# No-Code Automated Machine Learning Platforms

> Search results for `automated machine learning to build models without code` on awesome-repositories.com. 116 total matches; showing the first 50.

Explore on the web: https://awesome-repositories.com/q/automated-machine-learning-to-build-models-without-code

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## Results

- [aladdinpersson/machine-learning-collection](https://awesome-repositories.com/repository/aladdinpersson-machine-learning-collection.md) (8,465 ⭐) — This project is a machine learning educational repository providing a collection of implementations and guides for machine learning and deep learning algorithms. It serves as a deep learning model library and a reference for training workflows, covering foundational machine learning, convolutional, recurrent, and transformer architectures.

The collection includes a generative adversarial network suite for synthesizing realistic images and performing image-to-image translation. It also functions as a computer vision implementation guide for object detection and semantic segmentation, alongside
- [jack-cherish/machine-learning](https://awesome-repositories.com/repository/jack-cherish-machine-learning.md) (10,333 ⭐) — This project is a collection of supervised and unsupervised machine learning algorithms implemented from scratch using Python. It serves as an educational resource for studying model training, parameter optimization, and the implementation of core predictive models.

The library provides a variety of supervised learning tools, including linear and logistic regression, decision trees, and support vector machines. It also features unsupervised learning capabilities for discovering patterns in unlabeled datasets through clustering algorithms.

Broad capability areas include ensemble learning thro
- [josephmisiti/awesome-machine-learning](https://awesome-repositories.com/repository/josephmisiti-awesome-machine-learning.md) (72,867 ⭐) — This project is a comprehensive, community-driven directory of machine learning resources, software libraries, and educational materials. It serves as a centralized knowledge base for developers and researchers, organizing tools and frameworks by their primary programming language and technical domain to simplify discovery across the artificial intelligence ecosystem.

The collection distinguishes itself by providing a cross-language development index that spans diverse programming environments, including C, C++, Rust, Clojure, and Python. It covers a wide range of specialized capabilities, fr
- [jakevdp/pythondatasciencehandbook](https://awesome-repositories.com/repository/jakevdp-pythondatasciencehandbook.md) (48,561 ⭐) — This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping.

The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st
- [trainindata/deploying-machine-learning-models](https://awesome-repositories.com/repository/trainindata-deploying-machine-learning-models.md) (895 ⭐) — Accompanying repo for the online course Deployment of Machine Learning Models.
- [humansignal/label-studio](https://awesome-repositories.com/repository/humansignal-label-studio.md) (27,619 ⭐) — Label Studio is a multi-modal data annotation platform designed to create and manage high-quality training datasets for machine learning. It functions as a self-hosted, containerized environment that supports secure, private deployments, including air-gapped configurations. The platform provides a centralized workspace for labeling diverse media types, such as images, text, audio, and time-series data, to support supervised and reinforcement learning workflows.

The platform distinguishes itself through deep integration with machine learning backends, enabling active learning loops, automated
- [unslothai/unsloth](https://awesome-repositories.com/repository/unslothai-unsloth.md) (66,628 ⭐) — Unsloth is a high-performance training and inference platform designed to optimize the lifecycle of large language and multimodal models. It provides a comprehensive engine for fine-tuning, executing, and managing models locally, with a focus on reducing memory consumption and increasing compute speed on consumer-grade hardware.

The platform distinguishes itself through hand-optimized kernels and automated computational graph techniques that maximize hardware throughput. It supports advanced training methodologies, including reinforcement learning for reasoning and efficient adapter-based fin
- [hiyouga/llama-efficient-tuning](https://awesome-repositories.com/repository/hiyouga-llama-efficient-tuning.md) (72,239 ⭐) — This project is a fine-tuning framework and training pipeline designed to optimize and adapt large language and vision models. It provides a specialized toolkit for parameter-efficient tuning and supervised learning, serving as both a trainer for multimodal models and a deployment tool for serving fine-tuned models via high-performance inference engines.

The framework focuses on reducing memory and compute requirements by updating a small subset of model parameters. It supports a wide range of adaptation strategies, including vision-language model training to align text, image, video, and aud
- [rodekruis/automated-building-detection](https://awesome-repositories.com/repository/rodekruis-automated-building-detection.md) (103 ⭐) — Automated Building Detection using Deep Learning: a NLRC/510 tool
- [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
- [avelino/awesome-go](https://awesome-repositories.com/repository/avelino-awesome-go.md) (175,576 ⭐) — This project serves as a comprehensive language ecosystem index, functioning as a centralized, community-curated directory for the Go programming language. It organizes a vast landscape of software components, libraries, and development tools into a structured, navigable hierarchy, enabling developers to efficiently discover resources tailored to specific functional domains.

