Best AI tools for< Deploy Ai Infrastructure >

Cerebium is a serverless AI infrastructure platform that allows teams to build, test, and deploy AI applications quickly and efficiently. With a focus on speed, performance, and cost optimization, Cerebium offers a range of features and tools to simplify the development and deployment of AI projects. The platform ensures high reliability, security, and compliance while providing real-time logging, cost tracking, and observability tools. Cerebium also offers GPU variety and effortless autoscaling to meet the diverse needs of developers and businesses.

Denvr DataWorks AI Cloud is a cloud-based AI platform that provides end-to-end AI solutions for businesses. It offers a range of features including high-performance GPUs, scalable infrastructure, ultra-efficient workflows, and cost efficiency. Denvr DataWorks is an NVIDIA Elite Partner for Compute, and its platform is used by leading AI companies to develop and deploy innovative AI solutions.

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.

Hopsworks is an AI platform that offers a comprehensive solution for building, deploying, and monitoring machine learning systems. It provides features such as a Feature Store, real-time ML capabilities, and generative AI solutions. Hopsworks enables users to develop and deploy reliable AI systems, orchestrate and monitor models, and personalize machine learning models with private data. The platform supports batch and real-time ML tasks, with the flexibility to deploy on-premises or in the cloud.

Anyscale is a company that provides a scalable compute platform for AI and Python applications. Their platform includes a serverless API for serving and fine-tuning open LLMs, a private cloud solution for data privacy and governance, and an open source framework for training, batch, and real-time workloads. Anyscale's platform is used by companies such as OpenAI, Uber, and Spotify to power their AI workloads.

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.

OmniAI is an AI tool that allows teams to deploy AI applications on their existing infrastructure. It provides a unified API experience for building AI applications and offers a wide selection of industry-leading models. With tools like Llama 3, Claude 3, Mistral Large, and AWS Titan, OmniAI excels in tasks such as natural language understanding, generation, safety, ethical behavior, and context retention. It also enables users to deploy and query the latest AI models quickly and easily within their virtual private cloud environment.

DataRobot is a leading provider of AI cloud platforms. It offers a range of AI tools and services to help businesses build, deploy, and manage AI models. DataRobot's platform is designed to make AI accessible to businesses of all sizes, regardless of their level of AI expertise. DataRobot's platform includes a variety of features to help businesses build and deploy AI models, including: * A drag-and-drop interface that makes it easy to build AI models, even for users with no coding experience. * A library of pre-built AI models that can be used to solve common business problems. * A set of tools to help businesses monitor and manage their AI models. * A team of AI experts who can provide support and guidance to businesses using the platform.

ibl.ai is a generative AI platform that focuses on education, providing cutting-edge solutions for institutions to create AI mentors, tutoring apps, and content creation tools. The platform empowers educators by giving them full control over their code, data, and models. With advanced features and support for both web and native mobile platforms, ibl.ai seamlessly integrates with existing infrastructure, making it easy to deploy across organizations. The platform is designed to enhance learning experiences, foster critical thinking, and engage students deeply in educational content.

UbiOps is an AI infrastructure platform that helps teams quickly run their AI & ML workloads as reliable and secure microservices. It offers powerful AI model serving and orchestration with unmatched simplicity, speed, and scale. UbiOps allows users to deploy models and functions in minutes, manage AI workloads from a single control plane, integrate easily with tools like PyTorch and TensorFlow, and ensure security and compliance by design. The platform supports hybrid and multi-cloud workload orchestration, rapid adaptive scaling, and modular applications with unique workflow management system.

Backend.AI is an enterprise-scale cluster backend for AI frameworks that offers scalability, GPU virtualization, HPC optimization, and DGX-Ready software products. It provides a fast and efficient way to build, train, and serve AI models of any type and size, with flexible infrastructure options. Backend.AI aims to optimize backend resources, reduce costs, and simplify deployment for AI developers and researchers. The platform integrates seamlessly with existing tools and offers fractional GPU usage and pay-as-you-play model to maximize resource utilization.

Resemble AI is an all-in-one AI voice platform offering a cutting-edge AI Voice Generator and robust Deepfake Audio Detection. The platform allows users to create hyper-realistic AI voices, deploy AI voices through the cloud or on-prem, generate natural-sounding AI voices rapidly, control nuances of AI voices, and support multilingual capabilities. Resemble AI also provides deepfake detection, voice cloning, audio editing, and audio intelligence features. The platform is used by enterprises prioritizing advanced security and safety, with a focus on creating unique, dynamic messages tailored to individual users.

