# Results for "low code machine learning dashboards"

> Search results for `low code machine learning dashboards` on awesome-repositories.com. 103 total matches; showing the first 50.

Explore on the web: https://awesome-repositories.com/q/low-code-machine-learning-dashboards

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## Results

- [luwill/machine_learning_code_implementation](https://awesome-repositories.com/repository/luwill-machine-learning-code-implementation.md) (1,549 ⭐) — This repository provides a collection of machine learning algorithms implemented from scratch using pure Python. It serves as an educational resource designed to demonstrate the internal logic and mathematical foundations of predictive models without relying on external machine learning frameworks or black-box libraries.

The project distinguishes itself by mapping code implementations directly to their underlying statistical and calculus-based formulas. Each model is constructed using base language primitives and manual gradient descent optimization, allowing users to observe the mechanics of
- [patchy631/machine-learning](https://awesome-repositories.com/repository/patchy631-machine-learning.md) (1,540 ⭐) — This repository serves as an educational collection of interactive notebooks and code examples designed to demonstrate fundamental machine learning and deep learning concepts. It provides a structured environment for exploring data science workflows, ranging from basic numerical computing and statistical analysis to the construction of complex neural network architectures.

The project distinguishes itself through a focus on hands-on experimentation, offering practical implementations for tasks such as computer vision, natural language processing, and statistical simulation. Users can engage w
- [instillai/machine-learning-course](https://awesome-repositories.com/repository/instillai-machine-learning-course.md) (7,043 ⭐) — This is a comprehensive educational curriculum designed to teach machine learning fundamentals using the Python programming language. It provides a structured course covering the implementation and theory of supervised learning, unsupervised learning, and deep learning.

The curriculum is delivered through interactive notebooks that combine executable code with technical tutorials. It includes dedicated guides for building neural network architectures, implementing classification and regression models, and utilizing clustering techniques for pattern discovery in unlabeled data.

The materials
- [udacity/machine-learning](https://awesome-repositories.com/repository/udacity-machine-learning.md) (4,027 ⭐) — This project is a machine learning curriculum and data science educational resource. It provides a structured set of instructional materials and hands-on projects designed for learning machine learning concepts and the implementation of predictive models.

The resource functions as a training guide for supervised learning, focusing on the development of models for image classification and digit recognition. It uses a project-based training approach that pairs theoretical lessons with dataset-driven model training and evaluation.

The curriculum covers the mathematical foundations of machine le
- [rasbt/machine-learning-book](https://awesome-repositories.com/repository/rasbt-machine-learning-book.md) (5,239 ⭐) — This project is a comprehensive machine learning educational resource and tutorial series delivered as a collection of interactive Jupyter Notebooks. It provides practical Python implementations for the end-to-end machine learning lifecycle, covering supervised and unsupervised learning, deep learning, and reinforcement learning.

The resource distinguishes itself by providing detailed implementation guides for complex architectures, including transformers, generative adversarial networks, and convolutional neural networks. It also features specialized courseware for developing reinforcement l
- [machinelearningmindset/machine-learning-course](https://awesome-repositories.com/repository/machinelearningmindset-machine-learning-course.md) (7,043 ⭐) — This project is a comprehensive educational curriculum for learning data science and predictive modeling using the Python programming language. It provides structured instructional material and guides covering supervised learning, unsupervised learning, and neural network design.

The curriculum focuses on building, training, and evaluating machine learning models. It includes specific guides for implementing linear regression, decision trees, and support vector machines for predictive analysis, as well as tutorials on designing convolutional and recurrent neural network architectures.

The co
- [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
- [trekhleb/homemade-machine-learning](https://awesome-repositories.com/repository/trekhleb-homemade-machine-learning.md) (24,608 ⭐) — This project provides a collection of machine learning algorithms implemented from scratch in Python. It serves as an educational resource using interactive notebooks that combine code with mathematical explanations to demonstrate the first principles of data science.

The repository includes reference implementations for neural networks, such as multilayer perceptrons with backpropagation, and supervised learning models including linear and logistic regression. It also covers unsupervised learning through k-means clustering and Gaussian anomaly detection.

