Best AI tools for< Deploy Containers >

Microsoft Azure is a cloud computing service that offers a wide range of products and services for businesses and developers. It provides global infrastructure, FinOps capabilities, customer stories, and innovation insights. Azure features include virtual machines, AI services, Kubernetes service, Cosmos DB, and more. The platform supports hybrid and multicloud solutions, analytics, application development, and modernization. Azure also offers resources, pricing tools, and partner programs. With a focus on AI and machine learning, Azure enables responsible AI development and secure cloud solutions. The platform caters to IT professionals, developers, data analysts, business leaders, startups, and students, offering a comprehensive suite of tools and services.

Salad is a distributed GPU cloud platform that offers fully managed and massively scalable services for AI applications. It provides the lowest priced AI transcription in the market, with features like image generation, voice AI, computer vision, data collection, and batch processing. Salad democratizes cloud computing by leveraging consumer GPUs to deliver cost-effective AI/ML inference at scale. The platform is trusted by hundreds of machine learning and data science teams for its affordability, scalability, and ease of deployment.

Modal is a high-performance cloud platform designed for developers, AI data, and ML teams. It offers a serverless environment for running generative AI models, large-scale batch jobs, job queues, and more. With Modal, users can bring their own code and leverage the platform's optimized container file system for fast cold boots and seamless autoscaling. The platform is engineered for large-scale workloads, allowing users to scale to hundreds of GPUs, pay only for what they use, and deploy functions to the cloud in seconds without the need for YAML or Dockerfiles. Modal also provides features for job scheduling, web endpoints, observability, and security compliance.

Kubeflow is an open-source machine learning (ML) toolkit that makes deploying ML workflows on Kubernetes simple, portable, and scalable. It provides a unified interface for model training, serving, and hyperparameter tuning, and supports a variety of popular ML frameworks including PyTorch, TensorFlow, and XGBoost. Kubeflow is designed to be used with Kubernetes, a container orchestration system that automates the deployment, management, and scaling of containerized applications.

GrapixAI is a leading provider of low-cost cloud GPU rental services and AI server solutions. The company's focus on flexibility, scalability, and cutting-edge technology enables a variety of AI applications in both local and cloud environments. GrapixAI offers the lowest prices for on-demand GPUs such as RTX4090, RTX 3090, RTX A6000, RTX A5000, and A40. The platform provides Docker-based container ecosystem for quick software setup, powerful GPU search console, customizable pricing options, various security levels, GUI and CLI interfaces, real-time bidding system, and personalized customer support.

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.

Contentable.ai is a platform for comparing multiple AI models, rapidly moving from prototyping to production, and management of your custom AI solutions across multiple vendors. It allows users to test multiple AI models in seconds, compare models side-by-side across top AI providers, collaborate on AI models with their team seamlessly, design complex AI workflows without coding, and pay as they go.

Sandbox is an open-source cloud-based code editing environment with custom AI code autocompletion and real-time collaboration. It consists of a frontend built with Next.js, TailwindCSS, Shadcn UI, Clerk, Monaco, and Liveblocks, and a backend with Express, Socket.io, Cloudflare Workers, D1 database, R2 storage, Workers AI, and Drizzle ORM. The backend includes microservices for database, storage, and AI functionalities. Users can run the project locally by setting up environment variables and deploying the containers. Contributions are welcome following the commit convention and structure provided in the repository.

KitOps is a packaging and versioning system for AI/ML projects that uses open standards so it works with the AI/ML, development, and DevOps tools you are already using. KitOps simplifies the handoffs between data scientists, application developers, and SREs working with LLMs and other AI/ML models. KitOps' ModelKits are a standards-based package for models, their dependencies, configurations, and codebases. ModelKits are portable, reproducible, and work with the tools you already use.

A collection of LLM services you can self host via docker or modal labs to support your applications development. The goal is to provide docker containers or modal labs deployments of common patterns when using LLMs and endpoints to integrate easily with existing codebases using the openai api. It supports GPT4all's embedding api, JSONFormer api for chat completion, Cross Encoders based on sentence transformers, and provides documentation using MkDocs.

