
AI & ML from the Trenches: Adept.ai, Autodesk, and Epignosis – Massive Compute and GenAI
A summary of recent talks from enterprises building machine learning platforms with Metaflow.
Fortnightly posts covering major Metaflow and Outerbounds releases, community events, case studies, and all things machine learning infrastructure.
A summary of recent talks from enterprises building machine learning platforms with Metaflow.
Today, we release support for distributed training and other high-performance computing workloads in Metaflow, powered by popular open-source frameworks like Ray, Torch, Deepspeed, Tensorflow and MPI, introduced in this article.
Today, we release support for cost-efficient distributed training and other high-performance computing workloads in Metaflow. Read this post for the backstory.
A hands-on guided tour to LLMs, prompt engineering, retrieval-augmented generation, and building ML systems in the Metaflow Sandbox.
We’re excited to be in Chicago later this month for KubeCon North America.
A summary of recent talks from enterprises building machine learning platforms with Metaflow.
You can now install dependencies from PyPI as well as Conda in your Metaflow steps.
A summary of recent talks from enterprises building machine learning platforms with Metaflow.
Reactive, configurable, cheaper LLM fine-tuning with Metaflow
Learn how to use Retrieval Augmented Generation to control hallucinations and get more relevant responses from LLMs.
Learn how to develop ML, AI, and data science with Metaflow, deploy alongside existing data pipelines on Airflow.
Engineers from Chainguard and Outerbunds discuss their common philosophy around security and machine learning workflows.
Explore four major new features in the Outerbounds Platform
Learn how you can get started with full-stack ML today in a hands-on, project-driven course using production-grade ML systems.
A workflow template built with Metaflow for fine-tuning LLMs for custom use cases.
An overview of instruction tuning for large language models using the LLaMA family of models.
We sat down with Jason Reid, a Co-founder at Tabular, to discuss the Apache Iceberg project and how it fits into the modern data stack.
You can now access secrets securely in Metaflow flows using the new secrets decorator.
Using the new event triggering feature in Metaflow, you can compose systems using workflows as building blocks and trigger them based on external events.
An interview with Federico Bianchi, a postdoctoral NLP researcher at Stanford exploring the boundaries of large language models.
Load and process tabular data at lightning speed using Apache Arrow and Metaflow.
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.
We use Metaflow to train Dolly, to show an example of fine-tuning LLMs and what it takes to use these models in practice.
A peek into how Outerbounds views the ongoing evolution of the machine learning stack, in the wake of recent LLM waves.
Sharing results of your ML experiments with colleagues has never been easier with the Metaflow card viewer. Use it from your sandbox today!
How to run and optimize OpenAI's Whisper model on a state-of-the-art Kubernetes cluster powered by Metaflow.
Metaflow Sandboxes are now loaded with many new interactive ML tutorials: learn about cutting-edge AI and ML, right in your browser.
An interview with Yudhiesh Ravindranath, an MLOps Engineer at MoneyLion, detailing the extensions their team has built on top of Metaflow.
You can now orchestrate workflows developed with Metaflow on Apache Airflow.
Outerbounds Platform is now generally available! Get started with a managed platform for data and ML workloads that operates seamlessly with open-source Metaflow.
Join Jacopo Tagliabue and Hugo Bowne-Anderson in a live code along session to dive into how to build a production-grade recommender system.
Yudhiesh Ravindranath, an MLOps Engineer at MoneyLion, discusses how his team builds and maintains an MLOps stack at a FinTech company.
Ethan Rosenthal joins Hugo to discuss how you can build your career and the layers of a full machine learning stack in your organization.
Metaflow can now be used equally across Google Cloud, Azure, or AWS without any changes in user code or workflows.
Russell Brooks joins Hugo to discuss how you can build a valuable machine learning team and tech stack in your organization.
An interview with Jules Belveze, an MLOps Engineer at Hypefactors where they apply NLP and AI for media intelligence.
A recap of a chat with Shreya Shankar about her paper, Operationalizing Machine Learning: Patterns and Pain Points from MLOps Practitioners.
A conversation with Russell Brooks and Hugo Bowne-Anderson on the journey to reproducible and automated ML-powered software.
We show how to use large models like Whisper in a production-grade workflow by applying MLOps practices with Metaflow.
Metaflow on Azure provides one of the most user-friendly machine learning experiences on Azure, both for data scientists as well as engineers.
A collection of responses from an AMA event where Chip Huyen and Ville Tuulos addressed questions about machine learning infrastructure.
Shreya Shankar joins Hugo Bowne-Anderson to discuss her team’s recent paper Operationalizing Machine Learning: An Interview Study.
How can you leverage modern tools to apply a state-of-the-art machine learning infrastructure stack in practice?
Use new Metaflow Sandboxes to evaluate Metaflow and the full infrastructure stack behind it in the browser without having to install anything locally
How to run massively parallel Stable Diffusion for production use cases, producing new images automatically in a highly-available manner.
A book launch event including a discussion with authors Chip Huyen and Ville Tuulos about machine learning education and practice.
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.
How to combine DataOps and MLOps to produce business value: good machine learning tools provide a better way to think about the division of work and culture.
Learn how you can streamline authoring, testing and maintenance of technical documentation with Jupyter Notebooks, nbdev and Docusaurus.
Leveraging the Modern Data Stack for Machine Learning: how to combine DataOps and MLOps to produce business value.
We are happy to announce support for mutable tags in Metaflow! Learn five key patterns for effective human-in-the-loop workflows which leverage the new feature.
Discover the power of DAGs to level up your data science projects with examples from feature transforms to workflow orchestration.
A knowledge base of free, practical data science and machine learning materials, including how to set up a modern data science infrastructure.
A discussion about the machine learning deployment stack and why data makes software different.
Data scientists can now scale out their machine learning workflows to Kubernetes clusters using Metaflow and Argo, without needing to know the ins and outs of K8S.
How to get started today with ML in production: tools, workflows, and mental models.
A fireside chat about product management for ML products across various industries.
Hugo Bowne-Anderson discussed his journey to becoming Head of Developer Relations at Outerbounds.
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.
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.
Developed internally at Netflix and open-sourced in 2019, Metaflow is now used to power machine learning in production by hundreds of companies.
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.
A discussion about the machine learning deployment stack and why data makes software different.
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.
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