1GokuMohandas / Made-With-ML
Learn how to develop, deploy and iterate on production-grade ML applications.
+68 stars
from 49,824 · score 4.23
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2NirDiamant / agents-towards-production
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
+37 stars
from 21,769 · score 3.64
3DataTalksClub / mlops-zoomcamp
Free MLOps course from DataTalks.Club. Register here 👇🏼 to get notified about the next cohort
+33 stars
from 15,482 · score 3.53
4evidentlyai / evidently
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
+19 stars
from 8,010 · score 3.00
5apache / hamilton
Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
+5 stars
from 2,617 · score 1.79
6aws / amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
+3 stars
from 10,998 · score 1.39