KitOps Reviews and details

Social recommendations and mentions

• Docker Model Runner

  It's not the only one using OCI to package models. There's a CNCF project called KitOps (https://kitops.org) that has been around for quite a bit longer. It solves some of the limitations that using Docker has, one of those being that you don't have to pull the entire project when you want to work on it. Instead, you can pull just the data set, tuning, model, etc. - Source: Hacker News / 21 days ago

• Docker Model Runner

  Seems like https://kitops.org/ but fewer features. - Source: Hacker News / 21 days ago

• Securing MCP: Applying Lessons Learned from the Language Server Protocol

  At Jozu, we are uniquely positioned to address these critical MCP adoption challenges. With extensive experience gained from pioneering work on LSP and our development of KitOps—a proven open-source solution trusted by enterprises for securely packaging and deploying AI/ML workloads—we are prepared to solve MCP’s most pressing security and packaging issues. Partnering with us will help your organization... - Source: dev.to / about 1 month ago

• Turn Your Existing DevOps Pipeline Into an MLOps Pipeline

  Maintaining two separate pipelines for the same functionality can introduce communication overhead, technical debt, and waste company resources. As a result, it is wiser to bind the MLOps and DevOps pipelines into a single unit for efficient deployment of software engineering and machine learning projects. This can be easily achieved by embracing KitOps and ModelKit. Furthermore, compatibility with other open... - Source: dev.to / about 2 months ago

• 10 Must-Know Open Source Platform Engineering Tools for AI/ML Workflows

  Whether you're building scalable pipelines, tracking experiments, or deploying models into production, KitOps can tackle the complexities of ML projects and model development while keeping your workflow efficient, user-friendly, and robust. - Source: dev.to / 3 months ago

• Deploying ML projects with Argo CD

  To address these issues, this article demonstrates how Argo CD, a Kubernetes continuous delivery tool, can simplify the deployment process and transform how ML engineers and data scientists implement their projects. You will also learn to effectively package and seamlessly share your ML projects using KitOps: a ModelKit-based packaging tool. - Source: dev.to / 3 months ago

• Accelerating ML Development with DevPods and ModelKits

  Explore our resources, join the conversation on Discord, or check out our guide to get started. - Source: dev.to / 3 months ago

• Python in DevOps: Automation, Efficiency, and Scalability

  KitOps is an innovative tool designed for MLOps (Machine Learning Operations). AI workloads usually need more complex infrastructure than traditional software, but KitOps makes this easier by offering pre-configured, modular solutions that simplify the deployment, scaling, and management of AI models and pipelines. - Source: dev.to / 3 months ago

• AIOps, DevOps, MLOps, LLMOps – What’s the Difference?

  Containerization: Technologies like Docker, ModelKits, and Kubernetes to standardize and automate deployments in a controlled, scalable way. - Source: dev.to / 4 months ago

• Understanding the MLOps Lifecycle

  KitOps simplifies MLOps by bringing order and standardization to AI/ML development. By leveraging existing DevOps principles, KitOps allows teams to manage machine learning models, datasets, code, and metadata in a way that promotes collaboration, security, and efficiency. To learn more about KitOps, visit the project site, which also has an easy-to-follow guide to get you started. - Source: dev.to / 5 months ago

• Platform Engineering vs. MLOps: Key Comparisons

  Jozu offers tools like KitOps for model packaging and Jozu Hub for secure AI registries, providing a unified approach to streamline processes and drive innovation. Get started with KitOps or join the conversation on Discord. - Source: dev.to / 5 months ago