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Awesome-LLMOps

An awesome & curated list of best LLMOps tools for developers
https://github.com/steventkrawczyk/Awesome-LLMOps

Last synced: 4 days ago
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  • Large Language Model

    • Alpaca - lab/stanford_alpaca.svg?style=social) - Code and documentation to train Stanford's Alpaca models, and generate the data.
    • BELLE - A 7B Large Language Model fine-tune by 34B Chinese Character Corpus, based on LLaMA and Alpaca.
    • Bloom - workshop/model_card.svg?style=social) - BigScience Large Open-science Open-access Multilingual Language Model
    • dolly - Databricks’ Dolly, a large language model trained on the Databricks Machine Learning Platform
    • FastChat (Vicuna) - sys/FastChat.svg?style=social) - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and FastChat-T5.
    • GLM-6B (ChatGLM) - 6B.svg?style=social) - An Open Bilingual Pre-Trained Model, quantization of ChatGLM-130B, can run on consumer-level GPUs.
    • GLM-130B (ChatGLM) - 130B.svg?style=social) - An Open Bilingual Pre-Trained Model (ICLR 2023)
    • GPT-NeoX - neox.svg?style=social) - An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.
    • Luotuo - Chinese-LLM.svg?style=social) - A Chinese LLM, Based on LLaMA and fine tune by Stanford Alpaca, Alpaca LoRA, Japanese-Alpaca-LoRA.
    • StableLM - AI/StableLM.svg?style=social) - StableLM: Stability AI Language Models
    • Falcon 40B - Falcon-40B-Instruct is a 40B parameters causal decoder-only model built by TII based on Falcon-40B and finetuned on a mixture of Baize. It is made available under the Apache 2.0 license.

  • CV Foundation Model

    • disco-diffusion - diffusion.svg?style=social) - A frankensteinian amalgamation of notebooks, models and techniques for the generation of AI Art and Animations.
    • segment-anything (SAM) - anything.svg?style=social) - produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image.
    • stable-diffusion - diffusion.svg?style=social) - A latent text-to-image diffusion model
    • stable-diffusion v2 - AI/stablediffusion.svg?style=social) - High-Resolution Image Synthesis with Latent Diffusion Models
    • midjourney - Midjourney is an independent research lab exploring new mediums of thought and expanding the imaginative powers of the human species.

  • Audio Foundation Model

    • bark - ai/bark.svg?style=social) - Bark is a transformer-based text-to-audio model created by Suno. Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects.
    • whisper - Robust Speech Recognition via Large-Scale Weak Supervision

  • Large Model Serving

    • DeepSpeed-MII - MII.svg?style=social) - MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.
    • Flowise - Drag & drop UI to build your customized LLM flow using LangchainJS.
    • llama.cpp - Port of Facebook's LLaMA model in C/C++
    • Modelz-LLM - llm.svg?style=social) - OpenAI compatible API for LLMs and embeddings (LLaMA, Vicuna, ChatGLM and many others)
    • vllm - project/vllm.svg?style=social) - A high-throughput and memory-efficient inference and serving engine for LLMs.
    • whisper.cpp - Port of OpenAI's Whisper model in C/C++
    • x-stable-diffusion - stable-diffusion.svg?style=social) - Real-time inference for Stable Diffusion - 0.88s latency. Covers AITemplate, nvFuser, TensorRT, FlashAttention.
    • Alpaca-LoRA-Serve - diver/Alpaca-LoRA-Serve.svg?style=social) - Alpaca-LoRA as Chatbot service

  • Frameworks/Servers for Serving

    • BentoML - The Unified Model Serving Framework
    • Mosec - A machine learning model serving framework with dynamic batching and pipelined stages, provides an easy-to-use Python interface.
    • TFServing - A flexible, high-performance serving system for machine learning models.
    • Torchserve - Serve, optimize and scale PyTorch models in production
    • Triton Server (TRTIS) - inference-server/server.svg?style=social) - The Triton Inference Server provides an optimized cloud and edge inferencing solution.
    • langchain-serve - ai/langchain-serve.svg?style=social) - Serverless LLM apps on Production with Jina AI Cloud
    • lanarky - FastAPI framework to build production-grade LLM applications