Domino Data Lab is an enterprise AI platform that enables users to build, deploy, and manage AI models across any environment. It fosters collaboration, establishes best practices, and ensures governance while reducing costs. The platform provides access to a broad ecosystem of open source and commercial tools, and infrastructure, allowing users to accelerate and scale AI impact. Domino serves as a central hub for AI operations and knowledge, offering integrated workflows, automation, and hybrid multicloud capabilities. It helps users optimize compute utilization, enforce compliance, and centralize knowledge across teams.

FriendliAI is a generative AI infrastructure company that offers efficient, fast, and reliable generative AI inference solutions for production. Their cutting-edge technologies enable groundbreaking performance improvements, cost savings, and lower latency. FriendliAI provides a platform for building and serving compound AI systems, deploying custom models effortlessly, and monitoring and debugging model performance. The application guarantees consistent results regardless of the model used and offers seamless data integration for real-time knowledge enhancement. With a focus on security, scalability, and performance optimization, FriendliAI empowers businesses to scale with ease.

Daily is a platform offering real-time voice, video, and AI solutions for developers. It provides ultra-low latency, open-source SDKs, and enterprise reliability since 2016. Daily collaborates with NVIDIA on Voice Agent Blueprint, offers Pipecat - a vendor-neutral open-source orchestration framework, Daily Bots for Pipecat Cloud deployment, and Daily Infrastructure for running real-time calls on WebRTC global infrastructure. The platform ensures the best video quality on every network, with a global mesh network, low latency, and enterprise-grade security features.

Codimite is an AI-assisted offshore development company that provides a range of services to help businesses accelerate their software development, reduce costs, and drive innovation. Codimite's team of experienced engineers and project managers use AI-powered tools and technologies to deliver exceptional results for their clients. The company's services include AI-assisted software development, cloud modernization, and data and artificial intelligence solutions.

Restack is a developer tool and cloud infrastructure platform that enables users to build, launch, and scale AI products quickly and efficiently. With Restack, developers can go from local development to production in seconds, leveraging a variety of languages and frameworks. The platform offers templates, repository connections, and Dockerfile customization for seamless deployment. Restack Cloud provides cost-efficient scaling and GitHub integration for instant deployment. The platform simplifies the complexity of building and scaling AI applications, allowing users to move from code to production faster than ever before.

FinetuneFast is an AI tool designed to help developers, indie makers, and businesses to efficiently finetune machine learning models, process data, and deploy AI solutions at lightning speed. With pre-configured training scripts, efficient data loading pipelines, and one-click model deployment, FinetuneFast streamlines the process of building and deploying AI models, saving users valuable time and effort. The tool is user-friendly, accessible for ML beginners, and offers lifetime updates for continuous improvement.

Paperspace is an AI tool designed to develop, train, and deploy AI models of any size and complexity. It offers a cloud GPU platform for accelerated computing, with features such as GPU cloud workflows, machine learning solutions, GPU infrastructure, virtual desktops, gaming, rendering, 3D graphics, and simulation. Paperspace provides a seamless abstraction layer for individuals and organizations to focus on building AI applications, offering low-cost GPUs with per-second billing, infrastructure abstraction, job scheduling, resource provisioning, and collaboration tools.

Pulumi is an AI-powered infrastructure as code tool that allows engineers to manage cloud infrastructure using various programming languages like Node.js, Python, Go, .NET, Java, and YAML. It offers features such as generative AI-powered cloud management, security enforcement through policies, automated deployment workflows, asset management, compliance remediation, and AI insights over the cloud. Pulumi helps teams provision, automate, and evolve cloud infrastructure, centralize and secure secrets management, and gain security, compliance, and cost insights across all cloud assets.

The AI Enablement Stack is a curated collection of venture-backed companies, tools, and technologies that enable developers to build, deploy, and manage AI applications. It provides a structured view of the AI development ecosystem across five key layers: Agent Consumer Layer, Observability and Governance Layer, Engineering Layer, Intelligence Layer, and Infrastructure Layer. Each layer focuses on specific aspects of AI development, from end-user interaction to model training and deployment. The stack aims to help developers find the right tools for building AI applications faster and more efficiently, assist engineering leaders in making informed decisions about AI infrastructure and tooling, and help organizations understand the AI development landscape to plan technology adoption.

