Best AI tools for< Deploy Data Applications >

Streamlit is a web application framework that allows users to create interactive web applications effortlessly using Python. It enables data scientists and developers to build and deploy data-driven applications quickly and easily. With Streamlit, users can create interactive visualizations, dashboards, and machine learning models without the need for extensive web development knowledge. The platform provides a simple and intuitive way to turn data scripts into shareable web apps, making it ideal for prototyping, showcasing projects, and sharing insights with others.

Streamlit is a web application framework that allows data scientists and machine learning engineers to create interactive web applications quickly and easily. It simplifies the process of building and sharing data-focused applications by providing a simple Python script that can be turned into a shareable web app with just a few lines of code. With Streamlit, users can create interactive visualizations, dashboards, and machine learning models without the need for web development expertise.

SID is a data ingestion, storage, and retrieval pipeline that provides real-time context for AI applications. It connects to various data sources, handles authentication and permission flows, and keeps information up-to-date. SID's API allows developers to retrieve the right piece of data for a given task, enabling them to build AI apps that are fast, accurate, and scalable. With SID, developers can focus on building their products and leave the data management to SID.

Amazon Web Services (AWS) is a comprehensive, evolving cloud computing platform from Amazon that provides a broad set of global compute, storage, database, analytics, application, and deployment services that help organizations move faster, lower IT costs, and scale applications. With AWS, you can use as much or as little of its services as you need, and scale up or down as required with only a few minutes notice. AWS has a global network of regions and availability zones, so you can deploy your applications and data in the locations that are optimal for you.

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.

SingleStore is a real-time data platform designed for apps, analytics, and gen AI. It offers faster hybrid vector + full-text search, fast-scaling integrations, and a free tier. SingleStore can read, write, and reason on petabyte-scale data in milliseconds. It supports streaming ingestion, high concurrency, first-class vector support, record lookups, and more.

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.

Helix AI is a private GenAI platform that enables users to build AI applications using open source models. The platform offers tools for RAG (Retrieval-Augmented Generation) and fine-tuning, allowing deployment on-premises or in a Virtual Private Cloud (VPC). Users can access curated models, utilize Helix API tools to connect internal and external APIs, embed Helix Assistants into websites/apps for chatbot functionality, write AI application logic in natural language, and benefit from the innovative RAG system for Q&A generation. Additionally, users can fine-tune models for domain-specific needs and deploy securely on Kubernetes or Docker in any cloud environment. Helix Cloud offers free and premium tiers with GPU priority, catering to individuals, students, educators, and companies of varying sizes.

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.

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.

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.

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.

BentoML is a platform for software engineers to build, ship, and scale AI products. It provides a unified AI application framework that makes it easy to manage and version models, create service APIs, and build and run AI applications anywhere. BentoML is used by over 1000 organizations and has a global community of over 3000 members.

Dynamiq is an operating platform for GenAI applications that enables users to build compliant GenAI applications in their own infrastructure. It offers a comprehensive suite of features including rapid prototyping, testing, deployment, observability, and model fine-tuning. The platform helps streamline the development cycle of AI applications and provides tools for workflow automations, knowledge base management, and collaboration. Dynamiq is designed to optimize productivity, reduce AI adoption costs, and empower organizations to establish AI ahead of schedule.

Myple is an AI application that enables users to build, scale, and secure AI applications with ease. It provides production-ready AI solutions tailored to individual needs, offering a seamless user experience. With support for multiple languages and frameworks, Myple simplifies the integration of AI through open-source SDKs. The platform features a clean interface, keyboard shortcuts for efficient navigation, and templates to kickstart AI projects. Additionally, Myple offers AI-powered tools like RAG chatbot for documentation, Gmail agent for email notifications, and AskFeynman for physics-related queries. Users can connect their favorite tools and services effortlessly, without any coding. Joining the beta program grants early access to new features and issue resolution prioritization.

LangChain is a framework for developing applications powered by large language models (LLMs). It simplifies every stage of the LLM application lifecycle, including development, productionization, and deployment. LangChain consists of open-source libraries such as langchain-core, langchain-community, and partner packages. It also includes LangGraph for building stateful agents and LangSmith for debugging and monitoring LLM applications.