  • Observability

    • Deepchecks - Tests for Continuous Validation of ML Models & Data. Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort.
    • Evidently - Evaluate and monitor ML models from validation to production.
    • Great Expectations - expectations/great_expectations.svg?style=social) - Always know what to expect from your data.
    • whylogs - The open standard for data logging
    • agenta - AI/agenta.svg?style=social) - The LLMOps platform to build robust LLM apps. Easily experiment and evaluate different prompts, models, and workflows to build robust apps.
    • Arize-Phoenix - ai/phoenix.svg?style=social) - ML observability for LLMs, vision, language, and tabular models.
    • deeplake - Stream large multimodal datasets to achieve near 100% GPU utilization. Query, visualize, & version control data. Access data w/o the need to recompute the embeddings for the model finetuning.
    • GPTCache - Creating semantic cache to store responses from LLM queries.
    • Haystack - ai/haystack.svg?style=social) - Quickly compose applications with LLM Agents, semantic search, question-answering and more.
    • LangKit - Out-of-the-box LLM telemetry collection library that extracts features and profiles prompts, responses and metadata about how your LLM is performing over time to find problems at scale.
    • prompttools - Open-source tools for testing and experimenting with prompts. The core idea is to enable developers to evaluate prompts using familiar interfaces like code and notebooks. In just a few lines of codes, you can test your prompts and parameters across different models (whether you are using OpenAI, Anthropic, or LLaMA models). You can even evaluate the retrieval accuracy of vector databases.
    • xTuring - Build and control your personal LLMs with fast and efficient fine-tuning.
    • Dify - Open-source framework aims to enable developers (and even non-developers) to quickly build useful applications based on large language models, ensuring they are visual, operable, and improvable.
    • promptfoo - Open-source tool for testing & evaluating prompt quality. Create test cases, automatically check output quality and catch regressions, and reduce evaluation cost.
    • Weights & Biases (Prompts) - A suite of LLMOps tools within the developer-first W&B MLOps platform. Utilize W&B Prompts for visualizing and inspecting LLM execution flow, tracking inputs and outputs, viewing intermediate results, securely managing prompts and LLM chain configurations.
    • TrueFoundry - Deploy LLMOps tools like Vector DBs, Embedding server etc on your own Kubernetes (EKS,AKS,GKE,On-prem) Infra including deploying, Fine-tuning, tracking Prompts and serving Open Source LLM Models with full Data Security and Optimal GPU Management. Train and Launch your LLM Application at Production scale with best Software Engineering practices.
    • langchain - Building applications with LLMs through composability
    • LlamaIndex - Provides a central interface to connect your LLMs with external data.
    • ReliableGPT 💪 - Handle OpenAI Errors (overloaded OpenAI servers, rotated keys, or context window errors) for your production LLM Applications.
    • Chroma - core/chroma.svg?style=social) - the open source embedding database
    • Jina - ai/jina.svg?style=social) - Build multimodal AI services via cloud native technologies · Neural Search · Generative AI · Cloud Native
    • Marqo - ai/marqo.svg?style=social) - Tensor search for humans.
    • Milvus - io/milvus.svg?style=social) - Vector database for scalable similarity search and AI applications.
    • pgvector - Open-source vector similarity search for Postgres.
    • pgvecto.rs - Vector database plugin for Postgres, written in Rust, specifically designed for LLM.
    • Qdrant - Vector Search Engine and Database for the next generation of AI applications. Also available in the cloud
    • txtai - Build AI-powered semantic search applications
    • Vald - A Highly Scalable Distributed Vector Search Engine
    • Vearch - A distributed system for embedding-based vector retrieval
    • CodeGen - CodeGen is an open-source model for program synthesis. Trained on TPU-v4. Competitive with OpenAI Codex.
    • CodeT5 - Open Code LLMs for Code Understanding and Generation.
    • fauxpilot - An open-source alternative to GitHub Copilot server
    • tabby - Self-hosted AI coding assistant. An opensource / on-prem alternative to GitHub Copilot.
    • Weaviate - technologies/weaviate.svg?style=social) - Weaviate is an open source vector search engine that stores both objects and vectors, allowing for combining vector search with structured filtering with the fault-tolerance and scalability of a cloud-native database, all accessible through GraphQL, REST, and various language clients.
    • AquilaDB - Network/AquilaDB.svg?style=social) - An easy to use Neural Search Engine. Index latent vectors along with JSON metadata and do efficient k-NN search.
    • Pinecone - The Pinecone vector database makes it easy to build high-performance vector search applications. Developer-friendly, fully managed, and easily scalable without infrastructure hassles.

