6.5. Connecting the dots: full-stack ML observability.

6.4. ML monitoring with Evidently and Grafana. [OPTIONAL CODE PRACTICE]

6.3. ML model monitoring dashboard with Evidently. Online architecture. [CODE PRACTICE]

6.2. ML model monitoring dashboard with Evidently. Batch architecture. [CODE PRACTICE]

6.1. How to deploy a live ML monitoring dashboard

5.8. Log data drift test results to MLflow. [CODE PRACTICE]

5.7. Run data drift and model quality checks in a Prefect pipeline. [OPTIONAL CODE PRACTICE]

5.6. Run data drift and model quality checks in an Airflow pipeline. [OPTIONAL CODE PRACTICE]

5.5. Design a custom test suite with Evidently. [CODE PRACTICE]

5.4. Test ML model outputs and quality. [CODE PRACTICE]

5.3. Test input data quality, stability and drift. [CODE PRACTICE]

5.2. Train and evaluate an ML model. [OPTIONAL CODE PRACTICE]

5.1. Introduction to data and ML pipeline testing.

4.7. How to choose the ML monitoring deployment architecture.

4.6. Implementing custom metrics in Evidently [OPTIONAL, CODE PRACTICE]

4.5. Custom metrics in ML monitoring

4.4. How to choose a reference dataset in ML monitoring.

4.3. When to retrain machine learning models.

4.2. How to prioritize ML monitoring metrics

4.1. Logging for ML monitoring

3.6. Monitoring multimodal datasets.

3.5. Monitoring text data and embeddings. [CODE PRACTICE].

3.4. Monitoring embeddings drift

3.3. Monitoring text data quality and data drift with descriptors

3.2. Monitoring data drift on raw text data.

3.1. Introduction to NLP and LLM monitoring

2.8. Data and prediction drift in ML. [CODE PRACTICE]

2.5. Data quality in ML. [CODE PRACTICE].

2.3. Evaluating ML model quality. [CODE PRACTICE].

2.7. Deep dive into data drift detection. [OPTIONAL]