Blog
KitOps Release v0.2–Introducing Dev Mode and the ability to chain ModelKits
Welcome KitOps v0.2! This update brings two major features for working with LLMs, as well numerous...
Fine-tune your first large language model (LLM) with LoRA, llama.cpp, and KitOps in 5 easy steps
Getting started with LLMs can be intimidating. In this tutorial we will show you how to fine-tune a... Tagged with beginners, programming, tutorial, machinelearning.
I fine-tuned my model on a new programming language. You can do it too! 🚀
I have been using OpenAI ChatGPT-4 for a while now. I don't have a lot of bad stuff to say about... Tagged with webdev, javascript, beginners, programming.
Why enterprise AI projects are moving too slowly
This post lists the challenges with getting an AI/ML project from development into production and offers suggestions on organizational and tooling changes (like KitOps' ModelKits) that can help. Tagged with devops, ai, opensource, aiops.
KitOps: The Bridge Between AI/ML Models and DevOps
Yesterday, Brad Micklea, Jozu CEO and KitOps maintainer, was a guest on the Partially Redacted podcast hosted by Sean Falconer. The 45-minute conversation covered a lot of ground. Specifically, the current state of the KitOps project, where the project is headed, and some of our early ideas for productizing and releasing Jozu, which builds on top of KitOps. In this post I dive a bit deeper into a few of these topics.
Strategies for Tagging ModelKits
ModelKits, much like other OCI artifacts, can be identified using tags that are comprehensible to humans. This blog explores various strategies for effectively tagging your ModelKits.
Balancing Innovation and Responsibility in AI/ML Deployment with Jozu's Brad Micklea
Listen to this episode from Partially Redacted: Data Privacy, Security & Compliance on Spotify. In this episode, we dive into the world of MLOps, the engine behind secure and reliable AI/ML deployments. MLOps focuses on the lifecycle of machine learning models, ensuring they are developed and deployed efficiently and responsibly. With the explosion of ML applications, the demand for specialized tools has skyrocketed, highlighting the need for improved observability, auditing, and reproducibility. This shift necessitates an evolution in ML toolchains to address gaps in security, governance, and reliability. Jozu is a platform founded to tackle these very challenges by enhancing the collaboration between AI/ML and application development teams. Jozu aims to provide a comprehensive suite of tools focusing on efficiency throughout the model development and deployment process. This conversation discusses the importance of MLOps, the limitations of current tools, and how Jozu is paving the way for the future of secure and reliable ML deployments.
Beyond Git: A New Collaboration Model for AI/ML Development
Git is optimized to work with large numbers of small files, like text files. This alone makes Git impractical for managing such datasets.
The transitory nature of MLOps: Advocating for DevOps/MLOps coalescence
AI/ML is a wildfire of a trend. It’s being integrated into just about every application you can think... Tagged with ai, devops, opensource, machinelearning.
Do data scientists really like Git?
I have a theory: data scientists do not like Git. I think they did not adopt Git because they needed to version control their notebooks. They adopted Git because when they approached the software developers and DevOps engineers for collaboration that is what they were forced to do.