  • IDEs and Workspaces

    • code server - server.svg?style=social) - Run VS Code on any machine anywhere and access it in the browser.
    • conda - OS-agnostic, system-level binary package manager and ecosystem.
    • Docker - Moby is an open-source project created by Docker to enable and accelerate software containerization.
    • envd - 🏕️ Reproducible development environment for AI/ML.
    • Jupyter Notebooks - The Jupyter notebook is a web-based notebook environment for interactive computing.
    • Kurtosis - tech/kurtosis.svg?style=social) - A build, packaging, and run system for ephemeral multi-container environments.

  • Foundation Model Fine Tuning

    • alpaca-lora - lora.svg?style=social) - Instruct-tune LLaMA on consumer hardware
    • LMFlow - An Extensible Toolkit for Finetuning and Inference of Large Foundation Models
    • Lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
    • peft - State-of-the-art Parameter-Efficient Fine-Tuning.
    • p-tuning-v2 - tuning-v2.svg?style=social) - An optimized prompt tuning strategy achieving comparable performance to fine-tuning on small/medium-sized models and sequence tagging challenges. [(ACL 2022)](https://arxiv.org/abs/2110.07602)
    • QLoRA - Efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance.

  • Frameworks for Training

    • Accelerate - 🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.
    • Apache MXNet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler.
    • Caffe - A fast open framework for deep learning.
    • ColossalAI - An integrated large-scale model training system with efficient parallelization techniques.
    • DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
    • Horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
    • Kedro - org/kedro.svg?style=social) - Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code.
    • Keras - team/keras.svg?style=social) - Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow.
    • LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
    • MegEngine - MegEngine is a fast, scalable and easy-to-use deep learning framework, with auto-differentiation.
    • metric-learn - learn-contrib/metric-learn.svg?style=social) - Metric Learning Algorithms in Python.
    • MindSpore - ai/mindspore.svg?style=social) - MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
    • Oneflow - Inc/oneflow.svg?style=social) - OneFlow is a performance-centered and open-source deep learning framework.
    • PaddlePaddle - Machine Learning Framework from Industrial Practice.
    • PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration.
    • XGBoost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library.
    • scikit-learn - learn/scikit-learn.svg?style=social) - Machine Learning in Python.
    • TensorFlow - An Open Source Machine Learning Framework for Everyone.
    • VectorFlow - A minimalist neural network library optimized for sparse data and single machine environments.
    • PyTorchLightning - lightning.svg?style=social) - The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
    • Jax - Autograd and XLA for high-performance machine learning research.

  • Experiment Tracking

    • Aim - an easy-to-use and performant open-source experiment tracker.
    • Guild AI - Experiment tracking, ML developer tools.
    • Kedro-Viz - org/kedro-viz.svg?style=social) - Kedro-Viz is an interactive development tool for building data science pipelines with Kedro. Kedro-Viz also allows users to view and compare different runs in the Kedro project.
    • LabNotebook - LabNotebook is a tool that allows you to flexibly monitor, record, save, and query all your machine learning experiments.
    • Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments.

  • ML Platforms

    • MLRun - An open MLOps platform for quickly building and managing continuous ML applications across their lifecycle.
    • OpenLLM - An open platform for operating large language models (LLMs) in production. Fine-tune, serve, deploy, and monitor any LLMs with ease.
    • MLflow - Open source platform for the machine learning lifecycle.
    • ModelFox - ModelFox is a platform for managing and deploying machine learning models.
    • Kserve - Standardized Serverless ML Inference Platform on Kubernetes
    • Kubeflow - Machine Learning Toolkit for Kubernetes.
    • Polyaxon - Machine Learning Management & Orchestration Platform.
    • Primehub - An effortless infrastructure for machine learning built on the top of Kubernetes.
    • Seldon-core - core.svg?style=social) - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
    • ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management.
    • Weights & Biases - A lightweight and flexible platform for machine learning experiment tracking, dataset versioning, and model management, enhancing collaboration and streamlining MLOps workflows. W&B excels at tracking LLM-powered applications, featuring W&B Prompts for LLM execution flow visualization, input and output monitoring, and secure management of prompts and LLM chain configurations.

