21:I[4606,["9139","static/chunks/9139-29aac3297803e837.js","5162","static/chunks/5162-971519dd5e179b15.js","1163","static/chunks/app/%5Blocale%5D/(with-footer)/ai/%5BwebsiteName%5D/page-93c09f9a3f0813c2.js"],""] 23:I[1895,["9139","static/chunks/9139-29aac3297803e837.js","5162","static/chunks/5162-971519dd5e179b15.js","1163","static/chunks/app/%5Blocale%5D/(with-footer)/ai/%5BwebsiteName%5D/page-93c09f9a3f0813c2.js"],""] 22:T5eb,

What is Metaflow?

Metaflow is an open-source framework originally developed at Netflix for building and managing real-life ML, AI, and data science projects. It allows data scientists and ML/AI engineers to develop, debug, and deploy workflows in plain Python, leveraging cloud resources for scalable compute and data access. Metaflow integrates seamlessly with existing infrastructure, security, and data governance policies, supporting deployment on AWS, Azure, Google Cloud, and Kubernetes.

How to use Metaflow?

Develop workflows in Python, debug locally, and deploy to production with a single command. Metaflow handles versioning, orchestration, and compute scaling automatically. You can also try Metaflow Sandbox in the browser for a quick taste.

Metaflow's Core Features

Orchestration of data science workflows

Versioning of data and variables

Scalable compute using cloud resources

Seamless deployment to production

Integration with existing infrastructure

Metaflow's Use Cases

#1 Developing safe and reliable ML products

#2 Accelerating ML experimentation

#3 Improving data science processes

#4 Powering diverse projects from GenAI to business-oriented data science

Metaflow

📋Introduction

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