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  • Welcome!
  • ML OBSERVABILITY COURSE
    • Module 1: Introduction
      • 1.1. ML lifecycle. What can go wrong with ML in production?
      • 1.2. What is ML monitoring and observability?
      • 1.3. ML monitoring metrics. What exactly can you monitor?
      • 1.4. Key considerations for ML monitoring setup
      • 1.5. ML monitoring architectures
    • Module 2: ML monitoring metrics
      • 2.1. How to evaluate ML model quality
      • 2.2. Overview of ML quality metrics. Classification, regression, ranking
      • 2.3. Evaluating ML model quality [CODE PRACTICE]
      • 2.4. Data quality in machine learning
      • 2.5. Data quality in ML [CODE PRACTICE]
      • 2.6. Data and prediction drift in ML
      • 2.7. Deep dive into data drift detection [OPTIONAL]
      • 2.8. Data and prediction drift in ML [CODE PRACTICE]
    • Module 3: ML monitoring for unstructured data
      • 3.1. Introduction to NLP and LLM monitoring
      • 3.2. Monitoring data drift on raw text data
      • 3.3. Monitoring text data quality and data drift with descriptors
      • 3.4. Monitoring embeddings drift
      • 3.5. Monitoring text data [CODE PRACTICE]
      • 3.6. Monitoring multimodal datasets
    • Module 4: Designing effective ML monitoring
      • 4.1. Logging for ML monitoring
      • 4.2. How to prioritize ML monitoring metrics
      • 4.3. When to retrain machine learning models
      • 4.4. How to choose a reference dataset in ML monitoring
      • 4.5. Custom metrics in ML monitoring
      • 4.6. Implementing custom metrics in Evidently [OPTIONAL]
      • 4.7. How to choose the ML monitoring deployment architecture
    • Module 5: ML pipelines validation and testing
      • 5.1. Introduction to data and ML pipeline testing
      • 5.2. Train and evaluate an ML model [OPTIONAL CODE PRACTICE]
      • 5.3. Test input data quality, stability and drift [CODE PRACTICE]
      • 5.4. Test ML model outputs and quality [CODE PRACTICE]
      • 5.5. Design a custom test suite with Evidently [CODE PRACTICE]
      • 5.6. Run data drift and model quality checks in an Airflow pipeline [OPTIONAL CODE PRACTICE]
      • 5.7. Run data drift and model quality checks in a Prefect pipeline [OPTIONAL CODE PRACTICE]
      • 5.8. Log data drift test results to MLflow [CODE PRACTICE]
    • Module 6: Deploying an ML monitoring dashboard
      • 6.1. How to deploy a live ML monitoring dashboard
      • 6.2. ML model monitoring dashboard with Evidently. Batch architecture [CODE PRACTICE]
      • 6.3. ML model monitoring dashboard with Evidently. Online architecture [CODE PRACTICE]
      • 6.4. ML monitoring with Evidently and Grafana [OPTIONAL CODE PRACTICE]
      • 6.5. Connecting the dots: full-stack ML observability
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  • What we covered in the course
  • Summing up
  • Enjoyed the course?
  1. ML OBSERVABILITY COURSE
  2. Module 6: Deploying an ML monitoring dashboard

6.5. Connecting the dots: full-stack ML observability

A brief summary of the Open-source ML observability course learnings.

Previous6.4. ML monitoring with Evidently and Grafana [OPTIONAL CODE PRACTICE]

Last updated 1 year ago

Video 5. , by Emeli Dral

What we covered in the course

This is the final lesson of the Open-source ML observability course. Let’s recap what we’ve learned during the course!

Summing up

Start small and expand. Ad hoc reports are a good starting point for ML monitoring that is easy to implement. It is useful for initial learning about data and model quality before establishing a comprehensive monitoring system. Don’t hesitate to start small!

As you progress and deploy multiple models in production, or if you work with mission-critical use cases, you’d need a more extensive setup.

Jobs to be done to implement full-stack production ML observability:

  • Immediate monitoring flow helps to detect issues and to alert during model inference. If you have a production-critical service, it is essential to implement it.

  • Delayed monitoring flow allows you to evaluate model quality when you get the labels (as ground truth is often not available immediately!).

  • Model evaluation flow is needed to test model quality at updates and retraining.

Observability components to keep in mind when building ML monitoring:

  • Logging layer. If you have a production service, implementing logging is a must to capture model inferences and collect performance metrics.

  • Alerting layer allows you to monitor metrics and get notifications when things go wrong.

  • Dashboarding and analytics help to visualize the performance, quickly detect root cause issues, and define actions for debugging and retraining.

Enjoyed the course?

ML monitoring metrics We covered what metrics to use to assess , , and . We also discussed how to implement for specific use cases. For example, you can integrate custom metrics related to business KPIs and specific aspects of model quality into ML monitoring.

ML monitoring design We covered different aspects of ML monitoring design, including how to select and use . We also discussed the connection between cadence and ML monitoring.

ML monitoring architectures We explored different , from ad hoc reports and test suites to batch and real-time ML monitoring, and learned how to implement them in practice in and .

ML monitoring for unstructured data We also touched on how to build a monitoring system for and .

⭐️ to contribute back! This helps us create free, open-source tools and content for the community.

📌 so we can make this course better.

💻 for more discussions and materials on ML monitoring and observability.

data quality
model quality
data drift
custom metrics
reference datasets
model retraining
ML monitoring architectures
Module 5
Module 6
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embeddings
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Connecting the dots: full-stack ML observability