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Course Outline
Introduction to Apache Airflow for Machine Learning
- Overview of Apache Airflow and its significance to data science.
- Key features enabling the automation of machine learning workflows.
- Establishing Airflow for data science projects.
Building Machine Learning Pipelines with Airflow
- Designing DAGs for comprehensive end-to-end ML workflows.
- Utilizing operators for data ingestion, preprocessing, and feature engineering.
- Scheduling and managing pipeline dependencies.
Model Training and Validation
- Automating model training tasks using Airflow.
- Integrating Airflow with ML frameworks (e.g., TensorFlow, PyTorch).
- Validating models and storing evaluation metrics.
Model Deployment and Monitoring
- Deploying machine learning models via automated pipelines.
- Monitoring deployed models using Airflow tasks.
- Managing retraining and model updates.
Advanced Customization and Integration
- Developing custom operators tailored for ML-specific tasks.
- Integrating Airflow with cloud platforms and ML services.
- Extending Airflow workflows with plugins and sensors.
Optimizing and Scaling ML Pipelines
- Enhancing workflow performance for large-scale data.
- Scaling Airflow deployments using Celery and Kubernetes.
- Best practices for implementing production-grade ML workflows.
Case Studies and Practical Applications
- Real-world examples of ML automation using Airflow.
- Hands-on exercise: Constructing 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.
- A foundational understanding of Apache Airflow, including Directed Acyclic Graphs (DAGs) and operators.
- Proficiency in Python programming.
Target Audience
- Data scientists.
- Machine learning engineers.
- AI developers.
21 Hours
