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
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
