Check here: https://ml-ops.org/content/references.html
- Contains specific checklist on performing automated testing on Data Validity, Features, Feature and Data Pipelines, and Feature Creation Code
- Contains checklist on ensuring that the results of your ML Project is reproducible
- Contains more actionable steps on the Components of MLOps
- Contains specific Setup Components on MLOps such as Source Control, Model Registry, etc.
- Additional Definition on Continuous X. Aside from CI/CD, there's Continuous Training (CT) and Continuous Monitoring
- Contains info on AI Canvas and ML Canvas
- Contains overview on Model Serving Strategies
- Contains overview on Model formats such as Amalgamation
- Ensemble Methods: Foundations and Algorithms (Chapman & Hall/CRC Machine Learning & Pattern Recognition) 1st Edition Link
- Feature Engineering for Machine Learning Link
- Principles of Data Wrangling Link
- Software Architecture Patterns Link
- Accelerate: Building and Scaling High Performing Technology Organizations Link
- DVC : Data Version Control
- MLFlow : For Project and Model Management
- Weights and Biases : For Tracking Experiments