MLOps

Exploring the steps and processes of building an MLOps pipeline.

Join us as Brad Micklea, CEO of Jozu and maintainer of KitOps, shares exciting updates and insights on the Partially Redacted podcast with Sean Falconer. Discover the journey behind KitOps, the challenges of deploying AI/ML models, and how KitOps’ ModelKits and CLI are revolutionizing the landscape by bridging the gap between data science and DevOps. Learn about Jozu’s ambitious plans for creating a public hub for ModelKits, enterprise solutions for secure AI operations, and how you can be part of this innovative community.

Welcome KitOps v0.2! This update brings two major features for working with LLMs, as well numerous smaller enhancements. We are excited to introduce KitOps’ Dev mode – making it possible to test LLMs locally (even if you don’t have an internet connection or GPU).

Dive into the world of large language models with our step-by-step tutorial on fine-tuning using LoRA, powered by tools like llama.cpp and KitOps. LoRA (Low-Rank Adaptation) is an efficient technique for adapting pre-trained models, minimizing computational overhead. We’ll guide you through setting up your environment, creating a Kitfile, building a LoRA adapter, and deploying your fine-tuned model. By the end, you’ll have a packaged model ready for deployment.

In AI projects the biggest (and most solvable) source of friction are the handoffs between data scientists, application developers, testers, and infrastructure engineers as the project moves from development to production. This friction exists at every company size, in every industry, and every vertical. Gartner’s research shows that AI/ML projects are rarely deployed in under 9 months despite the use of ready-to-go large language models (LLMs) like Llama, Mistral, and Falcon.