KitOps Project Description
What is the project about?
KitOps is a packaging, versioning, and sharing system specifically designed for AI/ML projects. It aims to standardize and simplify the process of managing and distributing all the components of an AI/ML project, including models, datasets, code, and configurations.
What problem does it solve?
KitOps addresses the challenges of reproducibility, collaboration, and deployment in AI/ML projects. It streamlines handoffs between data scientists, developers, and SREs. It also helps with compliance, such as the EU AI Act, by providing tamper-proof, signable, and auditable model versions. It solves the problem of managing and sharing the various, often disparate, parts of an AI/ML project in a consistent and reliable way.
What are the features of the project?
- Unified Packaging: Packages models, datasets, configurations, and code into a single "ModelKit."
- Versioning: Tags each ModelKit for clear identification of compatible components.
- Tamper-proofing: Uses SHA digests to ensure the integrity of the ModelKit and its artifacts.
- Selective Unpacking: Allows users to extract only the necessary components from a ModelKit.
- Automation: Integrates with CI/CD workflows (GitHub Actions, Dagger).
- Deploy Containers: Generates Docker containers from ModelKits.
- Kubernetes-ready: Generates Kubernetes/KServe deployment configurations.
- LLM Fine-tuning Support: Facilitates fine-tuning of large language models.
- RAG Pipeline Support: Enables the creation of Retrieval-Augmented Generation pipelines.
- Artifact Signing: Provides cryptographic signing for provenance verification.
- Standards-based: Uses OCI 1.1-compliant container registries for storage.
- Simple Syntax: Uses YAML-based Kitfiles for easy configuration.
- Flexible: Allows referencing base models or storing key-value pairs in Kitfiles.
- Run Locally: Dev mode for running and configuring LLMs locally.
- Universal: Works with any AI, ML, or LLM project.
What are the technologies used in the project?
- Go: The primary programming language.
- OCI (Open Container Initiative) Standards: For packaging and distribution.
- YAML: For configuration files (Kitfiles).
- Docker: For containerization.
- Kubernetes/KServe: For deployment.
- GitHub Actions/Dagger: For CI/CD integration.
What are the benefits of the project?
- Improved Reproducibility: Ensures consistent results by packaging all project dependencies.
- Enhanced Collaboration: Facilitates sharing and teamwork among different roles.
- Simplified Deployment: Streamlines the process of deploying AI/ML models to production.
- Increased Security: Provides tamper-proofing and artifact signing.
- Reduced Storage Costs: Allows selective unpacking of only needed components.
- Compliance: Helps meet regulatory requirements (e.g., EU AI Act).
- Streamlined Workflow: Integrates with existing MLOps and DevOps tools.
- Faster Development: Speeds up the AI/ML project lifecycle.
What are the use cases of the project?
- Packaging and sharing predictive models, large language models, computer vision models, multi-modal models, and audio models.
- Creating a library of model versions for compliance purposes.
- Fine-tuning large language models.
- Building and distributing RAG pipelines.
- Managing and deploying AI/ML projects in various environments (local, cloud, on-premises).
- Collaborating on AI/ML projects across teams and organizations.
- Integrating AI/ML models into applications.
- Auditing and tracking AI/ML models.
