29 posts tagged with "MLOps"

There are many ways to deploy models and perform inference. Here, we share our decision rubric for model deployments using LLM inference as an example.

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.

How to run massively parallel Stable Video Diffusion for production use cases, producing new images and videos automatically in a highly-available manner.

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

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

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

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

Learn how you can get started with full-stack ML today in a hands-on, project-driven course using production-grade ML systems.

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.

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.

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.

Join Jacopo Tagliabue and Hugo Bowne-Anderson in a live code along session to dive into how to build a production-grade recommender system.

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.

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

How to run massively parallel Stable Diffusion for production use cases, producing new images automatically in a highly-available manner.

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.

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.

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.