milvusmilvus-io/milvus
Milvus is open source vector database for production AI, written in Go and C++, scalable and blazing fast for billions of embedding vectors.
98 libraries, 59 updated this year
Software for the lifecycle around a model rather than the model itself. MLflow, Weights & Biases, Aim and Sacred track experiments and metrics; DVC versions datasets and models alongside code; Optuna and Hyperopt search hyperparameters; and AutoML tools such as AutoGluon, TPOT, PyCaret and H2O try many models for you.
98 libraries
Milvus is open source vector database for production AI, written in Go and C++, scalable and blazing fast for billions of embedding vectors.
Qdrant is open source vector similarity search engine with extended filtering support, written in Rust.
Open-source search and retrieval database for AI applications. Vector, full-text, regex, and metadata search. Self-host or Cloud available.
Platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. Framework and language agnostic, take a look at all the built-in integrations.
Open-source LLM evaluation and red teaming framework. Test prompts, models, agents, and RAG pipelines. Run adversarial attacks (jailbreaks, prompt injection) and integrate security testing into CI/CD.
Weaviate is an open source vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale.
Data Science Version Control is an open-source version control system for machine learning projects with pipelines support. It makes ML projects reproducible and shareable.
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning.
Machine learning experiment tracking, dataset versioning, hyperparameter search, visualization, and collaboration
Kedro is a data and development workflow framework that implements best practices for data pipelines with an eye towards productionizing machine learning models.
AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.
Open-source SDK for running LLMs and multimodal models on-device across iOS, Android, and cross-platform apps.
Tool that automatically creates and optimizes machine learning pipelines using genetic programming. Consider it your personal data science assistant, automating a tedious part of machine learning.
An open-source, low-code machine learning library in Python that automates machine learning workflows.
Production-ready Multi-AI Agents framework with self-reflection. Fastest agent instantiation (3.77μs), 100+ LLM support via LiteLLM, MCP integration, agentic workflows (route/parallel/loop/repeat), built-in memory, Python & JS SDKs.
Fastest unstructured dataset management for TensorFlow/PyTorch. Stream & version-control data. Store even petabyte-scale data in a single numpy-like array on the cloud accessible on any machine. Visit activeloop.ai for more info.
Toolkit for package and deploy machine learning models for serving in production
Interactive reports to analyze machine learning models during validation or production monitoring.
Flyte makes it easy to create concurrent, scalable, and maintainable workflows for machine learning and data processing.
A library for automated feature engineering. It excels at transforming transactional and relational datasets into feature matrices for machine learning using reusable feature engineering "primitives".
ML engine that supports distributed learning on Hadoop, Spark or your laptop via APIs in R, Python, Scala, REST/JSON.
A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving.
Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution.
> An easy-to-use & supercharged open-source AI metadata tracker.
Free, no-signup APIs for text, image, and audio generation with no API keys required. Offers OpenAI-compatible interfaces and React hooks for easy integration.
The AI-native database built for LLM applications, providing incredibly fast vector and full-text search. Developed using C++20
Python tool to help you configure, organize, log and reproduce experiments. Like a notebook lab in the context of Chemistry/Biology. The community has built multiple add-ons leveraging the proposed standard.
A library for doing continuous integration with ML projects. Use GitHub Actions & GitLab CI to train and evaluate models in production like environments and automatically generate visual reports with metrics and graphs in pull/merge requests. Framework & language agnostic.
Validation & testing of machine learning models and data during model development, deployment, and production. This includes checks and suites related to various types of issues, such as model performance, data integrity, distribution mismatches, and more.
All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code).
An Automated Machine Learning (AutoML) python package for tabular data. It can handle: Binary Classification, MultiClass Classification and Regression. It provides explanations and markdown reports.
Scalable deep learning training platform, including integrated support for distributed training, hyperparameter tuning, experiment tracking, and model management.
Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps.
A tool that allows the conversion of ML models into native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart) with zero dependencies.
A lightweight library to define data transformations as a directed-acyclic graph (DAG). It helps author reliable feature engineering and machine learning pipelines, and more.
Open source Kubernetes-style control plane for deploying AI agents as distributed microservices, with built-in service discovery, durable workflows, and observability.
Open source visual data ETL to streamline the end-to-end visual data processing pipeline: extract unstructured visual data from pre-built data sources, transform it into analysable structured insights by Vision AI models imported from various ML platforms, and load the insights into warehouses or applications.
Python library capable of fully capturing the impact of data drift on performance. Allows estimation of post-deployment model performance without access to targets.
AutoViz performs automatic visualization of any dataset with a single line of Python code. Give it any input file (CSV, txt or JSON) of any size and AutoViz will visualize it. See Medium article.
Automated machine learning, data formatting, ensembling, and hyperparameter optimization for competitions and exploration- just give it a.csv file!
A data-intensive platform for AI with the industry's first open-source feature store. The Hopsworks Feature Store provides both a feature warehouse for training and batch based on Apache Hive and a feature serving database, based on MySQL Cluster, for online applications.
A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling.
Open-source CLI security scanner for agentic workflows. Scans your workflow’s source code, detects vulnerabilities, and generates an interactive visualization along with a detailed security report. Supports LangGraph, CrewAI, n8n, OpenAI Agents, and more.
Tool to log, analyze, compare and "optimize" experiments. It's cross-platform and framework independent, and provided integrated visualizers such as tensorboard.
ML powered analytics engine for outlier/anomaly detection and root cause analysis.
Version and deploy your ML models following GitOps principles
An AutoML framework for the automated design of composite modelling pipelines. It can handle classification, regression, and time series forecasting tasks on different types of data (including multi-modal datasets).