# ML frameworks and MLOps

> Search results for `ML frameworks and MLOps` on awesome-repositories.com. 114 total matches; showing the first 50.

Explore on the web: https://awesome-repositories.com/q/ml-frameworks-and-mlops

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## Results

- [tensorflow/tensorflow](https://awesome-repositories.com/repository/tensorflow-tensorflow.md) (195,697 ⭐) — TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The system provides high-level interfaces for defining neural network architectures, alongside a robust engine for managing multidimensional array structures and tensor mathematics.

The framework distinguishes itself through a scalable distributed runtime that orchestrates workloads acr
- [ten-framework/ten-framework](https://awesome-repositories.com/repository/ten-framework-ten-framework.md) (10,701 ⭐) — Ten Framework is a multimodal large language model agent framework designed for building low-latency conversational agents. It integrates voice, text, and visual inputs in real time to facilitate human interaction.

The project includes a real-time speech processing pipeline for streaming transcription, voice activity detection, and speaker diarization. It also features an avatar synchronization engine that coordinates character lip animations and visual outputs with synthesized speech.

The framework covers edge AI deployment through containerized packaging and direct integration with embedde
- [angel-ml/angel](https://awesome-repositories.com/repository/angel-ml-angel.md) (6,783 ⭐) — Angel is a distributed machine learning framework and graph computation engine designed to train predictive models and execute algorithms across a cluster of servers. It functions as a distributed parameter server that synchronizes model weights and gradients across multiple machines to handle massive datasets.

The system provides a production environment for model inference deployment to provide real-time predictions for end users. It integrates with Spark to run machine learning workflows and data processing pipelines through a compatible interface.

The framework covers distributed graph c
- [gokumohandas/made-with-ml](https://awesome-repositories.com/repository/gokumohandas-made-with-ml.md) (48,343 ⭐) — Made-With-ML is an automated documentation generator and developer experience platform designed to transform source code into structured, searchable reference websites. It functions as a codebase intelligence tool that parses implementation details to provide clear explanations of logic and data requirements.

The system distinguishes itself by leveraging language-level type annotations and structured code comments to generate interface specifications. By utilizing static analysis to extract metadata, it automates the transformation of docstrings into web-ready documentation, ensuring that tec
- [visenger/awesome-mlops](https://awesome-repositories.com/repository/visenger-awesome-mlops.md) (13,942 ⭐) — An awesome list of references for MLOps - Machine Learning Operations :pointright: ml-ops.org*
- [tracel-ai/burn](https://awesome-repositories.com/repository/tracel-ai-burn.md) (15,474 ⭐) — Burn is a deep learning framework designed for building, training, and deploying neural networks using a modular architecture. As a machine learning library built in Rust, it provides a backend-agnostic computational engine that enables the execution of models across diverse hardware, including central processors, graphics processors, and web runtimes.

The framework distinguishes itself through a highly portable design that allows developers to maintain a single workflow for both training and inference across heterogeneous environments. It incorporates advanced optimization techniques such as
- [kamranahmedse/developer-roadmap](https://awesome-repositories.com/repository/kamranahmedse-developer-roadmap.md) (357,434 ⭐) — Developer Roadmap is a community-driven platform that provides structured, graph-based learning paths for software engineering. It serves as a comprehensive knowledge repository where technical domains are organized into visual sequences to guide professional skill acquisition and career growth.

The project distinguishes itself through a collaborative ecosystem that enables users to contribute roadmaps, curate industry best practices, and maintain professional profiles. It integrates diagnostic assessment frameworks to evaluate technical proficiency, helping developers identify knowledge gaps
- [rapidai/rapidocr](https://awesome-repositories.com/repository/rapidai-rapidocr.md) (5,968 ⭐) — RapidOCR is an offline deep-learning OCR engine that detects and recognizes text in images using ONNX Runtime, operating entirely without an internet connection. It provides a unified inference pipeline that runs across multiple platforms including Windows, Linux, macOS, Android, and Raspberry Pi, with programming language bindings for Python, C++, Java, and C#.

The engine separates text detection and recognition into independent modules that can be swapped or fine-tuned individually, and abstracts the inference backend behind a unified interface allowing seamless switching between ONNX Runti
- [datatalksclub/mlops-zoomcamp](https://awesome-repositories.com/repository/datatalksclub-mlops-zoomcamp.md) (14,858 ⭐) — Free MLOps course from DataTalks.Club
- [keras-team/keras](https://awesome-repositories.com/repository/keras-team-keras.md) (64,094 ⭐) — Keras is a high-level deep learning framework designed for constructing and training neural networks through the composition of modular, functional layers. It serves as a comprehensive modeling toolkit that provides standardized procedures for defining, evaluating, and deploying complex architectures. By utilizing a directed acyclic graph approach, the framework allows users to build intricate models with multiple inputs, outputs, and shared layers, ensuring consistent numerical execution through functional state management.

