KitOps

61 posts in KitOps

Turn Your Existing DevOps Pipeline Into an MLOps Pipeline With ModelKits

In today’s world, where almost every company is embracing artificial intelligence (AI) and machine learning (ML) into their software offering, maintaining two separate pipelines for ML-powered software systems and conventional software projects can pose challenges. This also introduces friction within the team, which can slow down the development and deployment process.

KitOps - 12 min read

From Jupyter Notebook to deployed application in 4 steps

Deploy your Jupyter Notebook effortlessly with a ModelKit. Learn the 4-step process from unpacking and fine-tuning to pushing and deploying your ML models.

ai - 7 min read

How to Tune and Deploy Your First Small Language Model (SLM)

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

devops - 12 min read

Tools to ease collaboration between data scientists and application developers

What makes a great workflow between data scientists and ML application developers? This post discusses the disparities, required tools, and, eventually, a solution to poor collaboration.

ai - 14 min read

25 Open Source AI Tools to Cut Your Development Time in Half

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

ai - 9 min read

How to turn a Jupyter Notebook into a deployable artifact

From Jupyter Notebook to production-ready artifact: explore our guide to using KitOps and ModelKit for seamless deployment.