The repository distinguishes itself through a decentralized contribution model, where community-driven updates ensure the index remains current with the rapidly evolving software landscape. Beyond simple resource listing,
- [ethen8181/machine-learning](https://awesome-repositories.com/repository/ethen8181-machine-learning.md) (3,445 ⭐) — :earth_americas: machine learning tutorials (mainly in Python3)
- [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
- [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
- [shunliz/machine-learning](https://awesome-repositories.com/repository/shunliz-machine-learning.md) (1,424 ⭐) — 机器学习原理笔记整理. Gitbook地址https://shunliz.gitbooks.io/machine-learning/content/ 前半部分关注数学基础，机器学习和深度学习的理论部分，详尽的公式推导。 后半部分关注工程实践和理论应用部分
- [jeff1evesque/machine-learning](https://awesome-repositories.com/repository/jeff1evesque-machine-learning.md) (258 ⭐) — Web-interface + rest API for classification and regression (https://jeff1evesque.github.io/machine-learning.docs)
- [ivy-llc/ivy](https://awesome-repositories.com/repository/ivy-llc-ivy.md) (14,176 ⭐) — Ivy is a machine learning framework transpiler and model converter designed to translate code and computational graphs between different deep learning ecosystems. It serves as a portability tool for migrating model architectures and logic across competing frameworks to enable flexible deployment.

The system achieves cross-framework conversion by utilizing abstract syntax tree analysis to rewrite source code and by employing a computational graph tracer to capture tensor flows and operation sequences during live execution. This process allows for the translation of both high-level model defini
- [dformoso/machine-learning-mindmap](https://awesome-repositories.com/repository/dformoso-machine-learning-mindmap.md) (6,254 ⭐) — A Mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.
- [ludwig-ai/ludwig](https://awesome-repositories.com/repository/ludwig-ai-ludwig.md) (11,717 ⭐) — Ludwig is a multimodal machine learning platform and low-code framework designed for building, training, and deploying neural networks. It enables the construction of models that process text, images, audio, and tabular data through a unified interface using declarative configuration files rather than custom code.

The system features a specialized low-code framework for large language models, supporting supervised fine-tuning, preference alignment, and a constrained decoding tool to force structured data output via logit extraction. It also includes an automated model architecture search to i
- [ashishpatel26/500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code](https://awesome-repositories.com/repository/ashishpatel26-500-ai-machine-learning-deep-learning-computer-vision-nlp-projects.md) (34,579 ⭐) — This repository serves as a comprehensive, curated collection of open-source implementations focused on artificial intelligence, machine learning, and computer vision. It functions as a centralized knowledge base and technical resource index, providing students and professional engineers with a structured directory of code examples for educational and practical reference.

The project distinguishes itself through a community-driven curation model, relying on manual updates and contributions to maintain a relevant and expansive archive. By organizing these resources into categorized lists, the
- [pycaret/pycaret](https://awesome-repositories.com/repository/pycaret-pycaret.md) (9,811 ⭐) — PyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It functions as a low-code environment that leverages a scikit-learn native engine to execute preprocessing, training, and evaluation for tabular data.

The platform distinguishes itself as an LLM-powered ML copilot, using large language model agents to analyze datasets, design experiment configurations, and explain model results. It also serves as a Kubernetes ML orchestrator and model registry, enabling the versioning of trained pipelines and their promotion to production API endp
- [trekhleb/machine-learning-octave](https://awesome-repositories.com/repository/trekhleb-machine-learning-octave.md) (895 ⭐) — 🤖 MatLab/Octave examples of popular machine learning algorithms with code examples and mathematics being explained
- [jeffreyksmithjr/reactive-machine-learning-systems](https://awesome-repositories.com/repository/jeffreyksmithjr-reactive-machine-learning-systems.md) (145 ⭐) — Code from the book Machine Learning Systems.
- [aws/aws-cdk](https://awesome-repositories.com/repository/aws-aws-cdk.md) (12,817 ⭐) — The AWS Cloud Development Kit is an infrastructure-as-code framework that enables developers to define and provision cloud resources using familiar programming languages. By utilizing construct-based synthesis, it translates high-level, object-oriented code into declarative templates, allowing for the automated management of complex cloud environments through a centralized, code-driven control plane.

