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Duration 21 hours
Course Outline
Introduction to Apache Airflow in Machine Learning
- Understanding the relevance of Apache Airflow to data science and an overview of the platform.
- Exploring key features that facilitate the automation of machine learning workflows.
- Initial setup of Airflow for data science projects.
Constructing Machine Learning Pipelines with Airflow
- Architecting DAGs for comprehensive, end-to-end ML workflows.
- Leveraging operators for data ingestion, preprocessing, and feature engineering.
- Managing scheduling and pipeline dependencies effectively.
Model Training and Validation Processes
- Automating model training tasks utilizing Airflow.
- Integrating Airflow with major ML frameworks like TensorFlow and PyTorch.
- Validating models and managing the storage of evaluation metrics.
Model Deployment and Continuous Monitoring
- Deploying machine learning models via automated pipelines.
- Monitoring deployed models through specific Airflow tasks.
- Managing retraining cycles and model updates.
Advanced Customization and System Integration
- Creating custom operators tailored for ML-specific tasks.
- Connecting Airflow with cloud platforms and various ML services.
- Enhancing Airflow workflows using plugins and sensors.
Optimization and Scaling of ML Pipelines
- Boosting workflow performance for large-scale data operations.
- Scaling Airflow deployments utilizing Celery and Kubernetes.
- Applying best practices for production-grade ML workflows.
Case Studies and Practical Implementation
- Examining real-world examples of ML automation driven by Airflow.
- Engaging in a hands-on exercise to build a complete end-to-end ML pipeline.
- Discussing common challenges and effective solutions in ML workflow management.
Summary and Future Directions
Requirements
- A solid grasp of machine learning workflows and underlying concepts.
- Foundational knowledge of Apache Airflow, particularly DAGs and operators.
- Strong proficiency in Python programming.
Target Audience
- Data scientists.
- Machine learning engineers.
- AI developers.
Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
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Working from first principles in a focused way, and moving to applying case studies within the same day
