Best AI tools for< Visualize Machine Learning Data >

Weights & Biases is a machine learning platform that helps data scientists and engineers build, train, and deploy machine learning models. It provides a central location to track and manage all of your machine learning projects, and it offers a variety of tools to help you collaborate with others and share your work.

Streamlit is a platform that allows users to easily create web applications for machine learning and data science projects. It provides a simple and intuitive way to build interactive and customizable apps without the need for extensive web development knowledge. With Streamlit, users can visualize data, create dashboards, and share their projects with others seamlessly.

StrawPoll.ai is an AI-powered platform that offers tools for creating polls, generating charts, and utilizing machine learning to analyze data. Users can easily create polls tailored to their needs, share them to collect responses, and analyze the data using built-in chart and machine learning tools. The platform also provides a chart maker tool for visualizing existing data and a machine learning tool for building predictive models by identifying patterns in the data. Additionally, users can access guides for assistance and contact support for any queries.

DVC Studio is a collaboration tool for machine learning teams. It provides seamless data and model management, experiment tracking, visualization, and automation. DVC Studio is built for ML researchers, practitioners, and managers. It enables model organization and discovery across all ML projects and manages model lifecycle with Git, unifying ML projects with the best DevOps practices. DVC Studio also provides ML experiment tracking, visualization, collaboration, and automation using Git. It applies software engineering and DevOps best-practices to automate ML bookkeeping and model training, enabling easy collaboration and faster iterations.

Data Science Dojo is a globally recognized e-learning platform that offers programs in data science, data analytics, machine learning, and more. They provide comprehensive and hands-on training in various formats such as in-person, virtual instructor-led, and self-paced training. The focus is on helping students develop a think-business-first mindset to apply their data science skills effectively in real-world scenarios. With over 2500 enterprises trained, Data Science Dojo aims to make data science accessible to everyone.

Compact Data Science is a data science platform that provides a comprehensive set of tools and resources for data scientists and analysts. The platform includes a variety of features such as data preparation, data visualization, machine learning, and predictive analytics. Compact Data Science is designed to be easy to use and accessible to users of all skill levels.

Siml.ai is a software platform designed for fast AI-driven physics simulations. It combines state-of-the-art machine learning with physics simulation to provide interactive visualization and high-performance AI-based numerical simulators. The platform offers Model Engineer for training and optimizing physics simulators using deep learning techniques, Simulation Studio for leveraging AI simulator models, and features like web-based interface, painless scalability, and automated high-performance computing. Siml.ai aims to democratize scientific-grade simulation tools, making it accessible for engineers and researchers to develop and deploy physics-based simulations in their workflows.

Streamlit is a web application framework that allows data scientists and machine learning engineers to create interactive web applications easily using Python. It simplifies the process of building and sharing data-focused applications, enabling users to visualize data, create dashboards, and deploy machine learning models without the need for extensive web development knowledge.

Streamlit is a web application framework that allows data scientists and machine learning engineers to create interactive web applications quickly and easily. With Streamlit, users can build data-driven applications using simple Python scripts without the need for web development expertise. The platform provides a seamless way to showcase data visualizations, machine learning models, and other data science projects in a user-friendly and interactive manner.

Dflux is a cloud-based Unified Data Science Platform that offers end-to-end data engineering and intelligence with a no-code ML approach. It enables users to integrate data, perform data engineering, create customized models, analyze interactive dashboards, and make data-driven decisions for customer retention and business growth. Dflux bridges the gap between data strategy and data science, providing powerful SQL editor, intuitive dashboards, AI-powered text to SQL query builder, and AutoML capabilities. It accelerates insights with data science, enhances operational agility, and ensures a well-defined, automated data science life cycle. The platform caters to Data Engineers, Data Scientists, Data Analysts, and Decision Makers, offering all-round data preparation, AutoML models, and built-in data visualizations. Dflux is a secure, reliable, and comprehensive data platform that automates analytics, machine learning, and data processes, making data to insights easy and accessible for enterprises.

Byterat is a cloud-based platform that provides battery data management, visualization, and analytics. It offers an end-to-end data pipeline that automatically synchronizes, processes, and visualizes materials, manufacturing, and test data from all labs. Byterat also provides 24/7 access to experiments from anywhere in the world and integrates seamlessly with current workflows. It is customizable to specific cell chemistries and allows users to build custom visualizations, dashboards, and analyses. Byterat's AI-powered battery research has been published in leading journals, and its team has pioneered a new class of models that extract tell-tale signals of battery health from electrical signals to forecast future performance.

