mlops
There are 2129 repositories under mlops topic.
GokuMohandas/Made-With-ML
Learn how to design, develop, deploy and iterate on production-grade ML applications.
apache/airflow
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
jina-ai/jina
☁️ Build multimodal AI applications with cloud-native stack
vllm-project/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
qdrant/qdrant
Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
HumanSignal/label-studio
Label Studio is a multi-type data labeling and annotation tool with standardized output format
EthicalML/awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
argoproj/argo-workflows
Workflow Engine for Kubernetes
microsoft/nni
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
visenger/awesome-mlops
A curated list of references for MLOps
dagster-io/dagster
An orchestration platform for the development, production, and observation of data assets.
stas00/ml-engineering
Machine Learning Engineering Open Book
weaviate/weaviate
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
aws/amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
great-expectations/great_expectations
Always know what to expect from your data.
DataTalksClub/mlops-zoomcamp
Free MLOps course from DataTalks.Club
kedro-org/kedro
Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
bentoml/OpenLLM
Run any open-source LLMs, such as Llama 2, Mistral, as OpenAI compatible API endpoint in the cloud.
Avaiga/taipy
Turns Data and AI algorithms into production-ready web applications in no time.
chiphuyen/machine-learning-systems-design
A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"
wandb/wandb
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
activeloopai/deeplake
Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
Netflix/metaflow
:rocket: Build and manage real-life ML, AI, and data science projects with ease!
bentoml/BentoML
The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
allegroai/clearml
ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
feast-dev/feast
The Open Source Feature Store for Machine Learning
flyteorg/flyte
Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
aimhubio/aim
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
evidentlyai/evidently
Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
SkalskiP/courses
This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI)
SuperDuperDB/superduperdb
🔮 SuperDuperDB: Bring AI to your database! Build, deploy and manage any AI application directly with your existing data infrastructure, without moving your data. Including streaming inference, scalable model training and vector search.
SeldonIO/seldon-core
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
FedML-AI/FedML
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, TensorOpera AI (https://TensorOpera.ai) is your generative AI platform at scale.
pytorch/serve
Serve, optimize and scale PyTorch models in production
ashleve/lightning-hydra-template
PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡
zenml-io/zenml
ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.