A summary of recent talks from enterprises building machine learning platforms with Metaflow.

Learn how you can streamline authoring, testing and maintenance of technical documentation with Jupyter Notebooks, nbdev and Docusaurus.

A discussion about the machine learning deployment stack and why data makes software different.

Metaflow and Outerbounds were at PyData Global! We hosted sessions on full stack ML and emphasized discussions on the evolution of the compute layer of the ML stack. This post organizes the presentations in a quick choose-your-own-adventure read.

A summary of recent talks from enterprises building machine learning platforms for Climate Tech with Metaflow.

Outerbounds integrates now with NVIDIA NIM microservices,

Metaflow 2.12 introduces a new API for launching

Build a simple production-ready MLOps pipeline using Metaflow, an OSS framework allowing data scientists to build production-ready machine learning workflows using a simple Python API, and Seldon.

Learn how to develop ML, AI, and data science with Metaflow, deploy alongside existing data pipelines on Airflow.

This post introduces high-level trends at the intersection of biology and AI, discusses new (and old) technical challenges in building reproducible and scalable systems for AI-driven computational biology, and how frameworks like Metaflow can help address them.

Engineers from Chainguard and Outerbunds discuss their common philosophy around security and machine learning workflows.

A book launch event including a discussion with authors Chip Huyen and Ville Tuulos about machine learning education and practice.

With Outerbounds-certified professional services, you can now implement ML/AI solutions on Outerbounds

The latest version of Metaflow includes support for AWS Trainium hardware accelerators which allow you to train large models cost-efficiently on AWS.

A peek into how Outerbounds views the ongoing evolution of the machine learning stack, in the wake of recent LLM waves.

Discover the power of DAGs to level up your data science projects with examples from feature transforms to workflow orchestration.

Developed internally at Netflix and open-sourced in 2019, Metaflow is now used to power machine learning in production by hundreds of companies.

This article outlines ten stages of operational maturity for deploying ML/AI systems to production. Which stage are you at?

Introducing DAG cards for machine learning pipelines. These cards make it easy to attach custom visual reports in every workflow, without having to install any additional tooling.

We sat down with Russell Brooks, Principal ML Engineer at Realtor.com, to discuss how his team uses Metaflow, and how it impacts Realtor.com.