The project distinguishes itself as a multi-backend machine learning
- [graviraja/mlops-basics](https://awesome-repositories.com/repository/graviraja-mlops-basics.md) (8,585 ⭐) — MLOps-Basics is a collection of implementation guides and blueprints for automating the machine learning lifecycle. It provides practical workflows for managing the transition of models from training to production deployment, focusing on the integration of operational tools into the machine learning pipeline.

The project features specific architectural patterns for deploying containerized models using serverless infrastructure and cloud registries. It includes frameworks for tracking large datasets and model artifacts via remote storage, as well as guides for converting models into standardiz
- [h2oai/h2ogpt](https://awesome-repositories.com/repository/h2oai-h2ogpt.md) (12,016 ⭐) — h2oGPT is a self-hosted platform designed for running large language models and executing retrieval-augmented generation workflows locally. It provides a comprehensive web interface that allows users to index private document collections into searchable databases, enabling context-aware question answering and summarization without exposing sensitive data to external services.

The platform distinguishes itself by offering a modular architecture that supports both local model execution and connections to external inference servers. It facilitates the development of autonomous agents capable of
- [mlflow/mlflow](https://awesome-repositories.com/repository/mlflow-mlflow.md) (26,554 ⭐)
- [haifeng-jin/readable-ml-framework](https://awesome-repositories.com/repository/haifeng-jin-readable-ml-framework.md) (0 ⭐) — A machine learning framework with readable source code.
- [nousresearch/hermes-agent](https://awesome-repositories.com/repository/nousresearch-hermes-agent.md) (195,049 ⭐) — Hermes-agent is an autonomous AI agent framework and runtime designed to execute complex tasks and synthesize new skills from execution traces. It includes a provider-agnostic gateway for routing requests across multiple model backends and a serverless runtime that suspends idle agent instances and resumes them on demand across containers and virtual machines.

The project provides a desktop automation toolset that controls native GUI workflows on Linux by querying accessibility APIs and injecting input events. It further distinguishes itself with the ability to generate procedural skills from
- [paulescu/hands-on-train-and-deploy-ml](https://awesome-repositories.com/repository/paulescu-hands-on-train-and-deploy-ml.md) (885 ⭐) — Train and Deploy an ML REST API to predict crypto prices, in 10 steps
- [zsdonghao/tensorlayer](https://awesome-repositories.com/repository/zsdonghao-tensorlayer.md) (7,384 ⭐) — Tensorlayer is a deep learning framework and cross-backend AI library used to construct and execute neural network models. It serves as a scientific neural network toolkit providing customizable layers and architectures designed for research applications in science and engineering.

The library enables multi-backend model execution, allowing the same model code to run across different deep learning frameworks, GPUs, and specialized AI accelerators. It includes a reinforcement learning library that provides both low-level and high-level tools for developing intelligent agents.
- [eugeneyan/applied-ml](https://awesome-repositories.com/repository/eugeneyan-applied-ml.md) (29,783 ⭐) — This project is a comprehensive, curated knowledge base designed to support the development and maintenance of production-grade machine learning systems. It serves as a centralized repository of industry-standard technical literature, engineering case studies, and research papers, providing a structured reference for practitioners navigating the complexities of modern data science and machine learning engineering.

The resource distinguishes itself through a cross-domain approach that bridges the gap between academic research and practical implementation. By synthesizing proven industry archit
- [stas00/ml-engineering](https://awesome-repositories.com/repository/stas00-ml-engineering.md) (18,124 ⭐) — This project is a comprehensive engineering framework and technical reference for managing, scaling, and optimizing distributed machine learning infrastructure. It provides a suite of methodologies and diagnostic tools designed to support large-scale model training and inference on high-performance computing clusters.

The project distinguishes itself through a specialized diagnostic toolkit and infrastructure optimization suite that addresses the complexities of multi-node environments. It enables precise control over cluster resources, including hardware maintenance, network topology configu
- [zama-ai/concrete-ml](https://awesome-repositories.com/repository/zama-ai-concrete-ml.md) (1,437 ⭐) — Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.
- [openvinotoolkit/openvino](https://awesome-repositories.com/repository/openvinotoolkit-openvino.md) (10,414 ⭐) — OpenVINO is an AI inference engine and model serving platform designed to execute optimized deep learning models across CPUs, GPUs, and NPUs through a unified API. It includes a model optimization toolkit for converting, quantizing, and compressing models from various frameworks, alongside a specialized generative AI runtime for large language models.