Comet ML is an extensible, fully customizable machine learning platform that aims to move ML forward by supporting productivity, reproducibility, and collaboration. It integrates with existing infrastructure and tools to manage, visualize, and optimize models from training runs to production monitoring. Users can track and compare training runs, create a model registry, and monitor models in production all in one platform. Comet's platform can be run on any infrastructure, enabling users to reshape their ML workflow and bring their existing software and data stack.

KNIME is a data science platform that enables users to analyze, blend, transform, model, visualize, and deploy data science solutions without coding. It provides a range of features and advantages for business and domain experts, data experts, end users, and MLOps & IT professionals across various industries and departments.

Comet ML is a machine learning platform that integrates with your existing infrastructure and tools so you can manage, visualize, and optimize models—from training runs to production monitoring.

Comet ML is a machine learning platform that integrates with your existing infrastructure and tools so you can manage, visualize, and optimize models—from training runs to production monitoring.

VisualizeAI is a powerful AI-powered platform that helps businesses visualize and analyze their data. With VisualizeAI, you can easily create stunning data visualizations, dashboards, and reports that will help you make better decisions. VisualizeAI is perfect for businesses of all sizes, from startups to large enterprises. It is easy to use and affordable, and it can help you save time and money while improving your decision-making.

Graphy is a data visualization and reporting tool that helps marketers create beautiful, interactive reports in minutes. It is powered by AI to increase productivity and make data more accessible and understandable. With Graphy, you can unify your data from all your tools into a single, shareable view. You can also explore data in the tools you've already mastered, then save it in Graphy to tell your data story with AI Insights, comments, annotations, goals, trend lines, and even emojis.

Sertis is a leading AI solutions provider in Thailand, offering a comprehensive suite of end-to-end solutions encompassing AI, data analytics, data science, and data engineering. Their services include data analytics, predictive analytics, machine learning, data visualization, AI and data science consulting, and big data engineering. Sertis's mission is to help businesses unlock the full potential of their data and drive growth and innovation across multiple industries.

Resume Matcher is a free, open-source Applicant Tracking System (ATS) tool that utilizes Machine Learning and Natural Language Processing techniques to help users tailor their resumes to job descriptions. The platform offers data visualization, text similarity analysis, and upcoming features like Vector Similarity. It aims to empower developers by providing insights into resume-job description alignment and facilitating informed, data-driven decisions in the job market.

GPUX is a cloud platform that provides access to GPUs for running AI workloads. It offers a variety of features to make it easy to deploy and run AI models, including a user-friendly interface, pre-built templates, and support for a variety of programming languages. GPUX is also committed to providing a sustainable and ethical platform, and it has partnered with organizations such as the Climate Leadership Council to reduce its carbon footprint.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

Deep Lake is a Database for AI powered by a storage format optimized for deep-learning applications. Deep Lake can be used for: 1. Storing data and vectors while building LLM applications 2. Managing datasets while training deep learning models Deep Lake simplifies the deployment of enterprise-grade LLM-based products by offering storage for all data types (embeddings, audio, text, videos, images, pdfs, annotations, etc.), querying and vector search, data streaming while training models at scale, data versioning and lineage, and integrations with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more. Deep Lake works with data of any size, it is serverless, and it enables you to store all of your data in your own cloud and in one place. Deep Lake is used by Intel, Bayer Radiology, Matterport, ZERO Systems, Red Cross, Yale, & Oxford.

The 'awesome-AI4MolConformation-MD' repository focuses on protein conformations and molecular dynamics using generative artificial intelligence and deep learning. It provides resources, reviews, datasets, packages, and tools related to AI-driven molecular dynamics simulations. The repository covers a wide range of topics such as neural networks potentials, force fields, AI engines/frameworks, trajectory analysis, visualization tools, and various AI-based models for protein conformational sampling. It serves as a comprehensive guide for researchers and practitioners interested in leveraging AI for studying molecular structures and dynamics.

