FSV304: Industrialize Machine Learning Using CI/CD Techniques

As financial institutions look to accelerate and scale their use of machine learning, they need to address questions related to specific results, such as the version of the code and the data that lead to a particular inference. The use of disparate and increasingly non-traditional data sources for activities such as targeted marketing, fraud detection, and improved returns is driving a need for structured development of machine learning models. In this session, we'll discuss how we can use a combination of AWS services including Amazon SageMaker, AWS CodeCommit, AWS CodeBuild, and AWS CodePipeline to create a workflow that will help financial institutions meet their requirements and drive business results.