The project distinguishes itself through a plugin-based hardware acceleration layer that maps neural network operations to vendor-specific drivers. It features advanced execution mechanisms such as continuous batching, speculative decoding, and
- [yulingtianxia/core-ml-sample](https://awesome-repositories.com/repository/yulingtianxia-core-ml-sample.md) (221 ⭐) — A Demo using Vision Framework building on Core ML Framework
- [kubescape/kubescape](https://awesome-repositories.com/repository/kubescape-kubescape.md) (11,489 ⭐) — Kubescape is a Kubernetes security posture management platform designed to scan clusters, manifests, and images for misconfigurations, vulnerabilities, and compliance risks. It functions as a comprehensive security suite incorporating a compliance scanner, a container image vulnerability scanner, an admission controller for policy enforcement, and a runtime security monitor.

The platform distinguishes itself through runtime-aware vulnerability filtering, which maps libraries loaded in memory to determine if vulnerabilities are actually reachable. It also integrates with AI assistants via a Mo
- [microsoft/cntk](https://awesome-repositories.com/repository/microsoft-cntk.md) (17,602 ⭐) — CNTK is a deep learning toolkit used for the design, construction, and training of neural networks. It defines model architectures as computational graphs and optimizes network parameters using an automatic differentiation engine and stochastic gradient descent.

The project emphasizes large scale model distribution, spreading training workloads across multiple hardware nodes and GPUs. It features specialized support for dynamic sequence handling, allowing filters to be convolved across both spatial and dynamic sequence axes to process data of variable lengths.

The toolkit provides hardware-a
- [born-ml/born](https://awesome-repositories.com/repository/born-ml-born.md) (100 ⭐) — Born is a modern ML framework for Go — train and deploy models as single binaries. Pure Go, zero CGO, GPU accelerated.
- [hiyouga/llamafactory](https://awesome-repositories.com/repository/hiyouga-llamafactory.md) (72,213 ⭐) — LlamaFactory is a unified framework for fine-tuning and adapting large language models. It provides a comprehensive platform that standardizes training workflows across diverse machine learning architectures, allowing users to execute both full-tuning and parameter-efficient methods through a single interface.

The project distinguishes itself by offering a low-code visual dashboard that enables users to configure experiments and monitor performance metrics in real time without writing extensive custom scripts. It also features a configuration-driven orchestration system that decouples experim
- [clearml/clearml](https://awesome-repositories.com/repository/clearml-clearml.md) (6,740 ⭐) — ClearML is a comprehensive MLOps platform designed to manage the end-to-end machine learning lifecycle, from initial experimentation to production deployment. It provides a suite of integrated tools including a pipeline orchestrator for automating workflows, an experiment tracking tool for logging hyperparameters and metrics, and a metadata-driven data versioning system for managing large-scale datasets and model artifacts.

The platform is distinguished by its advanced compute management and serving capabilities. It features a GPU compute manager that supports fractional resource slicing and
- [lyhue1991/eat_tensorflow2_in_30_days](https://awesome-repositories.com/repository/lyhue1991-eat-tensorflow2-in-30-days.md) (9,933 ⭐) — This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow. It provides a comprehensive guide for building, training, and deploying neural networks, combining theoretical fundamentals with practical implementation examples.

The repository distinguishes itself by covering the end-to-end machine learning workflow, from low-level tensor mathematics and linear algebra to the creation of complex model architectures. It includes specific guidance on developing data pipelines for diverse data types, such as images, text, and time-series seque
- [allegroai/clearml](https://awesome-repositories.com/repository/allegroai-clearml.md) (6,733 ⭐) — ClearML is a comprehensive MLOps platform designed to manage the entire machine learning lifecycle. It functions as an experiment tracking tool, a data versioning system, and a pipeline orchestrator, while providing infrastructure for GPU cluster management and model serving.

