Esquema Detallado del Curso
Challenge 1: Local Model Build
- Understand how the model build process works on a local machine/notebook for training and evaluation
Challenge 2: Portable execution
- Refactor experimentation notebook to run as an experiment in ML service
- Add metrics/parameter logging
Challenge 3: Scripted ML Pipeline Creation
- Extend notebook from challenge 2 to use Azure ML API to set up ML pipeline that trains and registers the model
- Retrieve the registered model, deploy it as an inferencing service to ACI, and test the deployed service
Challenge 4: Automated ML Pipeline Creation
- DevOps pipeline integration incorporating MLOpsPython
- Train, evaluate, and register a model via Azure DevOps and Azure ML Service Pipelines
- Deploy and serve a model automatically after model is registered in model registry service
- Implement basic acceptance testing against API endpoint in the pipeline after model deployment
Challenge 5: Observability: ML Training
- Introduce a change in the model which breaks the model during training in the pipeline. Be able to understand why it broke utilizing centralized instrumented logging.
- Have a dashboard which reports on one of CPU/Memory/GPU utilization and fires an alert when below or above a set threshold. This is to ensure there is optimal use of the hardware for cost and performance.
Challenge 6: Observability: ML Inference / Serving
- Implement a custom metric in the model code which outputs the score, have a dashboard to see the results of this metric over time
- Have a dashboard which reports on one of CPU/Memory/GPU utilization and fires an alert when below or above a set threshold. This is to ensure there is optimal use of the hardware for cost and performance.
Challenge 7: Data Ops for ML: Data Ingestion & Pre-processing
- Understand the pros and cons of various Data Ingestion options available with Azure services.
- Implement a solution when new data drops (i.e. file to a blob store) it automatically triggers a data ingestion pipeline to run a Python notebook to process the data, dump the file to a different folder on the blob store and invokes model training pipeline.
Challenge 8: Data Ops for ML: CI/CD Pipelines as Code
- Store the source code of the data ingestion solution in SCM repository.
- Implement branching policy for the data pipeline and the notebooks.
- Implement CI/CD pipelines deploying the data ingestion pipeline and the notebooks to the target environment. Have the solution parametrized so it can be deployed to multiple environments.
- Have CI/CD pipelines stored in SCM repository
Challenge 9: Advanced: Canary deployment
- Setup a canary deployment pipeline of the model being served to production users
- Analyze results of score on dashboard for the canary versus production