Many data scientists and developers will already run their training models on Amazon Web Services (AWS) commoditised cloud computing platform.
At AWS re:Invent in November 2017 the vendor launched SageMaker, a fully managed machine learning platform which intends to take away some of the heavy lifting previously involved with running models on AWS.
SageMaker is essentially a platform for authoring, training and deploying machine learning algorithms to business applications without provisioning infrastructure and managing and tuning training models.
Read more: What is AWS SageMaker and can it really democratise machine learning in the enterprise?
Under the covers this means hosted Jupyter notebook integrated development environments (IDEs) for data exploration, cleaning, and preprocessing.
Then there is a distributed model building, training, and validation service where users can pick an AWS algorithm off the shelf, import a popular framework like TensorFlow or write and deploy their own algorithm with Docker containers, directly within SageMaker.
For training, you simply specify a location in S3 and the instance you want to use and in one click SageMaker spins up an isolated cluster and software defined network with autoscaling and data pipelines to start training. When you are done it tears down the cluster.
HTTPs endpoints are used for model hosting, which can scale to support traffic and allow you to A/B test multiple models simultaneously. The algorithms can be deployed straight into production using EC2 instances with one click, after which it will be deployed with autoscaling across availability zones.
Tuning models is traditionally a trial and error exercise but SageMaker comes with what AWS calls 'hyper parameter optimisation (HPO)'. By checking a box SageMaker will spin up multiple copies of the training model and uses machine learning to look at each change in parallel and tune parameters accordingly.