LocalAI is a free and open-source OpenAI alternative that acts as a drop-in replacement REST API compatible with OpenAI (Elevenlabs, Anthropic, etc.) API specifications for local AI inferencing. It allows users to run LLMs, generate images, audio, and more locally or on-premises with consumer-grade hardware, supporting multiple model families and not requiring a GPU. LocalAI offers features such as text generation with GPTs, text-to-audio, audio-to-text transcription, image generation with stable diffusion, OpenAI functions, embeddings generation for vector databases, constrained grammars, downloading models directly from Huggingface, and a Vision API. It provides a detailed step-by-step introduction in its Getting Started guide and supports community integrations such as custom containers, WebUIs, model galleries, and various bots for Discord, Slack, and Telegram. LocalAI also offers resources like an LLM fine-tuning guide, instructions for local building and Kubernetes installation, projects integrating LocalAI, and a how-tos section curated by the community. It encourages users to cite the repository when utilizing it in downstream projects and acknowledges the contributions of various software from the community.

The LLM Hosting Container repository provides Dockerfile and associated resources for building and hosting containers for large language models, specifically the HuggingFace Text Generation Inference (TGI) container. This tool allows users to easily deploy and manage large language models in a containerized environment, enabling efficient inference and deployment of language-based applications.

Beta9 is an open-source platform for running scalable serverless GPU workloads across cloud providers. It allows users to scale out workloads to thousands of GPU or CPU containers, achieve ultrafast cold-start for custom ML models, automatically scale to zero to pay for only what is used, utilize flexible distributed storage, distribute workloads across multiple cloud providers, and easily deploy task queues and functions using simple Python abstractions. The platform is designed for launching remote serverless containers quickly, featuring a custom, lazy loading image format backed by S3/FUSE, a fast redis-based container scheduling engine, content-addressed storage for caching images and files, and a custom runc container runtime.

Farfalle is an open-source AI-powered search engine that allows users to run their own local LLM or utilize the cloud. It provides a tech stack including Next.js for frontend, FastAPI for backend, Tavily for search API, Logfire for logging, and Redis for rate limiting. Users can get started by setting up prerequisites like Docker and Ollama, and obtaining API keys for Tavily, OpenAI, and Groq. The tool supports models like llama3, mistral, and gemma. Users can clone the repository, set environment variables, run containers using Docker Compose, and deploy the backend and frontend using services like Render and Vercel.

OpenLLM is a platform that helps developers run any open-source Large Language Models (LLMs) as OpenAI-compatible API endpoints, locally and in the cloud. It supports a wide range of LLMs, provides state-of-the-art serving and inference performance, and simplifies cloud deployment via BentoML. Users can fine-tune, serve, deploy, and monitor any LLMs with ease using OpenLLM. The platform also supports various quantization techniques, serving fine-tuning layers, and multiple runtime implementations. OpenLLM seamlessly integrates with other tools like OpenAI Compatible Endpoints, LlamaIndex, LangChain, and Transformers Agents. It offers deployment options through Docker containers, BentoCloud, and provides a community for collaboration and contributions.

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.

ClearML Server is a backend service infrastructure for ClearML, facilitating collaboration and experiment management. It includes a web app, RESTful API, and file server for storing images and models. Users can deploy ClearML Server using Docker, AWS EC2 AMI, or Kubernetes. The system design supports single IP or sub-domain configurations with specific open ports. ClearML-Agent Services container allows launching long-lasting jobs and various use cases like auto-scaler service, controllers, optimizer, and applications. Advanced functionality includes web login authentication and non-responsive experiments watchdog. Upgrading ClearML Server involves stopping containers, backing up data, downloading the latest docker-compose.yml file, configuring ClearML-Agent Services, and spinning up docker containers. Community support is available through ClearML FAQ, Stack Overflow, GitHub issues, and email contact.

