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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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  1. ML OBSERVABILITY COURSE

Module 1: Introduction

Key concepts of machine learning monitoring and observability and how they fit in the ML lifecycle.

This theoretical module introduces the key topics of machine learning monitoring and observability.

It covers the following topics:

  • what can go wrong with ML models in production;

  • what ML monitoring and observability are and how they fit in the ML lifecycle;

  • what types of evaluation you might need, from model quality to data drift;

  • key considerations to keep in mind when designing your monitoring.

At the end of this module, you will know the key concepts related to ML monitoring and observability and how they will be covered throughout the course.

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Last updated 1 year ago