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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 core concepts.
- Foundational understanding of Apache Airflow, including DAGs and operators.
- Proficiency in Python programming.
Target Audience
- Data scientists.
- Machine learning engineers.
- AI developers.
21 Hours