  • Visualization

    • Maniford - A model-agnostic visual debugging tool for machine learning.
    • netron - Visualizer for neural network, deep learning, and machine learning models.
    • TensorBoard - TensorFlow's Visualization Toolkit.
    • TensorSpace - team/tensorspace.svg?style=social) - Neural network 3D visualization framework, build interactive and intuitive model in browsers, support pre-trained deep learning models from TensorFlow, Keras, TensorFlow.js.
    • dtreeviz - A python library for decision tree visualization and model interpretation.
    • Zetane Viewer - ML models and internal tensors 3D visualizer.
    • Zeno - ml/zeno.svg?style=social) - AI evaluation platform for interactively exploring data and model outputs.
    • OpenOps - Bring multiple data streams into one dashboard.

  • Data Management

    • ArtiVC - A version control system to manage large files.
    • Dolt - Git for Data.
    • Delta-Lake - io/delta.svg?style=social) - Storage layer that brings scalable, ACID transactions to Apache Spark and other engines.
    • Pachyderm - Pachyderm is a version control system for data.
    • Quilt - A self-organizing data hub for S3.

  • Model Management

    • dvc - Data Version Control | Git for Data & Models | ML Experiments Management
    • ModelDB - Open Source ML Model Versioning, Metadata, and Experiment Management
    • MLEM - A tool to package, serve, and deploy any ML model on any platform.
    • ormb - Docker for Your ML/DL Models Based on OCI Artifacts

  • Data Storage

    • JuiceFS - A distributed POSIX file system built on top of Redis and S3.
    • LakeFS - Git-like capabilities for your object storage.

  • Data Tracking

    • Piperider - A CLI tool that allows you to build data profiles and write assertion tests for easily evaluating and tracking your data's reliability over time.
    • LUX - org/lux.svg?style=social) - A Python library that facilitates fast and easy data exploration by automating the visualization and data analysis process.

  • Feature Engineering

    • Featureform - The Virtual Feature Store. Turn your existing data infrastructure into a feature store.

  • Data/Feature enrichment

    • Upgini - Free automated data & feature enrichment library for machine learning: automatically searches through thousands of ready-to-use features from public and community shared data sources and enriches your training dataset with only the accuracy improving features
    • Feast - dev/feast.svg?style=social) - An open source feature store for machine learning.

  • Workflow

    • Argo Workflows - workflows.svg?style=social) - Workflow engine for Kubernetes.
    • Flyte - Kubernetes-native workflow automation platform for complex, mission-critical data and ML processes at scale.
    • Hamilton - inc/hamilton.svg?style=social) - A lightweight framework to represent ML/language model pipelines as a series of python functions.
    • Kubeflow Pipelines - Machine Learning Pipelines for Kubeflow.
    • Metaflow - Build and manage real-life data science projects with ease!
    • Ploomber - The fastest way to build data pipelines. Develop iteratively, deploy anywhere.
    • Prefect - The easiest way to automate your data.
    • aqueduct - An Open-Source Platform for Production Data Science
    • LangFlow - ai/langflow.svg?style=social) - An effortless way to experiment and prototype LangChain flows with drag-and-drop components and a chat interface.
    • ZenML - io/zenml.svg?style=social) - MLOps framework to create reproducible pipelines.
    • VDP - ai/vdp.svg?style=social) - An open-source unstructured data ETL tool to streamline the end-to-end unstructured data processing pipeline.
    • Airflow - A platform to programmatically author, schedule and monitor workflows.

  • Scheduling

    • Kueue - sigs/kueue.svg?style=social) - Kubernetes-native Job Queueing.
    • Slurm - A Highly Scalable Workload Manager.
    • Volcano - sh/volcano.svg?style=social) - A Cloud Native Batch System (Project under CNCF).
    • Yunikorn - core.svg?style=social) - Light-weight, universal resource scheduler for container orchestrator systems.
    • PAI - Resource scheduling and cluster management for AI (Open-sourced by Microsoft).

