Despite the investments and commitment from leadership, many organizations have yet to realize the full potential of artificial intelligence (AI) and machine learning (ML).
Data science and analytics teams are often squeezed between increasing business expectations and sandbox environments evolving into complex solutions. This makes it challenging to consistently transform data into solid answers for stakeholders .
How can teams tame complexity and live up to the expectations placed on them? MLOps provides some answers. There is no “one size fits all” approach when it comes to implementing an MLOps solution on Amazon Web Services (AWS). Like any other technical solution, MLOps should be implemented to meet the project requirements.
The first part of this blog discusses the key components of an MLOps solution architecture, regardless of the project requirements or business goals. The second part explores a conceptual example of an MLOps architecture to demonstrate how these key components can be glued together to implement an MLOps solution.