Machine learning is one branch of the field of artificial intelligence.
It's a way of solving problems without explicitly coding the solution.
Instead, human coders build systems that improve themselves over
time through repeated exposure to sample data which we call training data.
Major Google applications use machine learning like YouTube,
photos, the Google mobile App and Google translate.
The Google Machine Learning Platform is now available as a cloud service so
that you can add innovative capabilities to your own applications.
Cloud Machine Learning Platform provides modern machine learning services
with pre-trained models and a platform to generate your own tailored models.
As with other GCP products,
there's a range of services that stretches from the highly general to the pre-customized.
TensorFlow is an open source software library that's
exceptionally well suited for machine learning applications like neural networks.
It was developed by Google Brain for
Google's internal use and then open source so that the world could benefit.
You can run TensorFlow wherever you like but GCP is an ideal place for it because
machine learning models need lots of
on-demand compute resources and lots of training data.
TensorFlow can also take advantage of
Tensor processing units which are hardware devices designed to accelerate,
machine learning workloads with TensorFlow.
GCP makes them available in the cloud with compute engine virtual machines.
Each cloud TPU provides up to 180 teraflops of performance.
And because you pay for only what you use,
there's no upfront capital investment required.
Suppose you want a more managed service.
Google Cloud Machine Learning Engine lets you easily build
machine learning models that work on any type of data of any size.
It can take any TensorFlow model and perform large scale training on a managed cluster.
Finally, suppose you want to add various machine learning capabilities
to your applications without having to worry about the details of how they are provided.
Google Cloud also offers a range of machine learning APIs suited to specific purposes.
And I'll discuss them in a moment.
People use the Cloud Machine Learning Platform for lots of applications.
Generally, they fall into two categories depending
on whether the data they work on is structured or unstructured.
Based on structured data,
you can use ML for various kinds of
classification and regression tasks like customer churn analysis,
product diagnostics and forecasting.
It can be the heart of our recommendation engine for
content personalization and cross-sells and up-sells.
You can use ML to detect anomalies as in fraud detection,
sensor diagnostics or log metrics.
Based on unstructured data,
you can use ML for image analytics such as identifying damaged shipment,
identifying styles and flagging content.
You can do text analytics too.
Like a call center, blog analysis,
language identification, topic classification and sentiment analysis.
In many of the most innovative applications for machine learning,
several of these kinds of applications are combined.
What if whenever one of your customers
posted praise for one of your products on social media,
your application could automatically reach out to them
with a customized discount on another product they'll probably like.
The Google Cloud machine Learning Platform makes
that kind of interactivity well within your grasp.