KitchenAI is an open-source toolkit designed to simplify AI development by serving as an AI backend and LLMOps solution. It aims to empower developers to focus on delivering results without being bogged down by AI infrastructure complexities. With features like simplifying AI integration, providing an AI backend, and empowering developers, KitchenAI streamlines the process of turning AI experiments into production-ready APIs. It offers built-in LLMOps features, is framework-agnostic and extensible, and enables faster time-to-production. KitchenAI is suitable for application developers, AI developers & data scientists, and platform & infra engineers, allowing them to seamlessly integrate AI into apps, deploy custom AI techniques, and optimize AI services with a modular framework. The toolkit eliminates the need to build APIs and infrastructure from scratch, making it easier to deploy AI code as production-ready APIs in minutes. KitchenAI also provides observability, tracing, and evaluation tools, and offers a Docker-first deployment approach for scalability and confidence.

Swirl is an open-source software that allows users to simultaneously search multiple content sources and receive AI-ranked results. It connects to various data sources, including databases, public data services, and enterprise sources, and utilizes AI and LLMs to generate insights and answers based on the user's data. Swirl is easy to use, requiring only the download of a YML file, starting in Docker, and searching with Swirl. Users can add credentials to preloaded SearchProviders to access more sources. Swirl also offers integration with ChatGPT as a configured AI model. It adapts and distributes user queries to anything with a search API, re-ranking the unified results using Large Language Models without extracting or indexing anything. Swirl includes five Google Programmable Search Engines (PSEs) to get users up and running quickly. Key features of Swirl include Microsoft 365 integration, SearchProvider configurations, query adaptation, synchronous or asynchronous search federation, optional subscribe feature, pipelining of Processor stages, results stored in SQLite3 or PostgreSQL, built-in Query Transformation support, matching on word stems and handling of stopwords, duplicate detection, re-ranking of unified results using Cosine Vector Similarity, result mixers, page through all results requested, sample data sets, optional spell correction, optional search/result expiration service, easily extensible Connector and Mixer objects, and a welcoming community for collaboration and support.

E2B Infra is a cloud runtime for AI agents. It provides SDKs and CLI to customize and manage environments and run AI agents in the cloud. The infrastructure is deployed using Terraform and is currently only deployable on GCP. The main components of the infrastructure are the API server, daemon running inside instances (sandboxes), Nomad driver for managing instances (sandboxes), and Nomad driver for building environments (templates).

LitServe is a high-throughput serving engine designed for deploying AI models at scale. It generates an API endpoint for models, handles batching, streaming, and autoscaling across CPU/GPUs. LitServe is built for enterprise scale with a focus on minimal, hackable code-base without bloat. It supports various model types like LLMs, vision, time-series, and works with frameworks like PyTorch, JAX, Tensorflow, and more. The tool allows users to focus on model performance rather than serving boilerplate, providing full control and flexibility.

E2B Sandbox is a secure sandboxed cloud environment made for AI agents and AI apps. Sandboxes allow AI agents and apps to have long running cloud secure environments. In these environments, large language models can use the same tools as humans do. For example: * Cloud browsers * GitHub repositories and CLIs * Coding tools like linters, autocomplete, "go-to defintion" * Running LLM generated code * Audio & video editing The E2B sandbox can be connected to any LLM and any AI agent or app.

This open-source project, also known as **Deep Learning System** or **AI System (AISys)**, aims to explore and learn about the system design of artificial intelligence and deep learning. The project is centered around the full-stack content of AI systems that ZOMI has accumulated,整理, and built during his work. The goal is to collaborate with all friends who are interested in AI open-source projects to jointly promote learning and discussion.

Jina is a tool that allows users to build multimodal AI services and pipelines using cloud-native technologies. It provides a Pythonic experience for serving ML models and transitioning from local deployment to advanced orchestration frameworks like Docker-Compose, Kubernetes, or Jina AI Cloud. Users can build and serve models for any data type and deep learning framework, design high-performance services with easy scaling, serve LLM models while streaming their output, integrate with Docker containers via Executor Hub, and host on CPU/GPU using Jina AI Cloud. Jina also offers advanced orchestration and scaling capabilities, a smooth transition to the cloud, and easy scalability and concurrency features for applications. Users can deploy to their own cloud or system with Kubernetes and Docker Compose integration, and even deploy to JCloud for autoscaling and monitoring.

