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AWS Machine Learning for enterprises

Today I attended this webinar to introduce the main components that AWS offers for working on Machine Learning.

I like this picture that explains in plain english what is Deep Learning, machine learning and Artificial Inteligence.

The main component of the overall solution is SageMaker, which is an integrated deep learning development and deployment platform, launched in November 2017.

You can build, train, and deploy machine learning models at any scale.

The basic steps are:

  1. Collect and prepare your training data to discover which elements of your data set are important.
  2. Select which algorithm and framework you’ll use. This is basically define the approach you want to use.
  3. Teach the model how to make predictions by training. This step typically requires a lot of compute resources.
  4. Tune the model so it delivers the best possible predictions. This step is often a tedious and manual effort. Here it’s suppposed you have a fully trained model.
  5. Integrate the model with your application and deploy this application on infrastructure that will scale. Here your model can be linked to other models making the things more complex.
  6. Once done, experiment and optimize every part of the process. This takes a lot of specialized expertise, access to large amounts of compute and storage, and a lot of time.

One of the competitive advantages that Amazon SageMaker offers is that it includes modules that can be used together or independently to build, train, and deploy your machine learning models.

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