Machine learning is one of the hottest topics in the IT industry these days. As algorithms and tools are becoming more advanced, more companies choose to adopt Machine Learning for their business needs.
AWS introduced Amazon SageMaker in 2017 to enable data scientists and developers to quickly and easily build, train, and deploy machine learning models at scale. Since then, AWS added more and more features to the product. Sometimes it might be tricky for a newcomer to understand the core components of Amazon SageMaker and how to get started with this AWS service.
In this hands-on workshop, we guide you on using Amazon SageMaker core features. We begin with preparing the dataset, then move to building and training the ML models and finalize the workshop by deploying a model to an API endpoint.
The workshop is led by Laurynas Tumosa, DevOps Engineer from seca.