8 open-source projects similar to quant-aq/aeromancy, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best Aeromancy alternative.
PyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It functions as a low-code environment that leverages a scikit-learn native engine to execute preprocessing, training, and evaluation for tabular data. The platform distinguishes itself as an LLM-powered ML copilot, using large language model agents to analyze datasets, design experiment configurations, and explain model results. It also serves as a Kubernetes ML orchestrator and model registry, enabling the versioning of trained pipelines and their promotion to production API endp
Examples of Machine Learning code using Comet.ml
Sacred is an experiment management tool and reproducibility framework designed to organize multiple runs of a process with different configurations. It functions as a machine learning experiment tracker and hyperparameter configuration manager, logging hyperparameters, metrics, and metadata to a database to ensure that experimental executions remain trackable. The project focuses on scientific result reproducibility by automatically managing random seeds and tracking system dependencies. It allows for the execution of experiment variants through command-line parameter overrides and dynamic pa
Lightweight and modular MLOps library targeted at small teams or individuals
Open Source ML Model Versioning, Metadata, and Experiment Management
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
This project is a collection of utilities designed for machine learning experiment tracking, data versioning, and the observability of large language model applications. It provides a client for recording hyperparameters and metrics during training to visualize performance trends and compare different model versions. The tool includes a model evaluation framework that uses custom scorers and automated judges to assess the quality of generated text outputs. It also provides observability tools to monitor and debug the execution flow and runtime behavior of language model applications. The sys