The framework distinguishes itself through its ability to model infrastructure as a dependency-aware resource graph, ensuring that components are provisioned and updated in the correct order. It
- [freqtrade/freqtrade](https://awesome-repositories.com/repository/freqtrade-freqtrade.md) (51,527 ⭐) — This project is an algorithmic trading engine designed for the automated execution of cryptocurrency strategies. It provides a modular execution core that connects to multiple centralized and decentralized exchanges, allowing users to deploy rule-based trading logic across various spot and futures markets. The platform serves as a comprehensive environment for the entire trading lifecycle, from initial strategy development to live market operations.

What distinguishes this platform is its integrated suite for quantitative analysis and predictive modeling. It features a robust backtesting engi
- [oumi-ai/oumi](https://awesome-repositories.com/repository/oumi-ai-oumi.md) (8,858 ⭐) — Oumi is a comprehensive large language model development platform designed for synthesizing data, fine-tuning models, and running performance evaluations. It serves as a unified environment for the entire model lifecycle, encompassing a training and fine-tuning suite, an evaluation framework, and tools for synthetic data generation and model distillation.

The platform is distinguished by its iterative, failure-driven synthesis approach, which analyzes model weaknesses during evaluation to generate targeted training data. It utilizes an LLM-based judge framework to programmatically score respo
- [susanli2016/machine-learning-with-python](https://awesome-repositories.com/repository/susanli2016-machine-learning-with-python.md) (4,583 ⭐) — Python codes for common Machine Learning Algorithms
- [arize-ai/phoenix](https://awesome-repositories.com/repository/arize-ai-phoenix.md) (8,605 ⭐) — Arize Phoenix is an LLM observability platform and evaluation framework designed to capture execution traces and monitor large language model applications. It serves as a prompt management system for versioning and testing templates, and as a self-hosted AI operations infrastructure for managing telemetry and experiments.

The platform differentiates itself through a specialized embedding visualization tool used to detect data drift and optimize vector search. It provides a comprehensive evaluation suite that utilizes judge-based evaluators and ground-truth datasets to score model outputs, and
- [trekhleb/machine-learning-experiments](https://awesome-repositories.com/repository/trekhleb-machine-learning-experiments.md) (1,814 ⭐) — This is a collection of interactive machine-learning experiments. Each experiment consists of 🏋️ Jupyter/Colab notebook (to see how a model was trained) and 🎨 demo page (to see a model in action right in your browser).
- [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
- [tensorzero/tensorzero](https://awesome-repositories.com/repository/tensorzero-tensorzero.md) (10,985 ⭐) — TensorZero is an inference gateway and experimentation framework designed to manage the lifecycle of large language models in production environments. It functions as a central proxy that routes requests across multiple artificial intelligence providers while providing the infrastructure necessary to monitor performance, track costs, and ensure service reliability.

The platform distinguishes itself by integrating a comprehensive evaluation engine and an observability pipeline directly into the request flow. It enables developers to conduct controlled experiments and A/B tests to compare diffe
- [machinelearningmindset/machine-learning-course](https://awesome-repositories.com/repository/machinelearningmindset-machine-learning-course.md) (7,043 ⭐) — ################################################### A Machine Learning Course with Python ###################################################
- [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
- [instillai/machine-learning-course](https://awesome-repositories.com/repository/instillai-machine-learning-course.md) (7,043 ⭐) — ################################################### A Machine Learning Course with Python ###################################################
- [letianzj/quantresearch](https://awesome-repositories.com/repository/letianzj-quantresearch.md) (2,808 ⭐) — QuantResearch is a quantitative research framework and specialized toolkit for algorithmic simulation, financial time-series analysis, and systematic trading. It provides an event-driven backtesting environment for validating strategies against historical tick and bar data, alongside a dedicated portfolio optimization engine for calculating asset weights and risk metrics.

The project distinguishes itself through a machine learning finance toolkit that implements recurrent neural networks for price prediction and reinforcement learning for derivative pricing. It also features advanced statisti
- [lm-sys/fastchat](https://awesome-repositories.com/repository/lm-sys-fastchat.md) (39,472 ⭐) — FastChat is a training and serving platform for large language models that provides an integrated toolkit for fine-tuning, hosting, and benchmarking chatbots. It functions as an inference server capable of hosting multiple models and exposing them via a standardized API for chat applications.

