Course Outline
Introduction to Apache Airflow in Machine Learning
- An overview of Apache Airflow and its significance within data science disciplines
- Essential features for automating machine learning workflows
- Establishing Airflow configurations for data science projects
Constructing Machine Learning Pipelines with Airflow
- Architecting DAGs for comprehensive ML workflows
- Leveraging operators for data ingestion, preprocessing, and feature engineering
- Managing pipeline dependencies and scheduling tasks
Training and Validating Models
- Automating model training procedures using Airflow
- Integrating Airflow with ML frameworks such as TensorFlow and PyTorch
- Validating models and recording evaluation metrics
Deploying and Monitoring Models
- Rolling out machine learning models through automated pipelines
- Supervising deployed models via Airflow tasks
- Managing retraining cycles and model updates
Advanced Customization and Integrations
- Creating custom operators tailored for ML-specific tasks
- Connecting Airflow with cloud platforms and ML services
- Enhancing Airflow workflows through plugins and sensors
Optimizing and Scaling ML Pipelines
- Enhancing workflow performance for large-scale data operations
- Scaling Airflow deployments using Celery and Kubernetes
- Adopting best practices for production-grade ML workflows
Case Studies and Practical Implementation
- Real-world examples of ML automation achieved with Airflow
- Practical exercise: Constructing a complete end-to-end ML pipeline
- Exploring challenges and effective solutions in ML workflow management
Summary and Future Directions
Requirements
- A solid grasp of machine learning workflows and core concepts
- Fundamental knowledge of Apache Airflow, specifically regarding DAGs and operators
- Proficiency in Python programming
Target 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
