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An open source DevOps tool from the CNCF for packaging and versioning AI/ML models, datasets, code, and configuration into an OCI Artifact.
The main features of jozu-ai/kitops are: Experiment and Data Management, CI/CD for Machine Learning.
Open-source alternatives to jozu-ai/kitops include: allegroai/clearml — ClearML is a comprehensive MLOps platform designed to manage the entire machine learning lifecycle. It functions as an… bodaay/huggingfacemodeldownloader — Simple go utility to download HuggingFace Models and Datasets. clearml/clearml — ClearML is a comprehensive MLOps platform designed to manage the end-to-end machine learning lifecycle, from initial… datahub-project/datahub — DataHub is a metadata management platform designed to unify technical, operational, and business context across… dolthub/dolt — Dolt is a relational database engine that integrates version control directly into the database management layer. It… aimhubio/aim — Aim is an open-source platform for logging, visualizing, and comparing machine learning training runs and LLM traces.…
ClearML is a comprehensive MLOps platform designed to manage the entire machine learning lifecycle. It functions as an experiment tracking tool, a data versioning system, and a pipeline orchestrator, while providing infrastructure for GPU cluster management and model serving. The platform is distinguished by its ability to handle hybrid-cloud compute scheduling and fractional GPU allocation, allowing multiple workloads to share a single hardware accelerator. It employs a metadata-based approach to data versioning, using virtual views to track large datasets and artifacts without duplicating r
Simple go utility to download HuggingFace Models and Datasets
ClearML is a comprehensive MLOps platform designed to manage the end-to-end machine learning lifecycle, from initial experimentation to production deployment. It provides a suite of integrated tools including a pipeline orchestrator for automating workflows, an experiment tracking tool for logging hyperparameters and metrics, and a metadata-driven data versioning system for managing large-scale datasets and model artifacts. The platform is distinguished by its advanced compute management and serving capabilities. It features a GPU compute manager that supports fractional resource slicing and
Aim is an open-source platform for logging, visualizing, and comparing machine learning training runs and LLM traces. It provides a remote tracking server and a comparison UI, functioning as an ML experiment tracker, AI workflow logger, and LLM trace recorder that captures prompts, generations, and tool calls from AI applications. The platform distinguishes itself through a run-based data model with local SQLite storage, real-time metric streaming, and a plugin-based explorer system that supports specialized visual analysis of metrics, images, audio, and text. It offers a Python SDK with cont