FedML is a unified and scalable machine learning library for running training and deployment anywhere at any scale. It is highly integrated with FEDML Nexus AI, a next-gen cloud service for LLMs & Generative AI. FEDML Nexus AI provides holistic support of three interconnected AI infrastructure layers: user-friendly MLOps, a well-managed scheduler, and high-performance ML libraries for running any AI jobs across GPU Clouds.

Pathway's LLM (Large Language Model) Apps provide a platform to quickly deploy AI applications using the latest knowledge from data sources. The Python application examples in this repository are Docker-ready, exposing an HTTP API to the frontend. These apps utilize the Pathway framework for data synchronization, API serving, and low-latency data processing without the need for additional infrastructure dependencies. They connect to document data sources like S3, Google Drive, and Sharepoint, offering features like real-time data syncing, easy alert setup, scalability, monitoring, security, and unification of application logic.

AIGES is a core component of the Athena Serving Framework, designed as a universal encapsulation tool for AI developers to deploy AI algorithm models and engines quickly. By integrating AIGES, you can deploy AI algorithm models and engines rapidly and host them on the Athena Serving Framework, utilizing supporting auxiliary systems for networking, distribution strategies, data processing, etc. The Athena Serving Framework aims to accelerate the cloud service of AI algorithm models and engines, providing multiple guarantees for cloud service stability through cloud-native architecture. You can efficiently and securely deploy, upgrade, scale, operate, and monitor models and engines without focusing on underlying infrastructure and service-related development, governance, and operations.

This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources

OpenOrch is a daemon that transforms servers into a powerful development environment, running AI models, containers, and microservices. It serves as a blend of Kubernetes and a language-agnostic backend framework for building applications on fixed-resource setups. Users can deploy AI models and build microservices, managing applications while retaining control over infrastructure and data.

AI System School is a curated list of research in machine learning systems, focusing on ML/DL infra, LLM infra, domain-specific infra, ML/LLM conferences, and general resources. It provides resources such as data processing, training systems, video systems, autoML systems, and more. The repository aims to help users navigate the landscape of AI systems and machine learning infrastructure, offering insights into conferences, surveys, books, videos, courses, and blogs related to the field.

AI-in-a-Box is a curated collection of solution accelerators that can help engineers establish their AI/ML environments and solutions rapidly and with minimal friction, while maintaining the highest standards of quality and efficiency. It provides essential guidance on the responsible use of AI and LLM technologies, specific security guidance for Generative AI (GenAI) applications, and best practices for scaling OpenAI applications within Azure. The available accelerators include: Azure ML Operationalization in-a-box, Edge AI in-a-box, Doc Intelligence in-a-box, Image and Video Analysis in-a-box, Cognitive Services Landing Zone in-a-box, Semantic Kernel Bot in-a-box, NLP to SQL in-a-box, Assistants API in-a-box, and Assistants API Bot in-a-box.

Dust is a platform that provides customizable and secure AI assistants to amplify your team's potential. With Dust, you can build and deploy AI assistants that are tailored to your specific needs, without the need for extensive technical expertise. Dust's platform is easy to use and provides a variety of features to help you get started quickly, including a library of pre-built blocks, a developer platform, and an API reference.

GPT4DFCI is a private and secure generative AI tool based on GPT-4, deployed for non-clinical use at Dana-Farber Cancer Institute. The tool is overseen by the Dana-Farber AI Governance Committee and developed by the Dana-Farber Informatics & Analytics Department. The repository includes manuscript & policy details, training material, front-end and back-end code, infrastructure information, API client for programmatic use, licensing details, and contact information.

The Enterprise Azure OpenAI Hub is a comprehensive repository designed to guide users through the world of Generative AI on the Azure platform. It offers a structured learning experience to accelerate the transition from concept to production in an Enterprise context. The hub empowers users to explore various use cases with Azure services, ensuring security and compliance. It provides real-world examples and playbooks for practical insights into solving complex problems and developing cutting-edge AI solutions. The repository also serves as a library of proven patterns, aligning with industry standards and promoting best practices for secure and compliant AI development.

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.

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