The MLOps Workload Manager solution is built on Amazon Sagemaker & AWS DevOps services which helps you streamline and enforce architecture best practices for the machine learning model. This solution is an extendable framework that provides a standard interface for creating & managing ML pipelines.

The solution’s template allows customers to:

• Pre-process, train & evaluate models
• Upload their trained models (bring your model)
• Model configuration, deployment, and monitoring
• Configure and orchestrate the pipeline
• Monitor the pipeline’s operations
• Trigger the pipeline through new data upload and code changes.

MLOps Workload Overview:

There are three ways to trigger this workflow

1. Data Trigger: Whenever new data gets uploaded, it automatically triggers MLOps workflow, and the model gets built and deployed based on the new data.
2. Code Changes Trigger: Whenever a data scientist changes the code for pre-processing, model training, or evaluation, It will trigger this MLOps workflow, and the model gets built and deployed based on the new changes.
3. Deployment Changes: Whenever the ML engineer changes the deployment configuration. It will trigger this MLOps deployment workflow, and the model will deploy again based on the new deployment configuration.

Model Approval:

Once the model has been trained and evaluated, it will be registered in the model registry; then, after data scientist has to visit the registry and manually approve the model by examining a couple of metrics.