10. Retraining & Drift Management: Implement automated retraining workflows and strategies for handling concept/data drift.

5/5

7. Testing: (e.g., write unit tests for inference inputs/outputs; add statistical tests for data quality).
8. Deployment: Containerize the solution and deploy it to serve predictions.
9. Monitoring: Continuously log and monitor model performance in production.

4/5

5. Model Training: Train models and log models checkpoints
6. Model Evaluation & QA: Evaluate performance using confusion matrices (TP, FN, FP, TN) and robust validation techniques like k-fold cross-validation; iterate on hyper-parameter.

3/5

3. Data Cleaning & Wrangling: Transform data into the desired shape and ensure quality (handle missing values, outliers, etc.).
4. Feature Engineering: normalize/standardize data, perform feature selection (e.g., `sklearn` tools), and store in feature stores

2/5