Apache Airflow for Data Science: Automating Machine Learning Pipelines

• 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

Apache Airflow is an open-source platform for orchestrating workflows and automating complex data pipelines.

This instructor-led, live training (online or onsite) is aimed at intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

By the end of this training, participants will be able to:

• Set up Apache Airflow for machine learning workflow orchestration.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Airflow with machine learning frameworks and tools.
• Deploy machine learning models using automated pipelines.
• Monitor and optimize machine learning workflows in production.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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