  • ML Compiler

    • ONNX-MLIR - mlir.svg?style=social) - Compiler technology to transform a valid Open Neural Network Exchange (ONNX) graph into code that implements the graph with minimum runtime support.
    • TVM - Open deep learning compiler stack for cpu, gpu and specialized accelerators

  • Profiling

    • octoml-profile - profile.svg?style=social) - octoml-profile is a python library and cloud service designed to provide the simplest experience for assessing and optimizing the performance of PyTorch models on cloud hardware with state-of-the-art ML acceleration technology.
    • scalene - umass/scalene.svg?style=social) - a high-performance, high-precision CPU, GPU, and memory profiler for Python
    • Archai - a platform for Neural Network Search (NAS) that allows you to generate efficient deep networks for your applications.
    • autoai - A framework to find the best performing AI/ML model for any AI problem.
    • AutoGL - An autoML framework & toolkit for machine learning on graphs
    • automl-gs - gs.svg?style=social) - Provide an input CSV and a target field to predict, generate a model + code to run it.
    • autokeras - team/autokeras.svg?style=social) - AutoML library for deep learning.
    • Auto-PyTorch - PyTorch.svg?style=social) - Automatic architecture search and hyperparameter optimization for PyTorch.
    • auto-sklearn - sklearn.svg?style=social) - an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
    • Dragonfly - An open source python library for scalable Bayesian optimisation.
    • Determined - ai/determined.svg?style=social) - scalable deep learning training platform with integrated hyperparameter tuning support; includes Hyperband, PBT, and other search methods.
    • DEvol (DeepEvolution) - a basic proof of concept for genetic architecture search in Keras.
    • EvalML - An open source python library for AutoML.
    • FLAML - Fast and lightweight AutoML ([paper](https://www.microsoft.com/en-us/research/publication/flaml-a-fast-and-lightweight-automl-library/)).
    • Goptuna - bata/goptuna.svg?style=social) - A hyperparameter optimization framework, inspired by Optuna.
    • HpBandSter - a framework for distributed hyperparameter optimization.
    • Hyperband - open source code for tuning hyperparams with Hyperband.
    • Hypernets - A General Automated Machine Learning Framework.
    • Hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python.
    • hyperunity - A toolset for black-box hyperparameter optimisation.
    • Katib - Katib is a Kubernetes-native project for automated machine learning (AutoML).
    • Keras Tuner - team/keras-tuner.svg?style=social) - Hyperparameter tuning for humans.
    • learn2learn - PyTorch Meta-learning Framework for Researchers.
    • MOE - a global, black box optimization engine for real world metric optimization by Yelp.
    • Model Search - a framework that implements AutoML algorithms for model architecture search at scale.
    • NASGym - env.svg?style=social) - a proof-of-concept OpenAI Gym environment for Neural Architecture Search (NAS).
    • NNI - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
    • Optuna - A hyperparameter optimization framework.
    • Pycaret - An open-source, low-code machine learning library in Python that automates machine learning workflows.
    • REMBO - Bayesian optimization in high-dimensions via random embedding.
    • RoBO - a Robust Bayesian Optimization framework.
    • scikit-optimize(skopt) - optimize/scikit-optimize.svg?style=social) - Sequential model-based optimization with a `scipy.optimize` interface.
    • Spearmint - a software package to perform Bayesian optimization.
    • Torchmeta - meta.svg?style=social) - A Meta-Learning library for PyTorch.
    • Vegas - noah/vega.svg?style=social) - an AutoML algorithm tool chain by Huawei Noah's Arb Lab.
    • FeatherCNN - FeatherCNN is a high performance inference engine for convolutional neural networks.
    • Forward - A library for high performance deep learning inference on NVIDIA GPUs.
    • NCNN - ncnn is a high-performance neural network inference framework optimized for the mobile platform.
    • PocketFlow - use AutoML to do model compression.
    • TensorFlow Model Optimization - optimization.svg?style=social) - A suite of tools that users, both novice and advanced, can use to optimize machine learning models for deployment and execution.
