The Jozu Blog

Step-by-step guide for DevOps engineers: add ML model packaging, versioning, and deployment to your existing CI/CD pipeline using KitOps. Open source.

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.

Advance your career with Jozu Learning Center, a free resource for software developers who want to learn about ML.

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.

This post explores multiple tools to help you secure your AI project through model attestation and Software Bill of Materials (SBOMs)

Discover 10 underrated open source MLOps projects to boost your machine learning workflows.