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

Shreya Shankar joins Hugo Bowne-Anderson to discuss her team’s recent paper Operationalizing Machine Learning: An Interview Study.

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

We sat down with Jeremy Howard, the creator of fast.ai, to discuss the rapidly evolving AI landscape.

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.

An interview with Jules Belveze, an MLOps Engineer at Hypefactors where they apply NLP and AI for media intelligence.

Metaflow Sandboxes are now loaded with many new interactive ML tutorials: learn about cutting-edge AI and ML, right in your browser.

We share a six-step process for understanding cloud costs, attributing, and minimizing them. The process is conveniently integrated in the Outerbounds platform.

Yudhiesh Ravindranath, an MLOps Engineer at MoneyLion, discusses how his team builds and maintains an MLOps stack at a FinTech company.

You can now orchestrate workflows developed with Metaflow on Apache Airflow.

Metaflow can now be used equally across Google Cloud, Azure, or AWS without any changes in user code or workflows.

A knowledge base of free, practical data science and machine learning materials, including how to set up a modern data science infrastructure.

A workflow template built with Metaflow for fine-tuning LLMs for custom use cases.

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.

Sharing results of your ML experiments with colleagues has never been easier with the Metaflow card viewer. Use it from your sandbox today!

Russell Brooks joins Hugo to discuss how you can build a valuable machine learning team and tech stack in your organization.

Leveraging the Modern Data Stack for Machine Learning: how to combine DataOps and MLOps to produce business value.

A hands-on guided tour to LLMs, prompt engineering, retrieval-augmented generation, and building ML systems in the Metaflow Sandbox.

Today, we release support for distributed training and other high-performance computing workloads in Metaflow, powered by popular

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.