jobs API versus their existing solution. Jobs API returned a richer set of results by applying an expanded set of synonyms including “PT.” Another test showcased how Jobs API can refine search results. CareerBuilder has one of the largest repositories of healthcare industry jobs. CareerBuilder tested the terms “CNA psych” (Certified Nurses Assistant) against a dataset and reduced the results returned — delivering only CNA roles in a psychiatric setting — to notably increase accuracy for the job seeker. Based on these results, CareerBuilder is making plans to leverage the API for its customers in the near future.

Cloud Jobs API is now available in a limited alpha. To learn more, visit the Cloud Jobs API page.

Welcoming GPUs for Google Cloud Platform

Machine learning greatly benefits from fast and reliable hardware, and as hardware advances so do the capabilities of machine learning. This is exactly why Google continues to harness hardware innovations that can help to accelerate machine learning applications.

Beginning in 2017, Google Cloud will offer more hardware choices for businesses that want to use Google Cloud Platform (GCP) for their most complex workloads, including machine learning. For Google Compute Engine and Google Cloud Machine Learning, businesses will be able to use GPUs (Graphics Processing Units) that are highly-specialized processors capable of handling the complexities of machine learning applications. Making GPUs available in Google Cloud means that you can focus on solving challenging computational problems while accessing GPU machines from anywhere and only paying for what you need.

In other words, you'll be able to strap your ML-powered applications to a rocket engine, resulting in faster and more affordable machine learning models. To learn more, visit our GPU page.


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Making Cloud Vision API affordable for everyone

Google has been leveraging the latest hardware and tuned algorithms to significantly improve the performance of our Cloud Machine Learning services. Cloud Vision API now takes advantage of Google’s custom TPUs, our custom ASIC built for machine learning, to improve performance and efficiency. These improvements have enabled us to reduce prices for Cloud Vision API by ~80%. By offering the API at a more affordable price-point, more organizations than ever will be able to take advantage of Cloud Vision API to power new capabilities.

Along with the price reductions, we have made significant improvements to our image recognition capabilities over the last six months. For example: the logo detection feature can identify millions of logos and label detection can identify an expanded number of entities, such as landmarks and objects in images.

Since we first introduced Cloud Vision API, we’ve been very happy with the positive feedback and creativity customers have in using it to power their experiences. Since the beta release, businesses have analyzed well over a billion images. Image analysis, the core capability of Vision API, is fundamentally changing how businesses operate and interact with their end-users. We have thousands of customers using the product to do amazing things. For example, the e-discovery firm Platinum IDS uses Cloud Vision API to power content relevancy for millions of paper and digital files and deliver its new e-Discovery app, and Disney has leveraged Vision API as the basis of innovative marketing campaigns.

Now offering Cloud Translation API Premium

Most recently, Google announced the launch of our Google Neural Machine Translation system (GNMT) that uses state-of-the-art training techniques and runs on TPUs to achieve some of the largest improvements for machine translation in the past decade. Now, Google Cloud is offering these capabilities to all partners, developers and businesses with a Premium edition of Cloud Translation API (formerly Google Translate API).

This new edition provides:

  • Highest-quality model that reduces translation errors by more than 55%-85% on several major language pairs
  • Support for up to eight languages (English to Chinese, French, German, Japanese, Korean, Portuguese, Spanish, Turkish) and 16 language pairs. We'll support more languages in the near future.

The Premium edition is tailored for users who need precise, long-form translation services. Examples include livestream translations, high volume of emails and detailed articles and documents. The Standard edition continues to offer translation in over 100 languages and price-performance that's ideal for short, real-time conversational text.


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To make Cloud Translation API more affordable, we also decreased the price of the standard edition for higher usage volumes. Please visit our pricing page for more information.

Graduating Cloud Natural Language API to general availability

Cloud Natural Language API, our text analysis machine learning service, is now generally available for all businesses. Based on valuable feedback shared by beta testers such as Evernote, a productivity service used by over 200 million people to store billions of notes and attachments, we're releasing new features:

  • Expanded entity recognition to increase the accuracy at which the API identifies the names of things, such as people, companies, or locations in the text.
  • Granular sentiment analysis with expanded language support to provide sentiment analysis at the sentence level and not just within a document or record.
  • Improved syntax analysis with additional morphologies such as number, gender, person and tense, to improve coreference resolution required of advanced NLP tasks.

Click here to learn more about the technical details and to see the Cloud Natural Language API in action.

Our team is hard at work to enable new machine learning scenarios in the upcoming months. This year alone, we introduced brand new APIs and a fully-managed platform that are now available for all businesses to use. And as users explore our existing machine learning ecosystem, Google continues to invest in research and models that will bring new scenarios to life. We’re committed to quickly delivering new machine learning solutions for businesses in 2017 and beyond. Stay tuned for what’s next.