    • TNN - A uniform deep learning inference framework for mobile, desktop and server.
    • EasyFL - AI/EasyFL.svg?style=social) - An Easy-to-use Federated Learning Platform
    • FATE - An Industrial Grade Federated Learning Framework
    • FedML - AI/FedML.svg?style=social) - The federated learning and analytics library enabling secure and collaborative machine learning on decentralized data anywhere at any scale. Supporting large-scale cross-silo federated learning, cross-device federated learning on smartphones/IoTs, and research simulation.
    • Flower - A Friendly Federated Learning Framework
    • Harmonia - Harmonia is an open-source project aiming at developing systems/infrastructures and libraries to ease the adoption of federated learning (abbreviated to FL) for researches and production usage.
    • Awesome AutoDL - X-Y/Awesome-AutoDL.svg?style=social) - Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis)
    • Awesome AutoML - AutoML.svg?style=social) - Curating a list of AutoML-related research, tools, projects and other resources
    • Awesome AutoML Papers - automl-papers.svg?style=social) - A curated list of automated machine learning papers, articles, tutorials, slides and projects
    • Awesome Federated Learning - Federated-Learning.svg?style=social) - A curated list of federated learning publications, re-organized from Arxiv (mostly)
    • awesome-federated-learning - federated-learning.svg?style=social) - All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.
    • Awesome Open MLOps - open-mlops.svg?style=social) - This is the Fuzzy Labs guide to the universe of free and open source MLOps tools.
    • Awesome Production Machine Learning - production-machine-learning.svg?style=social) - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
    • Awesome Tensor Compilers - tensor-compilers.svg?style=social) - A list of awesome compiler projects and papers for tensor computation and deep learning.
    • kelvins/awesome-mlops - mlops.svg?style=social) - A curated list of awesome MLOps tools.
    • visenger/awesome-mlops - mlops.svg?style=social) - An awesome list of references for MLOps - Machine Learning Operations
    • currentslab/awesome-vector-search - vector-search.svg?style=social) - A curated list of awesome vector search framework/engine, library, cloud service and research papers to vector similarity search.
    • TensorFlow Federated - A framework for implementing federated learning
    • AutoGluon - AutoML for Image, Text, and Tabular Data.
    • FEDOT - itmo/FEDOT.svg?style=social) - AutoML framework for the design of composite pipelines.
    • HPOlib2 - a library for hyperparameter optimization and black box optimization benchmarks.
    • Awesome Argo - argo.svg?style=social) - A curated list of awesome projects and resources related to Argo
    • TPOT - one of the very first AutoML methods and open-source software packages.
    • Awesome Federated Learning Systems - paper.svg?style=social) - A curated list of Federated Learning Systems related academic papers, articles, tutorials, slides and projects.
Programming Languages
Python 103 Jupyter Notebook 23 Go 22 C++ 18 JavaScript 6 TypeScript 6 Rust 5 Shell 2 C 2 Java 2
Categories
Profiling 64 Frameworks for Training 21 Observability 19 Vector search 17 Workflow 12 Large Language Model 11 ML Platforms 11 Visualization 8 Large Model Serving 8 Frameworks/Servers for Serving 7 IDEs and Workspaces 6 Foundation Model Fine Tuning 6 Data Management 5 Experiment Tracking 5 CV Foundation Model 5 Scheduling 5 Model Management 4 ML Compiler 2 Data Tracking 2 Audio Foundation Model 2 Data/Feature enrichment 2 Data Storage 2 Feature Engineering 1
Sub Categories
Keywords
machine-learning 96 python 55 deep-learning 53 mlops 41 data-science 39 pytorch 34 tensorflow 26 automl 24 llm 24 ai 23 kubernetes 22 ml 21 hyperparameter-optimization 17 keras 14 neural-architecture-search 12 llmops 12 gpu 11 large-language-models 11 vector-search 10 golang 10 inference 10 neural-network 10 scikit-learn 9 hyperparameter-tuning 9 data-engineering 9 docker 9 gpt 8 vector-database 8 chatgpt 8 automated-machine-learning 8 workflow 8 developer-tools 7 model-serving 7 go 7 language-model 7 artificial-intelligence 7 jupyter 7 rag 7 nearest-neighbor-search 7 federated-learning 6 distributed-systems 6 visualization 6 bayesian-optimization 6 distributed 6 pipeline 6 generative-ai 6 data-quality 6 llama 6 cloud-native 6 llms 5