machinelearning

This post explores multiple tools to help you secure your AI project through model attestation and Software Bill of Materials (SBOMs)

Learn how to fine-tune and deploy your first Small Language Model (sLLM) using KitOps Dev Mode.

Discover 25 open-source tools to streamline your AI projects from development to production.

AWS bet early on cloud computing and maintained focus, leading to their success. Today, enterprises face similar challenges with AI, divided between cautious approaches and building in-house AI teams. This article outlines a path to tool selection, project prioritization, and organizational structure that will lead to an enduring competitive edge.

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.

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.