MLOps Code Challenges & Tips

1. Implement a script that automates the process of retraining a machine learning model given new input data and specifies an evaluation metric to check if the new model is better than the existing one.

2. Write a Python function that takes a list of model performance metrics (accuracy, precision, recall, F1-score) and returns a summary report indicating whether the model meets defined quality thresholds.

3. Create an end-to-end pipeline using a tool like Apache Airflow or Kubeflow that deploys a trained model and serves predictions through a REST API.

4. Write a unit test for a function that normalizes a dataset. The function should handle missing values and scale features to a range of 0 to 1.

5. Implement a logging mechanism for tracking model performance over time, including the ability to log predictions, actual outcomes, and performance metrics.

6. Create a command-line tool that automates the process of model versioning, ensuring that the model metadata (parameters, performance metrics) is stored alongside each version.

7. Given a dataset and a set of hyperparameters, write a function that performs hyperparameter tuning using Grid Search Cross-Validation and outputs the best hyperparameters.

8. Write a Python script that utilizes a containerization tool (like Docker) to package a machine learning model with its dependencies, making it ready for deployment.

9. Implement a feature engineering function that takes raw text data and returns a cleaned and tokenized output suitable for training a natural language processing (NLP) model.

10. Develop a script that monitors the drift in model performance by comparing current and historical metrics, alerting stakeholders if a specified threshold is exceeded.