The codebase covers a broad range of
- [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
- [mlflow/mlflow](https://awesome-repositories.com/repository/mlflow-mlflow.md) (26,554 ⭐)
- [zenitysec/awesome-low-code](https://awesome-repositories.com/repository/zenitysec-awesome-low-code.md) (445 ⭐) — Awesome Low Code platforms, vendors, tools and resources
- [paddlepaddle/visualdl](https://awesome-repositories.com/repository/paddlepaddle-visualdl.md) (4,882 ⭐) — VisualDL is a deep learning visualization toolkit and experiment tracking dashboard. It provides a web-based interface for monitoring training metrics, analyzing high-dimensional data, and rendering model architectures through static and dynamic graphs.

The toolkit serves as a performance profiler to identify execution bottlenecks and optimize resource usage. It also functions as a data analyzer that uses projection algorithms to identify relationships between points in complex datasets.

Capabilities include tracking training metrics via scalars and histograms, comparing multiple experiments
- [maiot-io/zenml](https://awesome-repositories.com/repository/maiot-io-zenml.md) (5,452 ⭐) — ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments.

The project distinguishes itself
- [rasbt/python-machine-learning-book](https://awesome-repositories.com/repository/rasbt-python-machine-learning-book.md) (12,614 ⭐) — This project is an educational resource providing practical code examples and implementations of machine learning algorithms using the Python language. It serves as a guide for constructing predictive pipelines, clustering models, and dimensionality reduction within the Scikit-Learn ecosystem.

The repository includes comprehensive demonstrations for supervised and unsupervised learning, as well as detailed examples for implementing neural networks and deep architectures. It also provides practical guidance on exporting model parameters to JSON and wrapping trained models in web APIs for produ
- [ethen8181/machine-learning](https://awesome-repositories.com/repository/ethen8181-machine-learning.md) (3,445 ⭐) — :earth_americas: machine learning tutorials (mainly in Python3)
- [polyaxon/polyaxon](https://awesome-repositories.com/repository/polyaxon-polyaxon.md) (3,707 ⭐) — Polyaxon is a Kubernetes-native machine learning orchestration platform and MLOps pipeline orchestrator. It serves as a control plane for managing distributed deep learning workloads, automated machine learning pipelines, and experiment tracking.

The platform distinguishes itself through specialized services for distributed training management, including MPI-based coordination for PyTorch and TensorFlow. It provides an automated hyperparameter optimization service utilizing Bayesian, random, and grid search algorithms, alongside managed interactive AI workspaces for launching Jupyter notebook
- [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
- [jwarmenhoven/coursera-machine-learning](https://awesome-repositories.com/repository/jwarmenhoven-coursera-machine-learning.md) (859 ⭐) — This repository serves as an educational collection of Python implementations for fundamental machine learning algorithms and statistical models. It provides a structured environment for learning core concepts through interactive computational documents that combine live code, narrative text, and data visualizations.

The codebase focuses on predictive modeling development, offering instructional examples for building and evaluating regression, classification, and neural network models. It utilizes standardized data science library interfaces to demonstrate how to implement and execute these a
- [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
- [ujjwalkarn/machine-learning-tutorials](https://awesome-repositories.com/repository/ujjwalkarn-machine-learning-tutorials.md) (17,909 ⭐) — This repository serves as a structured educational resource for machine learning and data science, providing a centralized collection of tutorials, lecture notes, and implementation guides. It is designed to support self-directed learning by organizing complex technical concepts into a clear, hierarchical path that spans from foundational statistical methods to advanced deep learning architectures.

The project distinguishes itself through a comprehensive approach to skill development, bridging the gap between theoretical algorithmic foundations and functional software applications. It offers
- [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)
- [ostris/ai-toolkit](https://awesome-repositories.com/repository/ostris-ai-toolkit.md) (9,509 ⭐) — ai-toolkit is a diffusion model training toolkit designed for fine-tuning image and video generation models. It functions as a containerized model trainer and GPU training job manager, providing the infrastructure to orchestrate dependencies and manage training processes on remote GPU hardware.

