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Duration 21 hours
Course Outline
Introduction to Apache Airflow in Machine Learning
- Overview of Apache Airflow and its significance in data science
- Core features for automating machine learning workflows
- Initial setup of Airflow for data science initiatives
Constructing Machine Learning Pipelines with Airflow
- Designing DAGs for comprehensive ML workflows
- Leveraging operators for data ingestion, preprocessing, and feature engineering
- Managing scheduling and pipeline interdependencies
Training and Validating Models
- Automating model training tasks using Airflow
- Integrating Airflow with ML frameworks (such as TensorFlow and PyTorch)
- Validating models and recording evaluation metrics
Deploying and Monitoring Models
- Deploying machine learning models through automated pipelines
- Monitoring deployed models via Airflow tasks
- Managing retraining and model version updates
Advanced Customization and System Integration
- Creating custom operators for ML-specific operations
- Connecting Airflow with cloud platforms and ML services
- Expanding Airflow capabilities using plugins and sensors
Optimizing and Scaling ML Pipelines
- Enhancing workflow performance for large-scale data sets
- Scaling Airflow deployments utilizing Celery and Kubernetes
- Best practices for production-ready ML workflows
Case Studies and Real-World Applications
- Practical examples of ML automation with Airflow
- Practical exercise: Constructing an end-to-end ML pipeline
- Review of common challenges and solutions in ML workflow management
Summary and Future Steps
Requirements
- Working knowledge of machine learning processes and core concepts
- Fundamental grasp of Apache Airflow, particularly DAGs and operators
- Competence 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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