Best AI tools for< Deploy Neural Networks >

Thunderbit is an all-in-one AI toolkit that provides a variety of AI-powered apps and automation tools. With Thunderbit, users can summarize web content, clarify text, translate languages, get definitions, send personalized emails, search the web, and interpret legal documents, among other tasks. Thunderbit's apps are easy to use and can be customized to fit the user's specific needs. Thunderbit also integrates with other popular apps, making it a versatile tool for a variety of tasks.

Enterprise AI provides comprehensive information, news, and tips on artificial intelligence (AI) for businesses. It covers various aspects of AI, including AI business strategies, AI infrastructure, AI technologies, AI platforms, careers in AI, and enterprise applications of AI. The website offers insights into the latest AI trends, best practices, and industry news. It also provides resources such as e-books, webinars, and podcasts to help businesses understand and implement AI solutions.

Apache MXNet is a flexible and efficient deep learning library designed for research, prototyping, and production. It features a hybrid front-end that seamlessly transitions between imperative and symbolic modes, enabling both flexibility and speed. MXNet also supports distributed training and performance optimization through Parameter Server and Horovod. With bindings for multiple languages, including Python, Scala, Julia, Clojure, Java, C++, R, and Perl, MXNet offers wide accessibility. Additionally, it boasts a thriving ecosystem of tools and libraries that extend its capabilities in computer vision, NLP, time series, and more.

NVIDIA is a world leader in artificial intelligence computing. The company's products and services are used by businesses and governments around the world to develop and deploy AI applications. NVIDIA's AI platform includes hardware, software, and tools that make it easy to build and train AI models. The company also offers a range of cloud-based AI services that make it easy to deploy and manage AI applications. NVIDIA's AI platform is used in a wide variety of industries, including healthcare, manufacturing, retail, and transportation. The company's AI technology is helping to improve the efficiency and accuracy of a wide range of tasks, from medical diagnosis to product design.

TensorFlow is an end-to-end platform for machine learning. It provides a wide range of tools and resources to help developers build, train, and deploy ML models. TensorFlow is used by researchers and developers all over the world to solve real-world problems in a variety of domains, including computer vision, natural language processing, and robotics.

Keras is an open-source deep learning API written in Python, designed to make building and training deep learning models easier. It provides a user-friendly interface and a wide range of features and tools to help developers create and deploy machine learning applications. Keras is compatible with multiple frameworks, including TensorFlow, Theano, and CNTK, and can be used for a variety of tasks, including image classification, natural language processing, and time series analysis.

Seldon is an MLOps platform that helps enterprises deploy, monitor, and manage machine learning models at scale. It provides a range of features to help organizations accelerate model deployment, optimize infrastructure resource allocation, and manage models and risk. Seldon is trusted by the world's leading MLOps teams and has been used to install and manage over 10 million ML models. With Seldon, organizations can reduce deployment time from months to minutes, increase efficiency, and reduce infrastructure and cloud costs.

Mystic.ai is an AI tool designed to deploy and scale Machine Learning models with ease. It offers a fully managed Kubernetes platform that runs in your own cloud, allowing users to deploy ML models in their own Azure/AWS/GCP account or in a shared GPU cluster. Mystic.ai provides cost optimizations, fast inference, simpler developer experience, and performance optimizations to ensure high-performance AI model serving. With features like pay-as-you-go API, cloud integration with AWS/Azure/GCP, and a beautiful dashboard, Mystic.ai simplifies the deployment and management of ML models for data scientists and AI engineers.

Azure Static Web Apps is a platform provided by Microsoft Azure for building and deploying modern web applications. It allows developers to easily host static web content and serverless APIs with seamless integration to popular frameworks like React, Angular, and Vue. With Azure Static Web Apps, developers can quickly set up continuous integration and deployment workflows, enabling them to focus on building great user experiences without worrying about infrastructure management.

PoplarML is a platform that enables the deployment of production-ready, scalable ML systems with minimal engineering effort. It offers one-click deploys, real-time inference, and framework agnostic support. With PoplarML, users can seamlessly deploy ML models using a CLI tool to a fleet of GPUs and invoke their models through a REST API endpoint. The platform supports Tensorflow, Pytorch, and JAX models.

Hanabi.rest is an AI-based API building platform that allows users to create REST APIs from natural language and screenshots using AI technology. Users can deploy the APIs on Cloudflare Workers and roll them out globally. The platform offers a live editor for testing database access and API endpoints, generates code compatible with various runtimes, and provides features like sharing APIs via URL, npm package integration, and CLI dump functionality. Hanabi.rest simplifies API design and deployment by leveraging natural language processing, image recognition, and v0.dev components.

Superflows is a tool that allows you to add an AI Copilot to your SaaS product. This AI Copilot can answer questions and perform tasks for users via chat. It is designed to be easy to set up and configure, and it can be integrated into your codebase with just a few lines of code. Superflows is a great way to improve the user experience of your SaaS product and help users get the most out of your software.