DagsHub is a platform for machine learning and data science teams to build, manage, and collaborate on their projects. With DagsHub you can: 1. Version code, data, and models in one place. Use the free provided DagsHub storage or connect it to your cloud storage 2. Track Experiments using Git, DVC or MLflow, to provide a fully reproducible environment 3. Visualize pipelines, data, and notebooks in and interactive, diff-able, and dynamic way 4. Label your data directly on the platform using Label Studio 5. Share your work with your team members 6. Stream and upload your data in an intuitive and easy way, while preserving versioning and structure. DagsHub is built firmly around open, standard formats for your project. In particular: * Git * DVC * MLflow * Label Studio * Standard data formats like YAML, JSON, CSV Therefore, you can work with DagsHub regardless of your chosen programming language or frameworks.

ExplainableAI.jl is a Julia package that implements interpretability methods for black-box classifiers, focusing on local explanations and attribution maps in input space. The package requires models to be differentiable with Zygote.jl. It is similar to Captum and Zennit for PyTorch and iNNvestigate for Keras models. Users can analyze and visualize explanations for model predictions, with support for different XAI methods and customization. The package aims to provide transparency and insights into model decision-making processes, making it a valuable tool for understanding and validating machine learning models.

FastTrackML is an experiment tracking server focused on speed and scalability, fully compatible with MLFlow. It provides a user-friendly interface to track and visualize your machine learning experiments, making it easy to compare different models and identify the best performing ones. FastTrackML is open source and can be easily installed and run with pip or Docker. It is also compatible with the MLFlow Python package, making it easy to integrate with your existing MLFlow workflows.

Kafka-ML is a framework designed to manage the pipeline of Tensorflow/Keras and PyTorch machine learning models on Kubernetes. It enables the design, training, and inference of ML models with datasets fed through Apache Kafka, connecting them directly to data streams like those from IoT devices. The Web UI allows easy definition of ML models without external libraries, catering to both experts and non-experts in ML/AI.

MATLAB-Simulink-Challenge-Project-Hub is a repository aimed at contributing to the progress of engineering and science by providing challenge projects with real industry relevance and societal impact. The repository offers a wide range of projects covering various technology trends such as Artificial Intelligence, Autonomous Vehicles, Big Data, Computer Vision, and Sustainability. Participants can gain practical skills with MATLAB and Simulink while making a significant contribution to science and engineering. The projects are designed to enhance expertise in areas like Sustainability and Renewable Energy, Control, Modeling and Simulation, Machine Learning, and Robotics. By participating in these projects, individuals can receive official recognition for their problem-solving skills from technology leaders at MathWorks and earn rewards upon project completion.

Weights & Biases (W&B) is a platform that helps users build better machine learning models faster by tracking and visualizing all components of the machine learning pipeline, from datasets to production models. It offers tools for tracking, debugging, evaluating, and monitoring machine learning applications. W&B provides integrations with popular frameworks like PyTorch, TensorFlow/Keras, Hugging Face Transformers, PyTorch Lightning, XGBoost, and Sci-Kit Learn. Users can easily log metrics, visualize performance, and compare experiments using W&B. The platform also supports hosting options in the cloud or on private infrastructure, making it versatile for various deployment needs.

Unofficial Julia Bindings for wandb.ai. Wandb is a platform for tracking and visualizing machine learning experiments. It provides a simple and consistent way to log metrics, parameters, and other data from your experiments, and to visualize them in a variety of ways. Wandb.jl provides a convenient way to use Wandb from Julia.

AIDE is a machine learning code generation agent that can generate solutions for machine learning tasks from natural language descriptions. It has the following features: 1. **Instruct with Natural Language**: Describe your problem or additional requirements and expert insights, all in natural language. 2. **Deliver Solution in Source Code**: AIDE will generate Python scripts for the **tested** machine learning pipeline. Enjoy full transparency, reproducibility, and the freedom to further improve the source code! 3. **Iterative Optimization**: AIDE iteratively runs, debugs, evaluates, and improves the ML code, all by itself. 4. **Visualization**: We also provide tools to visualize the solution tree produced by AIDE for a better understanding of its experimentation process. This gives you insights not only about what works but also what doesn't. AIDE has been benchmarked on over 60 Kaggle data science competitions and has demonstrated impressive performance, surpassing 50% of Kaggle participants on average. It is particularly well-suited for tasks that require complex data preprocessing, feature engineering, and model selection.