The system utilizes low-rank adaptation techniques, including LoRA and LoKr weight optimization, to reduce the hardware requirements for model training. It distinguishes itself through a web-based training controller that allows for the monitoring and modification of hyperparameters, secured by token-b
- [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
- [chatanyteam/chatany](https://awesome-repositories.com/repository/chatanyteam-chatany.md) (6,505 ⭐) — ChatAny is a multimodal AI dashboard and large language model aggregator that provides a unified interface for accessing multiple AI services. It functions as a centralized hub for generating text, images, music, and video through the integration of various artificial intelligence models.

The platform includes a SaaS management system to control service access via subscription packages, redemption codes, and referral rewards. It also features a dedicated tool for extracting text from PDF documents to enable conversational queries and analysis.

The system supports image generation and editing
- [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
- [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
- [rasbt/python-machine-learning-book-2nd-edition](https://awesome-repositories.com/repository/rasbt-python-machine-learning-book-2nd-edition.md) (7,194 ⭐) — This project is a machine learning educational resource and implementation guide for Python. It provides a collection of executable code and notebooks that demonstrate predictive modeling, data analysis workflows, and the implementation of various machine learning algorithms.

The repository features practical examples of classification, regression, and clustering tasks using Scikit-Learn, alongside tutorials for building and training deep learning architectures with TensorFlow. These include implementations of convolutional and recurrent networks.

The content covers a broad range of capabili
- [79e/chatgpt-web](https://awesome-repositories.com/repository/79e-chatgpt-web.md) (1,366 ⭐) — ChatGpt-Web is a web-based application designed to provide a responsive interface for interacting with large language models. It functions as a centralized dashboard that enables users to exchange text prompts with generative AI services while managing conversation history and system resources through a modular, component-based architecture.

The platform distinguishes itself by incorporating a backend proxy layer that routes client requests to external artificial intelligence providers. This infrastructure allows for the masking of sensitive API keys and the redirection of network traffic to
- [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
- [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
- [jeffreyksmithjr/reactive-machine-learning-systems](https://awesome-repositories.com/repository/jeffreyksmithjr-reactive-machine-learning-systems.md) (145 ⭐) — Code from the book Machine Learning Systems.
- [oobabooga/text-generation-webui](https://awesome-repositories.com/repository/oobabooga-text-generation-webui.md) (47,323 ⭐) — This project is a comprehensive platform for hosting and interacting with large language models directly on local hardware. It provides a web-based graphical interface that allows users to manage model loading, configure generation parameters, and execute text or chat interactions entirely offline. By running models locally, the software ensures complete data privacy and eliminates reliance on external cloud services for generative tasks.

Beyond basic inference, the platform functions as a versatile workbench for generative AI development. It includes an integrated pipeline for fine-tuning mo
- [timzhang642/3d-machine-learning](https://awesome-repositories.com/repository/timzhang642-3d-machine-learning.md) (10,176 ⭐) — A resource repository for 3D machine learning
- [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
- [ajaymache/machine-learning-yearning](https://awesome-repositories.com/repository/ajaymache-machine-learning-yearning.md) (1,135 ⭐) — Machine Learning Yearning book by  🅰️𝓷𝓭𝓻𝓮𝔀 🆖
- [automatic1111/stable-diffusion-webui](https://awesome-repositories.com/repository/automatic1111-stable-diffusion-webui.md) (163,743 ⭐) — Stable Diffusion Web UI is a browser-based interface designed for managing text-to-image generation tasks. It provides a centralized dashboard for controlling generative processes, including native support for multi-stage model architectures to facilitate high-quality image refinement.

The platform distinguishes itself through granular control over the generation process, offering tools for precise parameter management and advanced prompt engineering. Users can customize generation styles and capabilities by integrating external model-extension formats, such as textual inversions, low-rank ad
- [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
- [josephmisiti/machine-learning-module](https://awesome-repositories.com/repository/josephmisiti-machine-learning-module.md) (477 ⭐) — the best machine learning tutorials on the web
- [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,
- [open-edge-platform/anomalib](https://awesome-repositories.com/repository/open-edge-platform-anomalib.md) (5,871 ⭐) — Anomalib is a PyTorch-based library for visual anomaly detection, offering a modular framework, a comprehensive model zoo, and a benchmarking suite designed for industrial defect detection. It provides a wide range of algorithms—including generative, discriminative, teacher-student, and vision-language approaches—that support unsupervised, few-shot, and zero-shot settings.

