Google Cloud Platform
Announcing Google Cloud Video Intelligence API, and more Cloud Machine Learning updates
Artificial intelligence is playing an increasingly essential role in the enterprise, however, more and more businesses find themselves struggling to keep up. One of our most important goals is to make machine learning a transformational tool for organizations of any size, industry or sophistication.
We’re seeing customers making it part of their wider data analytics strategy, with early adopters like Airbus, Disney and Ocado serving as inspirational use cases.Today at Google Cloud Next ‘17 we’re excited to announce new products, research and education programs to ensure machine learning is accessible to all businesses, data scientists and developers. We're also thrilled to welcome Kaggle to Google Cloud. Home to the world's largest community of data scientists and machine learning enthusiasts, Kaggle is used by more than 800,000 data experts to explore, analyze and understand the latest updates in machine learning and data analytics.
Understanding videos with Cloud Video Intelligence APICloud Video Intelligence API (now in Private Beta) uses powerful deep-learning models, built using frameworks like TensorFlow and applied on large-scale media platforms like YouTube. The API is the first of its kind, enabling developers to 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. It can even provide contextual understanding of when those entities appear; for example, searching for “Tiger” would find all precise shots containing tigers across a video collection in Google Cloud Storage.
Google has a long history working with the largest media companies in the world, and we help them find value from unstructured data like video. This API is for large media organizations and consumer technology companies, who want to build their media catalogs or find easy ways to manage crowd-sourced content, and for partners like Cantemo to build it into their own video management software.
With this announcement, Google Cloud Machine Learning adds to a growing set of Cloud Machine Learning APIs: Vision, Video Intelligence, Speech, Natural Language, Translation and Jobs. These APIs let customers build the next generation of applications that can see, hear and understand unstructured data — greatly expanding the use cases for machine learning for everything from next-product recommendations, to medical-image analysis, to fraud detection and beyond.
Cloud Machine Learning Engine in GACloud Machine Learning Engine, now in GA, is an attractive option for organizations that want to train and deploy their own models into production in the cloud. It has the advantages of a managed service for building custom TensorFlow-based machine-learning models that interact with any type of data, at any scale. It’s also integrated with Google Cloud Platform’s complete data analytics pipeline that includes Cloud Dataflow (for data processing), Cloud Datalab (for data science workflow) and Google BigQuery (for SQL analytics).
We're also working with technology partners to power their own solutions with Cloud Machine Learning Engine. Two recent examples are: SpringML, which uses Cloud Machine Learning Engine to provide real-time analytics for its end-users, and SparkCognition, which uses it to identify and block zero-day threats.
Learn from our ML expertsTo help customers get value from machine learning quickly, our Advanced Solution Lab (ASL) provides dedicated facilities where they can directly collaborate with Google’s machine-learning experts to apply ML to their most pressing challenges. Throughout this unique experience, customers can explore specific business use cases while gaining a solid foundation in machine learning with TensorFlow and Cloud ML Engine.
Great partnerships come from finding possibilities where they never existed before. Machine-learning pioneers Google Cloud and customer-experience pioneers USAA found a great partnership in the Advanced Solutions Lab.”
Cloud Vision API 1.1 (beta)Cloud Vision is one of our fastest growing APIs. Since we launched it in April 2016, the API has enabled developers to extract metadata from over 1 billion images. Today, we're introducing new capabilities for enterprises and partners to help them 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. Computer vision is evolving from a “cool feature” to a fundamental part of the modern enterprise. The Vision API ensures that all cloud customers have fast, reliable access to this technology.
Through the use of Google’s machine learning, we achieved a match rate of 24 percentage points higher than a similar feature that relies on location alone for its search results."
Shorten your commute time with Cloud Jobs APICloud Jobs API uses machine learning to power career sites with more relevant job search. Since we first announced the API, we’ve incorporated the feedback of testers like CareerBuilder, Dice and Jibe and are adding new features such as Commute Search, which will return relevant jobs based on desired commute time and preferred mode of transportation.
With a focus on preventing and defeating cancer, Johnson & Johnson currently has over 350 open roles in this specialized area and a small pool of candidates and uses Cloud Jobs API to help job seekers find relevant roles.
Machine learning with the Cloud Jobs API will enable us to improve job seekers’ experiences and close the gap between hunting for a job and finding the right position, at the right time.”
Explore data with Cloud Datalab GACloud Datalab, an interactive data science workflow tool, makes it easy for developers and data scientists to explore, analyze and visualize data in BigQuery, Cloud Storage or local storage. For Machine Learning development, they can take a full-lifecycle approach: building a model prototype on a smaller dataset stored locally, then training in the cloud using the full dataset. In this GA release, there's new support for TensorFlow and Scikit-learn, as well as batch and stream processing using Cloud Dataflow or Apache Spark via Cloud Dataproc.
We hope you'll take a step forward in using machine learning to drive your business. And we look forward to feedback about how we can help you be successful with machine learning— and are particularly interested in hearing about new use cases that we haven't dreamed of yet!