Generative AI Workflows, Compute, HPC, and Metaflow at PyData Global
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.
Metaflow 2.11: Building Observable ML/AI Systems
Make ML/AI systems observable with new Metaflow Dynamic Cards that provide real-time visualizations.
LLMs, RAG, and Fine-Tuning: A Hands-On Guided Tour
A hands-on guided tour to LLMs, prompt engineering, retrieval-augmented generation, and building ML systems in the Metaflow Sandbox.
Learn Full-Stack Machine Learning: Batteries Included
Learn how you can get started with full-stack ML today in a hands-on, project-driven course using production-grade ML systems.
Announcing the Metaflow Card Viewer
Sharing results of your ML experiments with colleagues has never been easier with the Metaflow card viewer. Use it from your sandbox today!
Whisper with Metaflow on Kubernetes
How to run and optimize OpenAI's Whisper model on a state-of-the-art Kubernetes cluster powered by Metaflow.
Learn Machine Learning and AI Infrastructure in the Browser
Metaflow Sandboxes are now loaded with many new interactive ML tutorials: learn about cutting-edge AI and ML, right in your browser.
Announcing The Outerbounds Platform
Outerbounds Platform is now generally available! Get started with a managed platform for data and ML workloads that operates seamlessly with open-source Metaflow.
Event: How to Build a Full-Stack Recommender System
Join Jacopo Tagliabue and Hugo Bowne-Anderson in a live code along session to dive into how to build a production-grade recommender system.
Case Study: MLOps for FinTech using Metaflow
Yudhiesh Ravindranath, an MLOps Engineer at MoneyLion, discusses how his team builds and maintains an MLOps stack at a FinTech company.
Full Stack Machine Learning, ML Engineering, and SWE Skills for Data Science
Ethan Rosenthal joins Hugo to discuss how you can build your career and the layers of a full machine learning stack in your organization.
The Moving Parts of the Full Machine Learning Stack and Building ML Platforms
Russell Brooks joins Hugo to discuss how you can build a valuable machine learning team and tech stack in your organization.
MLOps for Foundation Models: Whisper and Metaflow
We show how to use large models like Whisper in a production-grade workflow by applying MLOps practices with Metaflow.
Now Available: Machine Learning with Metaflow on Azure
Metaflow on Azure provides one of the most user-friendly machine learning experiences on Azure, both for data scientists as well as engineers.
Ask Me Anything: Machine Learning Infrastructure for Humans
A collection of responses from an AMA event where Chip Huyen and Ville Tuulos addressed questions about machine learning infrastructure.
Applying the Practical Machine Learning Infrastructure Stack
How can you leverage modern tools to apply a state-of-the-art machine learning infrastructure stack in practice?
Announcing Metaflow Sandboxes: Free Data Science Infrastructure in the Browser
Use new Metaflow Sandboxes to evaluate Metaflow and the full infrastructure stack behind it in the browser without having to install anything locally
Parallelizing Stable Diffusion for Production Use Cases
How to run massively parallel Stable Diffusion for production use cases, producing new images automatically in a highly-available manner.
Event: Machine Learning + Infrastructure for Humans
A book launch event including a discussion with authors Chip Huyen and Ville Tuulos about machine learning education and practice.
Book Launch: Effective Data Science Infrastructure
By reading a new book, Effective Data Science Infrastructure, you will learn how to set up infrastructure for ML and data science applications, similar to the stack that powers Netflix and hundreds of other modern companies.
Developing Scalable Feature Engineering DAGs
Discover the power of DAGs to level up your data science projects with examples from feature transforms to workflow orchestration.
The Modern Stack of ML Infrastructure
A discussion about the machine learning deployment stack and why data makes software different.
Excited to join Metaflow and Outerbounds
Hugo Bowne-Anderson discussed his journey to becoming Head of Developer Relations at Outerbounds.
Data Scientists Don't Need to Know Kubernetes with Metaflow
With Metaflow, data scientists can leverage K8s clusters for their work. Metaflow presents a user-friendly UX to data scientists, while working nicely with production infrastructure.
Notebooks in Production with Metaflow
Learn how to use notebooks in production ML pipelines with a new Metaflow feature. This is model framework-agnostic and so will work with all types of ML models.
Metaflow Highlights of 2021
Developed internally at Netflix and open-sourced in 2019, Metaflow is now used to power machine learning in production by hundreds of companies.
Integrating Pythonic Visual Reports into ML Pipelines
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.
MLOps vs DevOps: the Difference Data Makes
A discussion about the machine learning deployment stack and why data makes software different.
Machine Learning Pipelines: from Prototype to Production
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.