Build DevOps skills required to automate the various aspects and stages of machine learning model building and monitoring.

Deploying machine learning models in production:

• PyLint and AutoPEP8
• Git and GitHub
• Testing with pytest and logging with logging

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Efficiency, effectiveness, and productivity in modern, real-world ML projects:

• Clean, organized, reproducible end-to-end ML pipeline with MLflow
• Track experiments, code, and results with GitHub and Weights & Biases
• Selecting and deploying the best performing model using MLflow

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Deploying a machine learning model in Production:

• Modeling performance, checking for bias using data cross-sections (called "slices"), and writing a model map
• Version control of data and models with Data Version Control (DVC)
• Continuous Integration with GitHub Actions and Continuous Delivery/Deployment
• Fast, type-checked and autodocumented writing of a user interface (API) with FastAPI

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Full automation of MLOps processes:

• Model training and deployment
• Establish regular assessment processes: Re-training and re-deployment of models at model drift.
• Diagnose operational issues with models, including data integrity and stability issues, timing issues, and dependency issues
• Setup of automated reports for APIs

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