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The Jeff Bullas Show: The Secret World of AI: What Businesses Are Doing Behind Closed Doors

Brad is the Founder & CEO of  and a project lead for the open source  project, a toolset designed to increase the speed and safety of building, testing, and managing AI/ML models in production. ‌This is Brad’s second startup, his first (Codenvy, the market’s first container-based developer environment) was sold to Red Hat in 2017. ‌In his 25-year career in the developer tools and DevOps software market, he’s been the GM for Amazon’s API Gateway, and built open- and closed-source products that have been leaders in Gartner Magic Quadrants. ‌In his free time he enjoys cycling, reading, and vintage cars. ‌What you will learn Learn the importance of protecting intellectual property and sensitive data in AI. Comprehend how AI is less deterministic and more human-like than traditional software. Recognize the challenges companies face in integrating AI while keeping their data secure. Explore the process and collaboration needed between data scientists and software engineers. Examine a real-world example of using AI for inventory management in a global retailer. Identify the problem of model drift and how to address it. Distinguish between consumer AI applications and enterprise AI needs. Appreciate the rapid pace of AI development and its unprecedented nature.

Brad Micklea
KitOpsMLOpsOpen Source

10 Open Source Tools for Building MLOps Pipelines

This blog explores 10 open source MLOps tools that can help build an effective (and flexible) MLOps pipeline.

Jesse Williams
KitOpsMLOpsOpen Source

A step-by-step guide to building an MLOps pipeline - Jozu MLOps

Exploring the steps and processes of building an MLOps pipeline.

Jesse Williams
KitOpsModelKitOpen Source

When to Dockerize vs. When to use ModelKit - Jozu MLOps

ML development can often be a cumbersome and iterative process, with many open source tools, built to handle specific parts of the machine learning workflow. In this post we explore when to use Docker and when to use ModelKits.

Jesse Williams
KitOpsDev ModeRelease Notes

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...

Gorkem Ercan
KitOpsModelKitsLLMsLlama

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.

Rajat Raina
KitOpsModelKitsLLMsCommunity

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.

Nevo David
MLOpsKitOpsModelKits

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.

Brad Micklea
MLOpsKitOpsmachine learningopen source

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.

Jesse Williams
MLOpsKitOpsModelKits

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.

Gorkem Ercan
MLOpsKitOpsmachine learningpodcast

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.

Brad Micklea
gitDevOpsopen sourcemachine learning

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.

Gorkem Ercan
AIDevOpsopen sourcemachine learning

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.

Jesse Williams
thought leadership

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.

Gorkem Ercan