The library enables deployment through model export to ONNX and OpenVINO for edge devices, and includes a no-code web application for training and inference. It also features a command-line interface for orchestrating multi
- [awslabs/machine-learning-samples](https://awesome-repositories.com/repository/awslabs-machine-learning-samples.md) (881 ⭐) — Sample applications built using AWS' Amazon Machine Learning.
- [sahith02/machine-learning-algorithms](https://awesome-repositories.com/repository/sahith02-machine-learning-algorithms.md) (376 ⭐) — A curated list of all machine learning algorithms and deep learning algorithms grouped by category.
- [susanli2016/machine-learning-with-python](https://awesome-repositories.com/repository/susanli2016-machine-learning-with-python.md) (4,583 ⭐) — This project is a Python machine learning library and data science toolkit designed for building predictive models and analyzing complex datasets. It provides a collection of implementations for common supervised and unsupervised algorithms using the Scikit-Learn framework.

The toolkit includes a predictive modeling suite for generating predictions from historical data and a statistical analysis framework for applying Bayesian modeling and causality tests. It also features a data visualization suite based on Matplotlib for rendering static charts and graphs to interpret classifier boundaries
- [swanhubx/swanlab](https://awesome-repositories.com/repository/swanhubx-swanlab.md) (4,005 ⭐) — SwanLab is an open-source machine learning experiment tracking platform and observability tool. It provides a centralized dashboard for logging training metrics, hyperparameters, and hardware performance to monitor and analyze AI model training runs.

The platform is distinguished by its focus on self-hosted infrastructure, allowing users to deploy private instances via Docker or Kubernetes for secure on-premises data control. It also includes specialized utilities for migrating historical experiment logs and synchronizing real-time metrics from external tools like MLflow.

The system covers a
- [facebookresearch/fairseq](https://awesome-repositories.com/repository/facebookresearch-fairseq.md) (32,228 ⭐) — Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic speech recognition, and large-scale language model training. It provides a framework for processing and aligning diverse data sources, including text, audio, and video, to support tasks such as speech-to-text conversion and multimodal sequence learning.

The project is distinguished by its distributed training capabilities, which utilize parameter sharding, mixed-precision training, and CPU offloading to handle models that exceed single-device memory. It also includes specializ
- [1094401996/machine-learning-coursera](https://awesome-repositories.com/repository/1094401996-machine-learning-coursera.md) (95 ⭐) — Lecture notes and assignments for coursera machine learning class
- [metrofun/machine-learning-surveys](https://awesome-repositories.com/repository/metrofun-machine-learning-surveys.md) (1,391 ⭐) — A curated list of Machine Learning Surveys, Tutorials and Books.
- [mrdbourke/zero-to-mastery-ml](https://awesome-repositories.com/repository/mrdbourke-zero-to-mastery-ml.md) (5,839 ⭐) — This project is a machine learning educational curriculum and learning platform delivered through interactive Jupyter Notebooks. It serves as a comprehensive guide for mastering the Python data science toolkit, providing structured tutorials for numerical computing, tabular data manipulation, and statistical visualization.

The curriculum includes specific implementation guides for Scikit-Learn and a practical course on TensorFlow for constructing, training, and deploying neural networks and computer vision models. It covers the end-to-end process of building predictive models, from initial pr
- [microsoft/generative-ai-for-beginners](https://awesome-repositories.com/repository/microsoft-generative-ai-for-beginners.md) (112,045 ⭐) — This project is a comprehensive, open-source educational curriculum designed to guide developers through the mastery of generative artificial intelligence. It provides a structured learning path that covers foundational concepts, prompt engineering, and the practical application of large language models. The repository serves as a central hub for skill acquisition, offering sequential modules that progress from basic model mechanics to advanced architectural patterns.

The curriculum distinguishes itself by focusing on the end-to-end lifecycle of intelligent software, including the implementat
- [firmai/financial-machine-learning](https://awesome-repositories.com/repository/firmai-financial-machine-learning.md) (8,647 ⭐) — A curated list of practical financial machine learning tools and applications.