The platform is distinguished by its ability to handle hybrid-cloud compute scheduling and fractional GPU allocation, allowing multiple workloads to share a single hardware accelerator. It employs a metadata-based approach to data versioning, using virtual views to track large datasets and artifacts without duplicating r
- [google/ml-metadata](https://awesome-repositories.com/repository/google-ml-metadata.md) (678 ⭐) — For recording and retrieving metadata associated with ML developer and data scientist workflows.
- [deeplearning-ai/machine-learning-yearning-cn](https://awesome-repositories.com/repository/deeplearning-ai-machine-learning-yearning-cn.md) (7,847 ⭐) — This project is a technical educational resource providing Chinese translations of instructional guidelines focused on machine learning. It functions as a markdown documentation project that delivers translated pedagogical materials regarding the practical application and optimization of AI models.

The repository utilizes git-based collaborative translation to track and manage the localization of English technical content into Chinese. This process involves manual human and technical translation of complex machine learning theory to preserve pedagogical nuance for Chinese-speaking readers.

T
- [stefan-jansen/machine-learning-for-trading](https://awesome-repositories.com/repository/stefan-jansen-machine-learning-for-trading.md) (16,552 ⭐) — This project is a comprehensive framework for engineering financial data pipelines, designed to automate the collection, cleaning, and synchronization of large-scale market datasets. It functions as a quantitative trading data engine, providing the infrastructure necessary to manage historical and real-time asset pricing information for research and machine learning workflows.

The system distinguishes itself through a configuration-driven approach to orchestration, allowing users to manage complex data acquisition tasks across multiple financial providers. It features resilient middleware tha
- [vanilla-framework/vanilla-framework](https://awesome-repositories.com/repository/vanilla-framework-vanilla-framework.md) (972 ⭐) — From community websites to web applications, this CSS framework will help you achieve a consistent look and feel.
- [huggingface/ml-intern](https://awesome-repositories.com/repository/huggingface-ml-intern.md) (10,521 ⭐) — This project is an autonomous AI agent framework and workflow orchestrator designed to automate machine learning engineering. It functions as a reasoning engine that reads research papers and writes code to train and deploy machine learning models through iterative reasoning loops and tool execution.

The system distinguishes itself by integrating a GPU-accelerated sandboxed execution environment, allowing it to run and verify machine learning scripts in isolated remote containers. It utilizes a model provider integration gateway to route inference requests across various hosted or local endpo
- [d2l-ai/d2l-zh](https://awesome-repositories.com/repository/d2l-ai-d2l-zh.md) (78,493 ⭐) — This project is an open-source, interactive educational platform designed to teach deep learning through a comprehensive, code-first curriculum. It provides a structured learning path that covers foundational mathematics, modern neural network architectures, and practical optimization techniques, enabling practitioners to master complex artificial intelligence concepts through hands-on experimentation.

The platform distinguishes itself by integrating technical explanations with executable Jupyter notebooks. This design allows readers to modify code and hyperparameters in real-time, facilitati
- [ajtulloch/haskell-ml](https://awesome-repositories.com/repository/ajtulloch-haskell-ml.md) (60 ⭐) — Haskell implementations of various ML algorithms.
- [qdrant/qdrant](https://awesome-repositories.com/repository/qdrant-qdrant.md) (32,372 ⭐) — Qdrant is a high-performance vector similarity database designed to store, index, and search high-dimensional vectors alongside structured metadata. It functions as a distributed search engine that manages large-scale data clusters, providing low-latency retrieval and complex filtering capabilities. The system is built to serve as a specialized middleware layer, connecting machine learning pipelines and AI agents to persistent storage for intelligent information retrieval and recommendation tasks.

The platform distinguishes itself through advanced retrieval techniques, including support for h
- [pyg-team/pytorch_geometric](https://awesome-repositories.com/repository/pyg-team-pytorch-geometric.md) (23,838 ⭐) — This project is a deep learning library designed for training neural networks on irregular data structures, including graphs, 3D meshes, and point clouds. It functions as an extension to the PyTorch framework, providing specialized layers and kernels that enable the processing of complex, non-Euclidean information.

The library distinguishes itself through a geometric deep learning toolkit that manages the unique requirements of graph-based data. It utilizes sparse matrix-based message passing to aggregate information across nodes and employs dynamic computational graph construction to accommo
- [pmerienne/trident-ml](https://awesome-repositories.com/repository/pmerienne-trident-ml.md) (384 ⭐) — Trident-ML : A realtime online machine learning library
- [flutter-team-archive/plugins](https://awesome-repositories.com/repository/flutter-team-archive-plugins.md) (17,710 ⭐) — This project is a collection of official plugin packages and a native integration library designed to provide a consistent interface for accessing hardware and software functionality across different mobile and desktop platforms. It serves as a native platform bridge, enabling cross-platform applications to invoke native code and manage operating system dependencies.

