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
 21 Hours

Delivery Options

Private Group Training

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  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

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