03-orchestration

3. Orchestration and ML Pipelines

3.0 Introduction: ML pipelines and Mage

3.1 Data preparation: ETL and feature engineering

3.2 Training: sklearn models and XGBoost

3.3 Observability: Monitoring and alerting

3.4 Triggering: Inference and retraining

3.5 Deploying: Running operations in production

Quickstart

1. Clone the following respository containing the complete code for this module:

   git clone https://github.com/mage-ai/mlops.git
   

2. Change directory into the cloned repo:

   cd mlops
   

3. Launch Mage and the database service (PostgreSQL):

   ./scripts/start.sh
   

4. The subproject that contains all the pipelines and code is named unit_3_observability

Need help?

1. Developer documentation
2. AI chat bot
3. Live chat with the Mage team directly

Notes previous editions

• 2022 Prefect notes
• 2023 Prefect notes