Best AI Development Platforms for Linux

With minimal setup, developers can add advanced generative AI to .NET projects for chatbots, text generation, content retrieval, natural language processing, translation, and structured data extraction, while on-device inference uses hybrid CPU and GPU acceleration for rapid local processing that protects data, and frequent updates fold in the latest research so teams can build secure, high-performance AI applications with streamlined development and full control.

An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.

Mistral AI is a pioneering artificial intelligence startup specializing in open-source generative AI. The company offers a range of customizable, enterprise-grade AI solutions deployable across various platforms, including on-premises, cloud, edge, and devices. Flagship products include "Le Chat," a multilingual AI assistant designed to enhance productivity in both personal and professional contexts, and "La Plateforme," a developer platform that enables the creation and deployment of AI-powered applications. Committed to transparency and innovation, Mistral AI positions itself as a leading independent AI lab, contributing significantly to open-source AI and policy development.

Build, deploy, and scale end-to-end AI applications in the cloud. Griptape gives developers everything they need to build, deploy, and scale retrieval-driven AI-powered applications, from the development framework to the execution runtime. 🎢 Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. ☁️ Griptape Cloud is a one-stop shop to hosting your AI structures, whether they are built with Griptape, another framework, or call directly to the LLMs themselves. Simply point to your GitHub repository to get started. 🔥 Run your hosted code by hitting a basic API layer from wherever you need, offloading the expensive tasks of AI development to the cloud. 📈 Automatically scale workloads to fit your needs.

Faros AI connects the dots between your engineering data sources – ticketing, source control, CI/CD, and more – giving unprecedented visibility and insight into your engineering processes. Be amazed at what you can achieve with Faros AI. With Faros AI, engineering leaders can scale their operations in a more data-informed way — using data to identify bottlenecks, measure progress towards organizational goals, better support teams with the right resources, and accurately assess the impact of interventions over time. DORA Metrics come standard in Faros AI, and the platform is extensible to allow organizations to build their own custom dashboards and metrics so they can get deep insights into their engineering operations and take intelligent action in a data-driven manner. Leading organizations including Box, Coursera, GoFundMe, Astronomer, Salesforce, etc. trust Faros AI as their engops platform of choice.

Ollama is an innovative platform that focuses on providing AI-powered tools and services, designed to make it easier for users to interact with and build AI-driven applications. Run AI models locally. By offering a range of solutions, including natural language processing models and customizable AI features, Ollama empowers developers, businesses, and organizations to integrate advanced machine learning technologies into their workflows. With an emphasis on usability and accessibility, Ollama strives to simplify the process of working with AI, making it an appealing option for those looking to harness the potential of artificial intelligence in their projects.

Lilac is an open source tool that enables data and AI practitioners to improve their products by improving their data. Understand your data with powerful search and filtering. Collaborate with your team on a single, centralized dataset. Apply best practices for data curation, like removing duplicates and PII to reduce dataset size and lower training cost and time. See how your pipeline impacts your data using our diff viewer. Clustering is a technique that automatically assigns categories to each document by analyzing the text content and putting similar documents in the same category. This reveals the overarching structure of your dataset. Lilac uses state-of-the-art algorithms and LLMs to cluster the dataset and assign informative, descriptive titles. Before we do advanced searching, like concept or semantic search, we can immediately use keyword search by typing a keyword in the search box.

With our advanced planning engine, even the most complex user requests can be executed. Out-of-the-box automation, inside your product. So your users can ask your system to do awesome things using normal texts, things like "Please show me last month's sales and give me some recommendations". You can plug OpenCopilot into your product using our chat bubble, and no coding skills are required. Or you can use our SDKs to make your copilot truly blend in. You can also feed your copilot all sorts of data and it will be able to understand it and offer help to your users. You can self-host OpenCopilot on your website using a single make install command. All paid plans include personal support from the team. Your users can ask complex questions that require executing multiple actions in one go. The single platform to build, manage, and deploy your next AI-powered feature. You will get new features first, it's going to be super nice since we ship a lot.

NVIDIA FLARE (Federated Learning Application Runtime Environment) is an open source, extensible SDK designed to facilitate federated learning across diverse industries, including healthcare, finance, and automotive. It enables secure, privacy-preserving AI model training by allowing multiple parties to collaboratively train models without sharing raw data. FLARE supports various machine learning frameworks such as PyTorch, TensorFlow, RAPIDS, and XGBoost, making it adaptable to existing workflows. FLARE's componentized architecture allows for customization and scalability, supporting both horizontal and vertical federated learning. It is suitable for applications requiring data privacy and regulatory compliance, such as medical imaging and financial analytics. It is available for download via the NVIDIA NVFlare GitHub repository and PyPi.

Composio is an integration platform designed to enhance AI agents and Large Language Models (LLMs) by providing seamless connections to over 150 tools with minimal code. It supports a wide array of agentic frameworks and LLM providers, facilitating function calling for efficient task execution. Composio offers a comprehensive repository of tools, including GitHub, Salesforce, file management systems, and code execution environments, enabling AI agents to perform diverse actions and subscribe to various triggers. The platform features managed authentication, allowing users to oversee authentication processes for all users and agents from a centralized dashboard. Composio's core capabilities include a developer-first integration approach, built-in authentication management, an expanding catalog of over 90 ready-to-connect tools, a 30% increase in reliability through simplified JSON structures and improved error handling, SOC Type II compliance ensuring maximum data security.

A hybrid front-end seamlessly transitions between Gluon eager imperative mode and symbolic mode to provide both flexibility and speed. Scalable distributed training and performance optimization in research and production is enabled by the dual parameter server and Horovod support. Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R and Perl. A thriving ecosystem of tools and libraries extends MXNet and enables use-cases in computer vision, NLP, time series and more. Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision-making process have stabilized in a manner consistent with other successful ASF projects. Join the MXNet scientific community to contribute, learn, and get answers to your questions.