This Helm chart bootstraps an Airflow deployment on a Kubernetes cluster using the Helm package manager. The version of this chart does not correlate to any other component. Users should not expect feature parity between OSS airflow chart and the Astronomer airflow-chart for identical version numbers. To install this helm chart remotely (using helm 3) kubectl create namespace airflow helm repo add astronomer https://helm.astronomer.io helm install airflow --namespace airflow astronomer/airflow To install this repository from source sh kubectl create namespace airflow helm install --namespace airflow . Prerequisites: Kubernetes 1.12+ Helm 3.6+ PV provisioner support in the underlying infrastructure Installing the Chart: sh helm install --name my-release . The command deploys Airflow on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured during installation. Upgrading the Chart: First, look at the updating documentation to identify any backwards-incompatible changes. To upgrade the chart with the release name `my-release`: sh helm upgrade --name my-release . Uninstalling the Chart: To uninstall/delete the `my-release` deployment: sh helm delete my-release The command removes all the Kubernetes components associated with the chart and deletes the release. Updating DAGs: Bake DAGs in Docker image The recommended way to update your DAGs with this chart is to build a new docker image with the latest code (`docker build -t my-company/airflow:8a0da78 .`), push it to an accessible registry (`docker push my-company/airflow:8a0da78`), then update the Airflow pods with that image: sh helm upgrade my-release . --set images.airflow.repository=my-company/airflow --set images.airflow.tag=8a0da78 Docker Images: The Airflow image that are referenced as the default values in this chart are generated from this repository: https://github.com/astronomer/ap-airflow. Other non-airflow images used in this chart are generated from this repository: https://github.com/astronomer/ap-vendor. Parameters: The complete list of parameters supported by the community chart can be found on the Parameteres Reference page, and can be set under the `airflow` key in this chart. The following tables lists the configurable parameters of the Astronomer chart and their default values. | Parameter | Description | Default | | :----------------------------- | :-------------------------------------------------------------------------------------------------------- | :---------------------------- | | `ingress.enabled` | Enable Kubernetes Ingress support | `false` | | `ingress.acme` | Add acme annotations to Ingress object | `false` | | `ingress.tlsSecretName` | Name of secret that contains a TLS secret | `~` | | `ingress.webserverAnnotations` | Annotations added to Webserver Ingress object | `{}` | | `ingress.flowerAnnotations` | Annotations added to Flower Ingress object | `{}` | | `ingress.baseDomain` | Base domain for VHOSTs | `~` | | `ingress.auth.enabled` | Enable auth with Astronomer Platform | `true` | | `extraObjects` | Extra K8s Objects to deploy (these are passed through `tpl`). More about Extra Objects. | `[]` | | `sccEnabled` | Enable security context constraints required for OpenShift | `false` | | `authSidecar.enabled` | Enable authSidecar | `false` | | `authSidecar.repository` | The image for the auth sidecar proxy | `nginxinc/nginx-unprivileged` | | `authSidecar.tag` | The image tag for the auth sidecar proxy | `stable` | | `authSidecar.pullPolicy` | The K8s pullPolicy for the the auth sidecar proxy image | `IfNotPresent` | | `authSidecar.port` | The port the auth sidecar exposes | `8084` | | `gitSyncRelay.enabled` | Enables git sync relay feature. | `False` | | `gitSyncRelay.repo.url` | Upstream URL to the git repo to clone. | `~` | | `gitSyncRelay.repo.branch` | Branch of the upstream git repo to checkout. | `main` | | `gitSyncRelay.repo.depth` | How many revisions to check out. Leave as default `1` except in dev where history is needed. | `1` | | `gitSyncRelay.repo.wait` | Seconds to wait before pulling from the upstream remote. | `60` | | `gitSyncRelay.repo.subPath` | Path to the dags directory within the git repository. | `~` | Specify each parameter using the `--set key=value[,key=value]` argument to `helm install`. For example, sh helm install --name my-release --set executor=CeleryExecutor --set enablePodLaunching=false . Walkthrough using kind: Install kind, and create a cluster We recommend testing with Kubernetes 1.25+, example: sh kind create cluster --image kindest/node:v1.25.11 Confirm it's up: sh kubectl cluster-info --context kind-kind Add Astronomer's Helm repo sh helm repo add astronomer https://helm.astronomer.io helm repo update Create namespace + install the chart sh kubectl create namespace airflow helm install airflow -n airflow astronomer/airflow It may take a few minutes. Confirm the pods are up: sh kubectl get pods --all-namespaces helm list -n airflow Run `kubectl port-forward svc/airflow-webserver 8080:8080 -n airflow` to port-forward the Airflow UI to http://localhost:8080/ to confirm Airflow is working. Login as _admin_ and password _admin_. Build a Docker image from your DAGs: 1. Start a project using astro-cli, which will generate a Dockerfile, and load your DAGs in. You can test locally before pushing to kind with `astro airflow start`. `sh mkdir my-airflow-project && cd my-airflow-project astro dev init` 2. Then build the image: `sh docker build -t my-dags:0.0.1 .` 3. Load the image into kind: `sh kind load docker-image my-dags:0.0.1` 4. Upgrade Helm deployment: sh helm upgrade airflow -n airflow --set images.airflow.repository=my-dags --set images.airflow.tag=0.0.1 astronomer/airflow Extra Objects: This chart can deploy extra Kubernetes objects (assuming the role used by Helm can manage them). For Astronomer Cloud and Enterprise, the role permissions can be found in the Commander role. yaml extraObjects: - apiVersion: batch/v1beta1 kind: CronJob metadata: name: "{{ .Release.Name }}-somejob" spec: schedule: "*/10 * * * *" concurrencyPolicy: Forbid jobTemplate: spec: template: spec: containers: - name: myjob image: ubuntu command: - echo args: - hello restartPolicy: OnFailure Contributing: Check out our contributing guide! License: Apache 2.0 with Commons Clause

