Stand:
How to evaluate toolchain reliability
Beyond popularity, prioritize release cadence, maintainer spread, and issue response speed to estimate real operational reliability.
Toolchain-Ranking
Evaluierungs- und Delivery-Toolchains (Daten, Training, Eval, 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
Datenumfang: 20
Method: webx-ranking-v2
GitHub-Repositorysuche (`topic:mlops`): Reihenfolge folgt den Stars im API-Ergebnis; der Score spiegelt diese Reihenfolge wider.
| Rang | Toolchain / Suite | Maintainer | Abdeckung | Punktzahl | Hinweise |
|---|---|---|---|---|---|
| 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. |