How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
Lightweight, Python library for fast and reproducible experimentation :microscope:
The main features of minerva-ml/steppy are: MLOps and Pipelines, Data Science Tooling, Data Science Tools.
Projects with overlapping indexed features include: dslp/dslp — The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and… dslp/dslp-repo-template — Template repository for data science lifecycle project. comet-ml/comet-examples — Examples of Machine Learning code using Comet.ml. comet-ml/comet-llm — Comet LLM is an observability platform and evaluation framework designed for large language model applications and… asavinov/lambdo — Feature engineering and machine learning: together at last! iterative/cml — CML is a pipeline automation tool for training and evaluating machine learning models, functioning as a CI/CD system…
Examples of Machine Learning code using Comet.ml
Comet LLM is an observability platform and evaluation framework designed for large language model applications and agentic workflows. It functions as a system for tracing, monitoring, and debugging execution flows while providing tools for prompt optimization and the enforcement of AI safety guardrails. The platform distinguishes itself through a combination of model-based scoring and heuristic metrics to quantify output quality and detect hallucinations. It includes a dedicated prompt and agent optimizer with an interactive playground for refining templates and tool configurations. For retri
The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo.
