Google Cloud Machine Learning provides modern machine learning services, with pre-trained models and a platform to generate your own tailored models. The neural net-based ML platform has better training performance and increased accuracy compared to other large scale deep learning systems.
Major Google applications use Cloud Machine Learning, including Photos (image search), the Google app (voice search), Translate, and Inbox (Smart Reply). The platform is now available as a cloud service to bring unmatched scale and speed to your business applications.
Cloud Machine Learning will take machine learning mainstream, giving data scientists and developers a way to build a new class of intelligent applications. It provides access to the same technologies that power Google Now, Google Photos and voice recognition in Google Search as easy to use REST APIs.
It enables you to build powerful Machine Learning models on your data using the open-source TensorFlow machine learning library:
Cloud Machine Learning makes it easy for you to build sophisticated, large scale machine learning models in a short amount of time. It’s portable, fully managed and scalable. Cloud Machine Learning works with data in many formats and is well integrated with other Cloud Platform products such as Google Cloud Dataflow, Google BigQuery, Google Cloud Dataproc, Google Cloud Storage, and Google Cloud Datalab. You can easily build predictive analytics models using your own training data. For example, a financial services app that predicts values using regression models, or a classification service for images. Cloud Machine Learning will take care of everything from data ingestion through to prediction. The result: now any application can take advantage of the same deep learning techniques that power many of Google’s services.
Pre-trained Machine Learning models like Google Translate API and Cloud Vision API are being joined today by Google Cloud Speech API. We’re very excited to now offer a full set of APIs that help your applications see, hear and translate. Cloud Speech API brings the same advanced neural network technology that powers voice search in the Google app and voice typing in Google Keyboard. It demonstrates speech-to-text conversion in 80+ languages with unparalleled accuracy, especially in noisy environments, and is blazingly fast. The technology that has already empowered developers to add speech to their Chrome and Android is now available for any application in real-time streaming or batch mode.
Google Cloud Speech API enables developers to convert audio to text by applying powerful neural network models in an easy to use API. The API recognizes over 80 languages and variants, to support your global user base. You can transcribe the text of users dictating to an application’s microphone, enable command-and-control through voice, or transcribe audio files, among many other use cases. Recognize audio uploaded in the request, and in upcoming releases, integrate with your audio storage on Google Cloud Storage.
Big Data and Analytics:
Doing big data the cloud way means being more productive when building applications, with faster and better insights, without having to worry about the underlying infrastructure. To further this mission, we recently announced the general availability of Cloud Dataproc, our managed Apache Hadoop and Apache Spark service, and we’re adding new services and capabilities today:
BigQuery continues to push the limits of what it means to be a fully managed Analytics Data Warehouse. Today we’re announcing lots of exciting new features that make analytics faster, cheaper and easier to use
The Cloud Machine Learning offering leverages Google’s cutting edge machine learning and data processing technologies, some of which we’ve recently open sourced:
TensorFlow, Google’s latest machine learning system, is the #1 Machine Learning project on GitHub today. They’re continuing to develop this ecosystem. For example, you can now use TensorFlow Serving with another of their open-source projects, Kubernetes, to scale and serve ML models. The Cloud Machine Learning product extends these capabilities, enabling you to build powerful Machine Learning models with your data on Cloud Platform.
Earlier this year, we partnered with a number of organizations, including data Artisans, Cloudera, Talend and others to submit the Dataflow model, SDKs and runners for popular OSS distributed systems to the Apache Incubator. This new incubating project, known as Apache Beam, allows you to define portable, powerful and simple data processing pipelines that can execute in either streaming or batch mode.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.