Dockerize.it
ChatGPT
Midjourney
Jasper.ai
Tailwind CSS
Writesonic
Kubernetes
Flagsmith
KitOps
Kubernetes
Prometheus
Apache Airflow
Git
ChatGPT
Grafana
Midjourney
KitOps is a packaging, versioning, and sharing system for AI/ML projects that uses open standards so it works with the AI/ML, development, and DevOps tools you are already using, and can be stored in your enterprise container registry. It's AI/ML platform engineering teams' preferred solution for securely packaging and versioning assets.
KitOps creates a ModelKit for your AI/ML project which includes everything you need to reproduce it locally or deploy it into production. You can even selectively unpack a ModelKit so different team members can save time and storage space by only grabbing what they need for a task. Because ModelKits are immutable, signable, and live in your existing container registry they're easy for organizations to track, control, and audit.
ModelKits simplify the handoffs between data scientists, application developers, and SREs working with LLMs and other AI/ML models. Teams and enterprises use KitOps as a secure storage throughout the AI/ML project lifecycle.
Use KitOps to speed up and de-risk all types of AI/ML projects:
Predictive models Large language models Computer vision models Multi-modal models Audio models etc...
Dockerize.it KitOpsBased on our record, KitOps seems to be more popular. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Ready to start versioning your prompts? Download KitOps and package your first ModelKit in minutes. - Source: dev.to / about 1 month ago
And there you have it: 10 Open-source AI/ML platform engineering tools. Whether you are building scalable pipelines, tracking experiments, or deploying models in production, tools like KitOps can tackle the complexities of machine learning projects and model development while keeping your workflow efficient, user-friendly, and robust. - Source: dev.to / 5 months ago
In machine learning (ML) projects, transitioning from experimentation to production deployment presents numerous challenges, including fragmented workflows, inconsistent processes, and scaling difficulties. These obstacles often result in project delays and increased operational costs. Effectively integrating MLOps tools with cloud platforms can address these issues by creating more coherent development processes,... - Source: dev.to / 5 months ago
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 / 6 months ago
Seems like https://kitops.org/ but fewer features. - Source: Hacker News / 6 months ago
ChatGPT - ChatGPT is a powerful, open-source language model.
Kubernetes - Kubernetes is an open source orchestration system for Docker containers
Midjourney - Midjourney lets you create images (paintings, digital art, logos and much more) simply by writing a prompt.
Prometheus - An open-source systems monitoring and alerting toolkit.
Jasper.ai - The Future of Writing Meet Jasper, your AI sidekick who creates amazing content fast!
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
