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Course Outline
Introduction to Apache Airflow for Machine Learning
- Overview of Apache Airflow and its relevance to data science
- Key features for automating machine learning workflows
- Setting up Airflow for data science projects
Building Machine Learning Pipelines with Airflow
- Designing DAGs for end-to-end ML workflows
- Using operators for data ingestion, preprocessing, and feature engineering
- Scheduling and managing pipeline dependencies
Model Training and Validation
- Automating model training tasks with Airflow
- Integrating Airflow with ML frameworks (e.g., TensorFlow, PyTorch)
- Validating models and storing evaluation metrics
Model Deployment and Monitoring
- Deploying machine learning models using automated pipelines
- Monitoring deployed models with Airflow tasks
- Handling retraining and model updates
Advanced Customization and Integration
- Developing custom operators for ML-specific tasks
- Integrating Airflow with cloud platforms and ML services
- Extending Airflow workflows with plugins and sensors
Optimizing and Scaling ML Pipelines
- Improving workflow performance for large-scale data
- Scaling Airflow deployments with Celery and Kubernetes
- Best practices for production-grade ML workflows
Case Studies and Practical Applications
- Real-world examples of ML automation using Airflow
- Hands-on exercise: Building an end-to-end ML pipeline
- Discussion of challenges and solutions in ML workflow management
Summary and Next Steps
Requirements
- Familiarity with machine learning workflows and concepts
- Basic understanding of Apache Airflow, including DAGs and operators
- Proficiency in Python programming
Audience
- Data scientists
- Machine learning engineers
- AI developers
21 Hours
