350M Tokens Don't Lie: Love And Hate In Hacker News
We analyzed topics covered by 100k Hacker News posts and the sentiments they evoke using LLMs. See the 14k topics we uncovered.
Building the ML-driven future
Fortnightly posts covering major Metaflow and Outerbounds releases, community events, case studies, and all things machine learning infrastructure.
Scaling LLM-Powered Document Understanding
Learn how to build production-grade document understanding systems using private LLMs.
Building AI/ML Without In-house Resources
With Outerbounds-certified professional services, you can now implement ML/AI solutions on Outerbounds quickly and efficiently, requiring minimal internal resources or expertise.
Cost-Efficient Generative AI on Outerbounds with NVIDIA NIM Inference Microservices
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: Running Flows In Notebooks And Scripts
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.
How To Organize Continuous Delivery of ML/AI Systems: a 10-Stage Maturity Model
This article outlines ten stages of operational maturity for deploying ML/AI systems to production. Which stage are you at?
Develop and Train Large Models Cost-efficiently with Metaflow and AWS Trainium
The latest version of Metaflow includes support for AWS Trainium hardware accelerators which allow you to train large models cost-efficiently on AWS.
AI & ML from the Trenches: A Climate Tech Special – Equilibrium Energy and Perennial Earth
A summary of recent talks from enterprises building machine learning platforms for Climate Tech with Metaflow.
Build Reproducible and Scalable Computational Biology Systems
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.
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