Fortnightly posts covering major Metaflow and Outerbounds releases, community events, case studies, and all things machine learning infrastructure.
Catch up with Outerbounds this fall as we travel to five major tech conferences across the U.S., showcasing the latest features in the platform, covering ML, LLMs, and other data use cases, and cool applications that you can build with them.
We analyzed topics covered by 100k Hacker News posts and the sentiments they evoke using LLMs. See the 14k topics we uncovered.
Learn how to build production-grade document understanding systems using private LLMs.
With Outerbounds-certified professional services, you can now implement ML/AI solutions on Outerbounds quickly and efficiently, requiring minimal internal resources or expertise.
Outerbounds integrates now with NVIDIA NIM microservices, so you can start building with performant, cost-effective GenAI models, deployed in your own environment.
Metaflow 2.12 introduces a new API for launching and managing flows which enables a number of highly requested use cases, such as executing flows in notebooks, as demonstrated in this article.
This article outlines ten stages of operational maturity for deploying ML/AI systems to production. Which stage are you at?
The latest version of Metaflow includes support for AWS Trainium hardware accelerators which allow you to train large models cost-efficiently on AWS.
A summary of recent talks from enterprises building machine learning platforms for Climate Tech with Metaflow.
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