The project utilizes a federated plugin architecture, splitting plugins into common interfaces and separate platform implementations to allow for independent development and extension. It further supports native integration throu
- [onnx/onnx](https://awesome-repositories.com/repository/onnx-onnx.md) (20,358 ⭐) — ONNX is an open-source standard for machine learning interoperability that provides a unified format for representing neural network models. By defining a common set of operators and a standardized file structure, it enables models to be shared, exported, and executed consistently across different training frameworks and software ecosystems.

The project functions as an intermediate representation layer that decouples model development from deployment. It utilizes a language-neutral binary serialization format to store model structures and weights, ensuring that computational graphs remain por
- [comet-ml/opik](https://awesome-repositories.com/repository/comet-ml-opik.md) (17,787 ⭐) — Opik is an observability and evaluation platform designed for generative AI applications and agentic workflows. It provides a centralized environment for tracing execution flows, managing prompt templates, and monitoring production performance, allowing teams to gain visibility into complex model interactions and tool usage without requiring manual application code changes.

The platform distinguishes itself through its integrated approach to the AI development lifecycle, combining distributed trace instrumentation with automated evaluation frameworks. It supports model-as-a-judge scoring, syn
- [planbrothers/ml-annotate](https://awesome-repositories.com/repository/planbrothers-ml-annotate.md) (109 ⭐) — Use ML-Annotate to label data for machine learning purposes
- [apache/tvm](https://awesome-repositories.com/repository/apache-tvm.md) (13,497 ⭐) — TVM is a machine learning compiler framework designed to convert deep learning models from various frameworks into optimized machine code. It functions as a cross-platform deployment engine that transforms high-level model definitions into efficient, hardware-specific binaries for diverse computing architectures.

The system utilizes a multi-level compilation pipeline that decouples algorithm logic from hardware implementation through tensor-operator abstractions. It employs a graph-level intermediate representation to perform cross-operator optimizations and memory planning before lowering co
- [ageron/handson-ml](https://awesome-repositories.com/repository/ageron-handson-ml.md) (25,608 ⭐) — This is a machine learning educational repository consisting of a collection of notebooks and code examples. It provides practical implementations of diverse machine learning algorithms and workflows, ranging from traditional scientific computing to deep learning.

The project features specific implementations of Scikit-Learn models, such as decision trees, random forests, and support vector machines, as well as TensorFlow examples for building neural networks, convolutional layers, and recurrent architectures. It also includes tutorials on reinforcement learning development and the creation o
- [src-d/ml](https://awesome-repositories.com/repository/src-d-ml.md) (141 ⭐) — sourced.ml is a library and command line tools to build and apply machine learning models on top of Universal Abstract Syntax Trees
- [oneflow-inc/oneflow](https://awesome-repositories.com/repository/oneflow-inc-oneflow.md) (9,400 ⭐) — OneFlow is a deep learning framework and distributed execution engine designed for building, training, and deploying neural network architectures. It functions as a scalable neural network library that allows for the development of deep learning models and their execution across distributed hardware.

The project includes a machine learning graph compiler used to optimize neural network execution graphs. This allows for the acceleration of model performance and the reduction of latency during both training and inference.

The framework covers broad capability areas including large-scale model
- [lift/framework](https://awesome-repositories.com/repository/lift-framework.md) (1,284 ⭐) — Lift Framework
- [zhaochenyang20/awesome-ml-sys-tutorial](https://awesome-repositories.com/repository/zhaochenyang20-awesome-ml-sys-tutorial.md) (5,371 ⭐) — This project provides a comprehensive technical guide and framework for engineering large-scale machine learning systems. It covers the full lifecycle of model development, focusing on the infrastructure and computational principles required to build, train, and serve generative AI models across distributed GPU clusters.

The repository distinguishes itself by offering deep-dive tutorials and implementation strategies for complex system challenges. It emphasizes high-performance architectural primitives, such as collective communication orchestration, distributed tensor sharding, and static gr
- [ddbourgin/numpy-ml](https://awesome-repositories.com/repository/ddbourgin-numpy-ml.md) (16,275 ⭐) — This library is a collection of machine learning algorithms and neural network components implemented from scratch using only NumPy. It serves as an educational toolkit for constructing and experimenting with machine learning architectures, emphasizing a modular approach where algorithms are organized into self-contained, object-oriented classes.

The project distinguishes itself by relying exclusively on array-oriented programming to perform mathematical operations, ensuring that all computations are vectorized for performance. By utilizing a standardized interface for forward and backward pa