The Microsoft Health Intelligence Machine Learning Toolbox is a repository that provides low-level and high-level building blocks for Machine Learning / AI researchers and practitioners. It simplifies and streamlines work on deep learning models for healthcare and life sciences by offering tested components such as data loaders, pre-processing tools, deep learning models, and cloud integration utilities. The repository includes two Python packages, 'hi-ml-azure' for helper functions in AzureML, 'hi-ml' for ML components, and 'hi-ml-cpath' for models and workflows related to histopathology images.

Evidently is an open-source Python library designed for evaluating, testing, and monitoring machine learning (ML) and large language model (LLM) powered systems. It offers a wide range of functionalities, including working with tabular, text data, and embeddings, supporting predictive and generative systems, providing over 100 built-in metrics for data drift detection and LLM evaluation, allowing for custom metrics and tests, enabling both offline evaluations and live monitoring, and offering an open architecture for easy data export and integration with existing tools. Users can utilize Evidently for one-off evaluations using Reports or Test Suites in Python, or opt for real-time monitoring through the Dashboard service.

The Vertex AI SDK for Python is a library that provides a convenient way to use the Vertex AI API. It offers a high-level interface for creating and managing Vertex AI resources, such as datasets, models, and endpoints. The SDK also provides support for training and deploying custom models, as well as using AutoML models. With the Vertex AI SDK for Python, you can quickly and easily build and deploy machine learning models on Vertex AI.

Awesome AI Tools is a curated list of popular tools and resources for artificial intelligence enthusiasts. It includes a wide range of tools such as machine learning libraries, deep learning frameworks, data visualization tools, and natural language processing resources. Whether you are a beginner or an experienced AI practitioner, this repository aims to provide you with a comprehensive collection of tools to enhance your AI projects and research. Explore the list to discover new tools, stay updated with the latest advancements in AI technology, and find the right resources to support your AI endeavors.

This repository contains a collection of AI projects covering various areas of machine learning. Each project is accompanied by detailed articles on the associated blog sciblog. Projects range from introductory topics like Convolutional Neural Networks and Transfer Learning to advanced topics like Fraud Detection and Recommendation Systems. The repository also includes tutorials on data generation, distributed training, natural language processing, and time series forecasting. Additionally, it features visualization projects such as football match visualization using Datashader.

FarmVibes.AI is a repository focused on developing multi-modal geospatial machine learning models for agriculture and sustainability. It enables users to fuse various geospatial and spatiotemporal datasets, such as satellite imagery, drone imagery, and weather data, to generate robust insights for agriculture-related problems. The repository provides fusion workflows, data preparation tools, model training notebooks, and an inference engine to facilitate the creation of geospatial models tailored for agriculture and farming. Users can interact with the tools via a local cluster, REST API, or a Python client, and the repository includes documentation and notebook examples to guide users in utilizing FarmVibes.AI for tasks like harvest date detection, climate impact estimation, micro climate prediction, and crop identification.

Assists with predictive modeling techniques and data analysis insights.

파이썬, C++, MATLAB 코드 및 GIF 제공창고, 특정 기술 키워드 제공

我是一个温和的老师，以最温和的语气解答我学生的一切问题，聪明的你提问吧，加微信simons2035获取python\numpy\pandas\matplotlib全套思维导图吧！

Guides on building and deploying apps on Streamlit.

Detailed technical diagram and graph assistant

Specialist in belief transformation using advanced NLP and visualization, now more powerful with a two-component structure.

Data specialist for Toronto Government Data Platform insights

Friendly, professional data analysis expert.

Expert in data analysis and visualization, offering clear explanations and guidance.

I'm the data, image, or document you uploaded, talking to you.

Expert in data analysis, visualizations, and predictions.

Your interactive data analysis expert

A Data Engineer assistant offering advice on data pipelines and data-related tasks.

A data assistant for devs, now with real video and image links.

Your wise R Shiny sage, offering enlightened guidance.

Expert in precise visualization of 'Chibi Adarsh'.

Your expert in Omniscope with an expanded knowledge base.

Expert Power BI assistant with a knowledge-driven approach.

I guide chess strategy and visualize board states.

I craft visuals for medical concepts.