Building Data Pipelines with Metaflow

Mastering Workflow Orchestration, Data Transformations, and Pipeline Management

Duration

2 days (8 hours per day)

Level

Basic Level

Design and Tailor this course

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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Introduction to Metaflow

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

Introduction to Workflow Orchestration

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 Basics and Advanced Features

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

Building and Managing ML/AI Flows

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

Data Transformations and Integrations

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

Debugging and Troubleshooting

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

Collaboration and Teamwork

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

Case Studies and Hands-On Projects

Real-world case studies
Hands-on projects to build end-to-end ML/AI pipelines
Group activities and peer reviews

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Building Data Pipelines with Metaflow

Duration

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