Dernière mise à jour :
How to evaluate toolchain reliability
Beyond popularity, prioritize release cadence, maintainer spread, and issue response speed to estimate real operational reliability.
Classement toolchains
Chaînes d’évaluation et de livraison (données, entraînement, éval, release).
Source: GitHub Search API
Metrics: stars_normalized_within_result_set
Update cadence: daily
Fetched at: 2026-05-23T13:51:52.988Z
Data version: v20260523T135152Z
Taille des données: 20
Method: webx-ranking-v2
Recherche de dépôts GitHub (`topic:mlops`) : l’ordre suit les étoiles du jeu de résultats de l’API ; le score reflète cet ordre.
| Rang | Toolchain / suite | Mainteneur | Couverture | Score | Notes |
|---|---|---|---|---|---|
| 1 | Made-With-ML | GokuMohandas | Jupyter Notebook · ⭐47,746 | 99.8 | Learn how to develop, deploy and iterate on production-grade ML applications. |
| 2 | airflow | apache | Python · ⭐45,503 | 98 | Apache Airflow - A platform to programmatically author, schedule, and monitor workflows |
| 3 | qdrant | qdrant | Rust · ⭐31,519 | 87 | Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdran |
| 4 | label-studio | HumanSignal | TypeScript · ⭐27,383 | 83.7 | Label Studio is a multi-type data labeling and annotation tool with standardized output format |
| 5 | mlflow | mlflow | Python · ⭐26,072 | 82.6 | The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production- |
| 6 | serve | jina-ai | Python · ⭐21,869 | 79.3 | ☁️ Build multimodal AI applications with cloud-native stack |
| 7 | awesome-production-machine-learning | EthicalML | — · ⭐20,548 | 78.3 | A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning |
| 8 | agents-towards-production | NirDiamant | Jupyter Notebook · ⭐20,393 | 78.1 | End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment. |
| 9 | taipy | Avaiga | Python · ⭐19,220 | 77.2 | Turns Data and AI algorithms into production-ready web applications in no time. |
| 10 | ml-engineering | stas00 | Python · ⭐17,968 | 76.2 | Machine Learning Engineering Open Book |
| 11 | agent-lightning | 🇺🇸 microsoft | Python · ⭐17,211 | 75.6 | The absolute trainer to light up AI agents. |
| 12 | argo-workflows | argoproj | Go · ⭐16,698 | 75.2 | Workflow Engine for Kubernetes |
| 13 | weaviate | weaviate | Go · ⭐16,228 | 74.8 | Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with th |
| 14 | dagster | dagster-io | Python · ⭐15,557 | 74.3 | An orchestration platform for the development, production, and observation of data assets. |
| 15 | mlops-zoomcamp | DataTalksClub | Jupyter Notebook · ⭐14,649 | 73.6 | Free MLOps course from DataTalks.Club |
| 16 | nni | 🇺🇸 microsoft | Python · ⭐14,357 | 73.4 | An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-pa |
| 17 | awesome-mlops | visenger | — · ⭐13,913 | 73 | A curated list of references for MLOps |
| 18 | OpenLLM | bentoml | Python · ⭐12,330 | 71.8 | Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud. |
| 19 | great_expectations | great-expectations | Python · ⭐11,524 | 71.1 | Always know what to expect from your data. |
| 20 | tensorzero | tensorzero | Rust · ⭐11,392 | 71 | TensorZero is an open-source LLMOps platform that unifies an LLM gateway, observability, evaluation, optimization, and experimentation. |