The MLOps process
Machine Learning Operations (MLOps) in AWS refers to the practices and tools employed to manage and operationalize ML workflows and models on the AWS platform. MLOps aims to streamline and automate the deployment, monitoring, and management of ML models, ensuring their reliability, scalability, and reproducibility.
MLOps has a direct impact in the following ways:
- It boosts data scientists’ productivity by simplifying the ML process
- It helps maintain high model accuracy
- It helps enhance the security and compliance of the ML platform
ML is an iterative process and without MLOps, creating an end-to-end ML process would be a challenge. Every stage in the ML life cycle has its own set of activities, and specific tools in Amazon SageMaker assist at every stage.
The following figure highlights all the different stages the whole ML process goes through.
Figure 17.16 – ML life cycle