Cog is an open-source tool that lets you package machine learning models in a standard, production-ready container. You can deploy your packaged model to your own infrastructure, or to Replicate.

The 'ai-dev-2024-ml-workshop' repository contains materials for the Deploy and Monitor ML Pipelines workshop at the AI_dev 2024 conference in Paris, focusing on deployment designs of machine learning pipelines using open-source applications and free-tier tools. It demonstrates automating data refresh and forecasting using GitHub Actions and Docker, monitoring with MLflow and YData Profiling, and setting up a monitoring dashboard with Quarto doc on GitHub Pages.

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.

Tau is a framework for building low maintenance & highly scalable cloud computing platforms that software developers will love. It aims to solve the high cost and time required to build, deploy, and scale software by providing a developer-friendly platform that offers autonomy and flexibility. Tau simplifies the process of building and maintaining a cloud computing platform, enabling developers to achieve 'Local Coding Equals Global Production' effortlessly. With features like auto-discovery, content-addressing, and support for WebAssembly, Tau empowers users to create serverless computing environments, host frontends, manage databases, and more. The platform also supports E2E testing and can be extended using a plugin system called orbit.

This repository showcases how to deploy generative AI models from Amazon SageMaker JumpStart using the AWS CDK. Generative AI is a type of AI that can create new content and ideas, such as conversations, stories, images, videos, and music. The repository provides a detailed guide on deploying image and text generative AI models, utilizing pre-trained models from SageMaker JumpStart. The web application is built on Streamlit and hosted on Amazon ECS with Fargate. It interacts with the SageMaker model endpoints through Lambda functions and Amazon API Gateway. The repository also includes instructions on setting up the AWS CDK application, deploying the stacks, using the models, and viewing the deployed resources on the AWS Management Console.

CB-Tumblebug (CB-TB) is a system for managing multi-cloud infrastructure consisting of resources from multiple cloud service providers. It provides an overview, features, and architecture. The tool supports various cloud providers and resource types, with ongoing development and localization efforts. Users can deploy a multi-cloud infra with GPUs, enjoy multiple LLMs in parallel, and utilize LLM-related scripts. The tool requires Linux, Docker, Docker Compose, and Golang for building the source. Users can run CB-TB with Docker Compose or from the Makefile, set up prerequisites, contribute to the project, and view a list of contributors. The tool is licensed under an open-source license.

LangServe helps developers deploy `LangChain` runnables and chains as a REST API. This library is integrated with FastAPI and uses pydantic for data validation. In addition, it provides a client that can be used to call into runnables deployed on a server. A JavaScript client is available in LangChain.js.

NVIDIA Generative AI Examples are state-of-the-art examples that are easy to deploy, test, and extend. All examples run on the high performance NVIDIA CUDA-X software stack and NVIDIA GPUs. These examples showcase the capabilities of NVIDIA's Generative AI platform, which includes tools, frameworks, and models for building and deploying generative AI applications.

Flyte is an open-source orchestrator that facilitates building production-grade data and ML pipelines. It is built for scalability and reproducibility, leveraging Kubernetes as its underlying platform. With Flyte, user teams can construct pipelines using the Python SDK, and seamlessly deploy them on both cloud and on-premises environments, enabling distributed processing and efficient resource utilization.

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