Google machine learning gains Kaggle and more

Google machine learning gains Kaggle and more

Google is adding Kaggle and new APIs, as well as releasing new machine learning tools

Google has already carved out a niche for itself in machine learning with projects like TensorFlow and Google Brain. Now, it's adding data science provider Kaggle, which runs contests related to machine learning and provides services for data discovery and analysis, to the fold. The company also is moving ahead with other machine learning projects, including an API providing intelligence for video.

Google Cloud is gaining access to Kaggle's community of more than 850,000 data scientists and vice versa. Kaggle and Google Cloud will continue to support machine learning training and deployment, while the community gets the capability to store and query large data sets.

Google on Wednesday also introduced its Cloud Video Intelligence API, providing an ability to search video for specific content. Currently a private beta, the API uses deep learning models to provide information about content, enabling, for example, searches for content about baseball or dogs. The API was built using Google's TensorFlow framework and has been applied to YouTube. With this unveiling, Google Cloud Machine Learning adds to a set of APIs that also includes Vision, Speech, Natural Language Translation, and Jobs.

Google's Cloud Machine Learning Engine, meanwhile, has become generally available. It can be used for training machine learning models into production in the cloud. TensorFlow models can be built for interacting with data.

The company also unveiled the beta release of Cloud Vision Vision API 1.1, broadening an ability to classify images. The API can now recognize millions of entities from Google's Knowledge Graph and has enhanced optical character recognition capabilities to extract text from scans of text-heavy documents, including legal contracts or books.

Finally, Google's Cloud Datalab interactive data science workflow tool is now generally available. Developers and data scientists can analyze and visualize data in a BigQuery data warehouse, cloud storage, or local storage. The general release adds support for TensorFlow and Scikit-learn machine learning along with batch and stream processing using Google Cloud Dataflow, Apache Spark, or the Cloud Dataproc service.

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