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Scaling LLM-Powered Document Understanding
Learn how to build production-grade document understanding systems using private LLMs.
Fortnightly posts covering major Metaflow and Outerbounds releases, community events, case studies, and all things machine learning infrastructure.
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.
This post introduces high-level trends at the intersection of biology and AI, discusses new (and old) technical challenges in building reproducible and scalable systems for AI-driven computational biology, and how frameworks like Metaflow can help address them.
Join Hugo Bowne-Anderson for two live courses on building full-stack machine learning systems.
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