Outfit AI is an AI tool that enables users to design and deploy AI models or workflows as user-ready applications in minutes. It allows users to create custom user interfaces for their AI-powered apps by dropping in an API key from Replicate or Hugging Face. With Outfit AI, users can have creative control over the design of their apps, build complex workflows without any code, and optimize prompts for better performance. The tool aims to help users launch their models faster, save time, and enhance their AI applications with a built-in product copilot.

IBM Watsonx is an enterprise studio for AI builders. It provides a platform to train, validate, tune, and deploy AI models quickly and efficiently. With Watsonx, users can access a library of pre-trained AI models, build their own models, and deploy them to the cloud or on-premises. Watsonx also offers a range of tools and services to help users manage and monitor their AI models.

Lazy AI is a platform that enables users to build full stack web applications 10 times faster by utilizing AI technology. Users can create and modify web apps with prompts and deploy them to the cloud with just one click. The platform offers a variety of features including AI Component Builder, eCommerce store creation, Crypto Arbitrage Scraper, Text to Speech Converter, Lazy Image to Video generation, PDF Chatbot, and more. Lazy AI aims to streamline the app development process and empower users to leverage AI for various tasks.

PixieBrix is an AI engagement platform that allows users to build, deploy, and manage internal AI tools to drive team productivity. It unifies AI landscapes with oversight and governance for enterprise scale. The platform is enterprise-ready and fully customizable to meet unique needs, and can be deployed on any site, making it easy to integrate into existing systems. PixieBrix leverages the power of AI and automation to harness the latest technology to streamline workflows and take productivity to new heights.

Datature is an all-in-one platform for building and deploying computer vision models. It provides tools for data management, annotation, training, and deployment, making it easy to develop and implement computer vision solutions. Datature is used by a variety of industries, including healthcare, retail, manufacturing, and agriculture.

Amazon Bedrock is a cloud-based platform that enables developers to build, deploy, and manage serverless applications. It provides a fully managed environment that takes care of the infrastructure and operations, so developers can focus on writing code. Bedrock also offers a variety of tools and services to help developers build and deploy their applications, including a code editor, a debugger, and a deployment pipeline.

TitanML is a platform that provides tools and services for deploying and scaling Generative AI applications. Their flagship product, the Titan Takeoff Inference Server, helps machine learning engineers build, deploy, and run Generative AI models in secure environments. TitanML's platform is designed to make it easy for businesses to adopt and use Generative AI, without having to worry about the underlying infrastructure. With TitanML, businesses can focus on building great products and solving real business problems.

This is a unified DevOps platform to build AI applications. It provides a comprehensive set of tools and services to help developers build, deploy, and manage AI applications. The platform includes a variety of features such as a code editor, a debugger, a profiler, and a deployment manager. It also provides access to a variety of AI services, such as natural language processing, machine learning, and computer vision.

Qdrant is a vector similarity search engine and vector database. It is written in Rust, which makes it fast and reliable even under high load. Qdrant can be used for a variety of applications, including: * Semantic search * Image search * Product recommendations * Chatbots * Anomaly detection Qdrant offers a variety of features, including: * Payload storage and filtering * Hybrid search with sparse vectors * Vector quantization and on-disk storage * Distributed deployment * Highlighted features such as query planning, payload indexes, SIMD hardware acceleration, async I/O, and write-ahead logging Qdrant is available as a fully managed cloud service or as an open-source software that can be deployed on-premises.

Netsaur is a powerful machine learning library for Deno, offering a lightweight and easy-to-use neural network solution. It is blazingly fast and efficient, providing a simple API for creating and training neural networks. Netsaur can run on both CPU and GPU, making it suitable for serverless environments. With Netsaur, users can quickly build and deploy machine learning models for various applications with minimal dependencies. This library is perfect for both beginners and experienced machine learning practitioners.

The Cheat-Sheet Collection for DevOps, Engineers, IT professionals, and more is a curated list of cheat sheets for various tools and technologies commonly used in the software development and IT industry. It includes cheat sheets for Nginx, Docker, Ansible, Python, Go (Golang), Git, Regular Expressions (Regex), PowerShell, VIM, Jenkins, CI/CD, Kubernetes, Linux, Redis, Slack, Puppet, Google Cloud Developer, AI, Neural Networks, Machine Learning, Deep Learning & Data Science, PostgreSQL, Ajax, AWS, Infrastructure as Code (IaC), System Design, and Cyber Security.

AITemplate (AIT) is a Python framework that transforms deep neural networks into CUDA (NVIDIA GPU) / HIP (AMD GPU) C++ code for lightning-fast inference serving. It offers high performance close to roofline fp16 TensorCore (NVIDIA GPU) / MatrixCore (AMD GPU) performance on major models. AITemplate is unified, open, and flexible, supporting a comprehensive range of fusions for both GPU platforms. It provides excellent backward capability, horizontal fusion, vertical fusion, memory fusion, and works with or without PyTorch. FX2AIT is a tool that converts PyTorch models into AIT for fast inference serving, offering easy conversion and expanded support for models with unsupported operators.

