Building Data Pipelines with Metaflow
Mastering Workflow Orchestration, Data Transformations, and Pipeline Management
Duration
2 days (8 hours per day)
Level
Basic Level
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As per your team needs
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This course provides an in-depth exploration of Metaflow, a framework for managing real-life data science and machine learning projects. Students will learn to design, implement, deploy, and manage scalable and robust ML/AI pipelines using Metaflow. The course covers fundamental concepts, hands-on exercises, and best practices for leveraging Metaflow’s features to streamline data workflows and facilitate collaboration.
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- Data Engineers
- Data Scientists
- Data Analysts
- Machine Learning Engineers
- IT Professionals involved in data management and analytics
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- Overview of Metaflow and its role in ML/AI pipelines
- Key features and benefits of using Metaflow
- Installation and configuration of Metaflow
- Introduction to the Metaflow command-line interface (CLI)
- Hands-on Demo:
- Setting up Metaflow
- Basic operations using Metaflow CLI
- Overview of workflow orchestration concepts
- Importance of orchestration in ML/AI pipelines
- Common orchestration tools and their features (e.g., Apache Airflow, Luigi)
- Basics of scheduling and task dependencies
- Hands-on Demo:
- Setting up a simple workflow using an orchestration tool
- Scheduling tasks and managing dependencies
- Metaflow concepts: Flows, steps, and tasks
- Writing and running your first flow
- Advanced features: Branching, parallelism, and conditional logic
- Handling artifacts and parameters
- Hands-on Demo:
- Creating and running basic flows
- Implementing advanced flow features in Metaflow
- Designing robust ML/AI pipelines with Metaflow
- Managing dependencies and resources
- Scheduling and automating flows
- Monitoring and logging flow executions
- Hands-on Demo:
- Building and managing complex ML/AI flows
- Scheduling and automating flows using Metaflow
- Performing data transformations within Metaflow
- Integrating Metaflow with data sources (e.g., databases, data lakes)
- Using external libraries and tools within Metaflow flows
- Managing data versions and ensuring data consistency
- Hands-on Demo:
- Implementing data transformations in Metaflow
- Integrating external data sources and libraries into flows
- Common issues in Metaflow and how to resolve them
- Debugging techniques and best practices
- Using Metaflow’s built-in debugging tools
- Handling errors and exceptions gracefully
- Hands-on Demo:
- Debugging a Metaflow pipeline
- Troubleshooting and resolving common issues
- Sharing and collaborating on flows
- Version control and collaborative features
- Documenting and communicating workflows
- Hands-on Demo:
- Collaborating on a Metaflow project
- Using version control in Metaflow
- Real-world case studies
- Hands-on projects to build end-to-end ML/AI pipelines
- Group activities and peer reviews
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- Basic understanding of data pipelines and ETL processes
- Familiarity with Python programming
