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Google Announces Cloud Machine Learning API Updates

| by Srini Penchikala on Apr 10, 2017. Estimated reading time: 3 minutes |

Google recently announced the Cloud Machine Learning API updates at the Google Cloud Next Conference. This includes a set of APIs in the areas of vision, video intelligence, speech, natural language, translation and job search. They enable the customers to build the machine learning applications that can see, hear and understand unstructured data, helping with use cases like next-product recommendations, medical-image analysis, and fraud detection.

Fei-Fei Li, Chief Scientist at Google Cloud AI and Machine Learning, wrote about Cloud ML and other APIs which include the following:

  • Cloud Datalab
  • Cloud Video Intelligence
  • Cloud Vision
  • Cloud Jobs

The Cloud Machine Learning Engine, now available in GA, can be used by organizations to train and deploy their own models into production in the cloud. It's a managed service for building custom TensorFlow based machine-learning models. It's also integrated with Google Cloud Platform's data analytics pipeline that includes services for data processing (Cloud Dataflow), data science workflow (Cloud Datalab) and SQL analytics (Google BigQuery).

The Google team is also working with technology partners to deploy their solutions to Cloud Machine Learning Engine. These partners include SpringML using the cloud platform to provide real-time analytics for its end-users, and SparkCognition using it to identify and block zero-day threats.

Cloud Datalab, also available in GA, is an interactive data science workflow tool used by developers and data scientists to explore, analyze and visualize data in BigQuery, Cloud Storage or local storage. It can be used for the steps involved in a typical machine learning development lifecycle: build a model prototype on a smaller dataset stored locally and train the model in the cloud using the complete dataset. The new release also includes support for TensorFlow and Scikit-learn, as well as batch and stream processing using Cloud Dataflow or Apache Spark via Cloud Dataproc.

Cloud Video Intelligence API uses Deep Learning models (built using TensorFlow) and is applied on media platforms like YouTube. The API enables developers to search and discover video content by providing information about entities inside the video content. The search criteria includes nouns such as "dog," "flower" or "human", or verbs such as "run," "swim" or "fly". It can also provide contextual understanding of when those entities appear.

This API is currently in Private Beta release. This API can be used by media organizations and consumer technology companies to find insights from unstructured data like video. The use cases include building media catalogs or finding ways to manage crowd-sourced content.

The new announcement also includes Cloud Vision API version 1.1, which helps to understand the content of an image by using machine learning models (via a REST API). It classifies images into different categories, detects individual objects and faces within images, and finds and reads printed words contained within images (OCR). Use cases for this include building metadata on image catalogs, moderating offensive content and developing marketing scenarios using image sentiment analysis. Realtor.com website uses Cloud Vision API to let the customers use smartphones to snap home photos and get information on that property right away.

Cloud Jobs API uses machine learning to help career sites with job search use cases. One of the new features is the Commute Search, which will return relevant jobs based on desired commute time and preferred mode of transportation. The API uses machine learning to understand the different attributes of job search such as job titles, job descriptions, skills and preferences and matches job seeker preferences with job listings based on classifications and relational models.

In related news, Google added Kaggle to the Google Cloud platform. Kaggle is the world's largest community of data scientists and machine learning enthusiasts and is used to explore, analyze and understand the latest updates in machine learning and data analytics.

 

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