Zeta is a tool designed to build state-of-the-art AI models faster by providing modular, high-performance, and scalable building blocks. It addresses the common issues faced while working with neural nets, such as chaotic codebases, lack of modularity, and low performance modules. Zeta emphasizes usability, modularity, and performance, and is currently used in hundreds of models across various GitHub repositories. It enables users to prototype, train, optimize, and deploy the latest SOTA neural nets into production. The tool offers various modules like FlashAttention, SwiGLUStacked, RelativePositionBias, FeedForward, BitLinear, PalmE, Unet, VisionEmbeddings, niva, FusedDenseGELUDense, FusedDropoutLayerNorm, MambaBlock, Film, hyper_optimize, DPO, and ZetaCloud for different tasks in AI model development.

Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. It is designed to be easy to get started with and simple to use for Java developers. DJL provides a native Java development experience and allows users to integrate machine learning and deep learning models with their Java applications. The framework is deep learning engine agnostic, enabling users to switch engines at any point for optimal performance. DJL's ergonomic API interface guides users with best practices to accomplish deep learning tasks, such as running inference and training neural networks.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

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.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

The 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

JAX AI Stack is a suite of libraries built around the JAX Python package for array-oriented computation and program transformation. It provides a growing ecosystem of packages for specialized numerical computing across various domains, encouraging modularity and innovation in domain-specific libraries. The stack includes core packages like JAX, flax for building neural networks, ml_dtypes for NumPy dtype extensions, optax for gradient processing and optimization, and orbax for checkpointing and persistence utilities. Optional packages like grain data loader and tensorflow are also available for installation.

Ludwig is a declarative deep learning framework designed for scale and efficiency. It is a low-code framework that allows users to build custom AI models like LLMs and other deep neural networks with ease. Ludwig offers features such as optimized scale and efficiency, expert level control, modularity, and extensibility. It is engineered for production with prebuilt Docker containers, support for running with Ray on Kubernetes, and the ability to export models to Torchscript and Triton. Ludwig is hosted by the Linux Foundation AI & Data.

The 100 Days in AI repository provides a comprehensive roadmap for individuals to learn Artificial Intelligence over a period of 100 days. It covers topics ranging from basic programming in Python to advanced concepts in AI, including machine learning, deep learning, and specialized AI topics. The repository includes daily tasks, resources, and exercises to ensure a structured learning experience. By following this roadmap, users can gain a solid understanding of AI and be prepared to work on real-world AI projects.

This repository is a compilation of excellent ML and DL tutorials created by various individuals and organizations. It covers a wide range of topics, including machine learning fundamentals, deep learning, computer vision, natural language processing, reinforcement learning, and more. The resources are organized into categories, making it easy to find the information you need. Whether you're a beginner or an experienced practitioner, you're sure to find something valuable in this repository.

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.

Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

Start Machine Learning in 2024 is a comprehensive guide for beginners to advance in machine learning and artificial intelligence without any prior background. The guide covers various resources such as free online courses, articles, books, and practical tips to become an expert in the field. It emphasizes self-paced learning and provides recommendations for learning paths, including videos, podcasts, and online communities. The guide also includes information on building language models and applications, practicing through Kaggle competitions, and staying updated with the latest news and developments in AI. The goal is to empower individuals with the knowledge and resources to excel in machine learning and AI.

Assists with creating, refining, and understanding neural networks.

I'm an expert in TensorFlow, providing detailed, accurate guidance for all skill levels.

NeurIPS 2023 Gateway

Adapts explanations to user's TensorFlow skill level

Expert in PyTorch, adept at simplifying complex concepts.

Build & deploy websites from your ideas.

AI front-end developer expert in coding React, Nextjs, Vue, Svelte, Typescript, Gatsby, Angular, HTML, CSS, JavaScript & advanced in Flexbox, Tailwind & Material Design. Mentors in coding & debugging for junior, intermediate & senior front-end developers alike. Let’s code, build & deploy a SaaS app.

Agent specialist on building and deploying OpenAI Assistant API's

Firebase Cloud Functions expert and guide.

Your Kubernetes and cloud-native tech guide

Your Personal Cloud DevOps Mentor

Assistant for kubernetes environments managed by gitops

Expert on Jelastic/Virtuozzo PaaS solutions.

Expert in Kubernetes, using Kubernetes website source code for insights.

Azure Arc expert providing guidance on architecture, deployment, and management.

Docker & cloud computing expert

An expert MLOps engineer assisting in DevOps and pipeline optimization.

Formador para la certificación de ML Engineer en GCP, con respuestas y explicaciones detalladas.

Expert in Docker and Docker Swarm solutions and troubleshooting.

Expert advice on Azure, clear and concise.