Openlayer

It provides all you need to build and deploy computer vision models, from data annotation and organization tools to scalable deployment solutions that work across devices.

It is a framework built around LLMs. It can be used for chatbots, generative question-answering, summarization, and much more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs.

It is an open-source library designed to help developers build conversational streaming user interfaces in JavaScript and TypeScript. The SDK supports React/Next.js, Svelte/SvelteKit, and Vue/Nuxt as well as Node.js, Serverless, and the Edge Runtime.

A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.

Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.

This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.

Build, train, and deploy state of the art models powered by the reference open source in machine learning.

It allows you to run open-source large language models, such as Llama 2, locally.

It is a project that provides a central interface to connect your LLMs with external data. It offers you a comprehensive toolset trading off cost and performance.

It lets you run machine learning models with a few lines of code, without needing to understand how machine learning works.

It is an open-source embedding database. Chroma makes it easy to build LLM apps by making knowledge, facts, and skills pluggable for LLMs.

Makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively.

Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables

It is a Rust ecosystem of libraries for running inference on large language models, inspired by llama.cpp. On top of llm, there is a CLI application, llm-cli, which provides a convenient interface for running inference on supported models.

Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon.

It is an open-source, drag & drop UI to build your customized LLM flow. It is built on top of LangChainJS, with the aim to make it easy for people to visualize and build LLM apps.

Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications.

Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.

It is the easiest way for customers to build and scale generative AI-based applications using FMs, democratizing access for all builders.

It lets you run or deploy machine learning models, massively parallel compute jobs, task queues, web apps, and much more, without your own infrastructure.

It is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner.

BigML provides a hosted machine learning platform for advanced analytics. Through BigML's intuitive interface and/or its open API and bindings in several languages, analysts, data scientists and developers alike can quickly build fully actionable predictive models and clusters that can easily be incorporated into related applications and services.

Firebase Predictions uses the power of Google’s machine learning to create dynamic user groups based on users’ predicted behavior.

It is an easy to use desktop app for experimenting with local and open-source Large Language Models (LLMs). It supports any ggml Llama, MPT, and StarCoder model on Hugging Face (Llama 2, Orca, Vicuna, Nous Hermes, WizardCoder, MPT, etc.)

It aims to enable developers (and even non-developers) to quickly build useful applications based on large language models, ensuring they are visual, operable, and improvable.

Machine learners share, stress test, and stay up-to-date on all the latest ML techniques and technologies. Discover a huge repository of community-published models, data & code for your next project.

It is a full-stack application and tool suite that enables you to turn any document, resource, or piece of content into a piece of data that any LLM can use as reference during chatting. This application runs with very minimal overhead as by default the LLM and vectorDB are hosted remotely, but can be swapped for local instances.

Platform-as-a-Service for training and deploying your DL models in the cloud. Start running your first project in < 30 sec! Floyd takes care of the grunt work so you can focus on the core of your problem.

Building an intelligent, predictive application involves iterating over multiple steps: cleaning the data, developing features, training a model, and creating and maintaining a predictive service. GraphLab Create does all of this in one platform. It is easy to use, fast, and powerful.

It is the machine learning platform for developers to build better models faster. Use W&B's lightweight, interoperable tools to quickly track experiments, version and iterate on datasets, evaluate model performance, reproduce models, visualize results and spot regressions, and share findings with colleagues.

It is an open-source product analytics suite for LLM-based applications. Iterate faster on your application with a granular view of exact execution traces, quality, cost, and latency.

It is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation.

It is a platform for building production-grade LLM applications. It lets you debug, test, evaluate, and monitor chains and intelligent agents built on any LLM framework and seamlessly integrates with LangChain, the go-to open source framework for building with LLMs.

It is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. It achieves this by allowing you to define plugins that can be chained together in just a few lines of code.

Lamina helps you integrate Deep Learning models like Sentiment Analysis and Entity Extraction into your products with a simple API call. Relieving you of getting data, creating a model and training them which would be compute-intensive.

It is the framework for solving advanced tasks with language models (LMs) and retrieval models (RMs). It unifies techniques for prompting and fine-tuning LMs — and approaches for reasoning, self-improvement, and augmentation with retrieval and tools.

Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs (GPT-4, ChatGPT, etc.). It offers production-ready tools to build NLP backend services, e.g., question answering or semantic search.

It is a chat interface that lets you interact with Ollama. It offers features such as code syntax highlighting, Markdown and LaTeX support, local RAG integration, and prompt preset support. It can be installed using Docker or Kubernetes.

It is a Recommender as a Service with easy integration and powerful Admin UI. The Recombee recommendation engine can be applied to any domain that has a catalog of items and is interacted by a large number of users. Applicable to web and mobile apps, It improves user experience by showing the most relevant content for individual users.

Gradient° is a suite of tools for exploring data and training neural networks. Gradient° includes 1-click Jupyter notebooks, a powerful job runner, and a python module to run any code on a fully managed GPU cluster in the cloud. Gradient is also rolling out full support for Google's new TPUv2 accelerator to power even more newer workflows.

It is an open-source monitoring & observability for AI apps and agents. It is designed to be usable with any model, not just OpenAI. It is easy to integrate and simple to self-host.

It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.

Dasha is a conversational AI as a Service platform. Dasha lets you create conversational apps that are more human-like than ever before, quicker than ever before and quickly integrate them into your products.

Delight your users with personalised content recommendations. It's easy to set up and works with or without collaborative data. The Lateral API is trained on 10s of millions of high quality documents from law, academia and journalism. It can understand any document and provide intelligent recommendations.

It enables LLMs to use tools by invoking APIs. Given a natural language query, it comes up with the semantically and syntactically correct API to invoke.

It is a powerful generative large language model that is designed to improve search accuracy and provide personalized recommendations. It is capable of performing a range of generative AI tasks, including text summarization, and text generation, etc.

It is a lightning-fast inference platform that helps you serve your large language models (LLMs). Use a state-of-the-art, open-source model or fine-tune and deploy your own at no additional cost.

It is an open platform for operating large language models (LLMs) in production. Fine-tune, serve, deploy, and monitor any LLMs with ease. Run inference with any open-source large-language models, deploy to the cloud or on-premise, and build powerful AI apps.

It is the easiest way to deploy Machine Learning models. Start deploying Tensorflow, Scikit, Keras and spaCy straight from your notebook with just one extra line.

It is a machine learning profiler. It helps data scientists and ML engineers make model training and inference faster and more efficient.