20 posts tagged with "infrastructure"

You can now access secrets securely in Metaflow flows using the new secrets decorator.

An interview with Federico Bianchi, a postdoctoral NLP researcher at Stanford exploring the boundaries of large language models.

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.

How to run and optimize OpenAI's Whisper model on a state-of-the-art Kubernetes cluster powered by Metaflow.

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.

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

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.

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.

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

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.

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

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

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.