The platform distinguishes itself through a distributed model controller that manages worker nodes and routes requests across a hardware-agnostic inference layer supporting various accelerators. It includes a dedicated evaluation framework for assessing model quality using automated judges, multi-turn di
- [ajaymache/machine-learning-yearning](https://awesome-repositories.com/repository/ajaymache-machine-learning-yearning.md) (1,135 ⭐) — Machine Learning Yearning book by  🅰️𝓷𝓭𝓻𝓮𝔀 🆖
- [vibrantlabsai/ragas](https://awesome-repositories.com/repository/vibrantlabsai-ragas.md) (12,659 ⭐) — Ragas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and autonomous agent workflows. It provides a comprehensive suite of tools for benchmarking system outputs, utilizing language models as automated judges to score performance against defined rubrics and reference data. By standardizing inputs, retrieved contexts, and generated responses into a unified schema, the project enables consistent analysis across complex AI applications.

The framework distinguishes itself through its ability to generate synthetic test datasets from existin
- [bytebytegohq/system-design-101](https://awesome-repositories.com/repository/bytebytegohq-system-design-101.md) (83,491 ⭐) — This project is a centralized engineering knowledge repository that provides a structured curriculum for mastering system design, architectural patterns, and fundamental software development workflows. It serves as a professional development resource for engineers, offering foundational knowledge and real-world case studies to support the design of scalable, secure, and efficient distributed systems.

The repository distinguishes itself through a visual-first approach to knowledge synthesis, distilling complex technical concepts into high-density graphical diagrams and succinct illustrations.
- [harvard-edge/cs249r_book](https://awesome-repositories.com/repository/harvard-edge-cs249r-book.md) (20,217 ⭐) — This project is a comprehensive educational framework designed to teach the design, deployment, and performance optimization of machine learning systems. It provides a structured curriculum that covers the full stack of artificial intelligence engineering, ranging from the construction of core framework components like tensors and automatic differentiation engines to the orchestration of large-scale distributed training clusters.

The platform distinguishes itself through its integration of physics-grounded systems modeling and interactive simulation environments. Users can experiment with dis
- [timzhang642/3d-machine-learning](https://awesome-repositories.com/repository/timzhang642-3d-machine-learning.md) (10,176 ⭐) — A resource repository for 3D machine learning
- [chiphuyen/aie-book](https://awesome-repositories.com/repository/chiphuyen-aie-book.md) (13,779 ⭐) — This project serves as a comprehensive educational resource and technical handbook for engineers building applications powered by large language models. It provides a structured framework for mastering the principles of artificial intelligence engineering, covering the full lifecycle of model development from initial design to production deployment.

The repository distinguishes itself by offering a deep dive into the practical implementation of advanced design patterns, including retrieval-augmented generation, agentic tool orchestration, and parameter-efficient model adaptation. It emphasize
- [google-research/google-research](https://awesome-repositories.com/repository/google-research-google-research.md) (38,139 ⭐) — This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development.

The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed
- [awslabs/machine-learning-samples](https://awesome-repositories.com/repository/awslabs-machine-learning-samples.md) (881 ⭐) — Sample applications built using AWS' Amazon Machine Learning.
- [rlinf/rlinf](https://awesome-repositories.com/repository/rlinf-rlinf.md) (2,502 ⭐) — RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the infrastructure to train vision-language-action models and robotic policies using a combination of reinforcement learning and supervised fine-tuning.

The system is designed for scaling workloads across GPU clusters, managing the placement of actors, rollout workers, and environment components. It features a specialized robotics data collection pipeline for gathering teleoperated demonstrations and simulation trajectories into standardized replay buffers, alongside a hardware interface
- [amitshekhariitbhu/fast-android-networking](https://awesome-repositories.com/repository/amitshekhariitbhu-fast-android-networking.md) (5,906 ⭐) — 🚀 A Complete Fast Android Networking Library that also supports HTTP/2 🚀
- [josephmisiti/machine-learning-module](https://awesome-repositories.com/repository/josephmisiti-machine-learning-module.md) (477 ⭐) — the best machine learning tutorials on the web
- [mlflow/mlflow](https://awesome-repositories.com/repository/mlflow-mlflow.md) (26,554 ⭐)
- [amitshekhariitbhu/androidnetworking](https://awesome-repositories.com/repository/amitshekhariitbhu-androidnetworking.md) (5,906 ⭐) — AndroidNetworking is an HTTP networking library for Android that handles the full lifecycle of network communication, from sending requests to parsing responses and caching data. It provides a unified interface for executing GET, POST, PUT, DELETE, HEAD, and PATCH requests, with support for both synchronous and asynchronous execution, and includes built-in JSON response parsing that converts server responses directly into Java objects or lists.

The library distinguishes itself through a set of integrated capabilities that go beyond basic request execution. It manages file downloads and upload
- [1094401996/machine-learning-coursera](https://awesome-repositories.com/repository/1094401996-machine-learning-coursera.md) (95 ⭐) — Lecture notes and assignments for coursera machine learning class