Liner is a free and easy-to-use tool that allows users to train machine learning models without writing any code. It provides a user-friendly interface that guides users through the process of importing data, selecting a model, and training the model. Liner also offers a variety of pre-trained models that can be used for common tasks such as image classification, text classification, and object detection. With Liner, users can quickly and easily create and deploy machine learning applications without the need for specialized knowledge or expertise.

API Fabric is an AI API Generator that allows users to easily create and deploy APIs for their applications. With a user-friendly interface, API Fabric simplifies the process of generating APIs by providing pre-built templates and customization options. Users can quickly integrate AI capabilities into their projects without the need for extensive coding knowledge. The platform supports various AI models and algorithms, making it versatile for different use cases. API Fabric streamlines the API development process, saving time and effort for developers.

Vellum AI is an AI platform that supports using Microsoft Azure hosted OpenAI models. It offers tools for prompt engineering, semantic search, prompt chaining, evaluations, and monitoring. Vellum enables users to build AI systems with features like workflow automation, document analysis, fine-tuning, Q&A over documents, intent classification, summarization, vector search, chatbots, blog generation, sentiment analysis, and more. The platform is backed by top VCs and founders of well-known companies, providing a complete solution for building LLM-powered applications.

JFrog ML is an AI platform designed to streamline AI development from prototype to production. It offers a unified MLOps platform to build, train, deploy, and manage AI workflows at scale. With features like Feature Store, LLMOps, and model monitoring, JFrog ML empowers AI teams to collaborate efficiently and optimize AI & ML models in production.

DataAgent is a multi-agent system for data analysis, capable of understanding data development and data analysis requirements, understanding data, and generating SQL and Python code for tasks such as data query, data visualization, and machine learning.

A curated list of 50+ resources to help you become a Generative AI Data Scientist. This repository includes resources on building GenAI applications with Large Language Models (LLMs), and deploying LLMs and GenAI with Cloud-based solutions.

Preswald is a full-stack platform for building, deploying, and managing interactive data applications in Python. It simplifies the process by combining ingestion, storage, transformation, and visualization into one lightweight SDK. With Preswald, users can connect to various data sources, customize app themes, and easily deploy apps locally. The platform focuses on code-first simplicity, end-to-end coverage, and efficiency by design, making it suitable for prototyping internal tools or deploying production-grade apps with reduced complexity and cost.

Buster is a modern analytics platform designed with AI in mind, focusing on self-serve experiences powered by Large Language Models. It addresses pain points in existing tools by advocating for AI-centric app development, cost-effective data warehousing, improved CI/CD processes, and empowering data teams to create powerful, user-friendly data experiences. The platform aims to revolutionize AI analytics by enabling data teams to build deep integrations and own their entire analytics stack.

This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their variants. It includes tutorials, papers, tools, frameworks, and best practices to aid researchers, data scientists, and machine learning practitioners in adapting pre-trained models to specific tasks and domains. The resources cover a wide range of topics related to fine-tuning LLMs, providing valuable insights and guidelines to streamline the process and enhance model performance.

Helicone is an open-source observability platform designed for Language Learning Models (LLMs). It logs requests to OpenAI in a user-friendly UI, offers caching, rate limits, and retries, tracks costs and latencies, provides a playground for iterating on prompts and chat conversations, supports collaboration, and will soon have APIs for feedback and evaluation. The platform is deployed on Cloudflare and consists of services like Web (NextJs), Worker (Cloudflare Workers), Jawn (Express), Supabase, and ClickHouse. Users can interact with Helicone locally by setting up the required services and environment variables. The platform encourages contributions and provides resources for learning, documentation, and integrations.

Robocorp is a platform that allows users to create, deploy, and operate Python automations and AI actions. It provides an easy way to extend the capabilities of AI agents, assistants, and copilots with custom actions written in Python. Users can create and deploy tools, skills, loaders, and plugins that securely connect any AI Assistant platform to their data and applications. The Robocorp Action Server makes Python scripts compatible with ChatGPT and LangChain by automatically creating and exposing an API based on function declaration, type hints, and docstrings. It simplifies the process of developing and deploying AI actions, enabling users to interact with AI frameworks effortlessly.

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.

This repository is for the book 'Building AI Applications with ChatGPT APIs' published by Packt. It provides code examples and instructions for mastering ChatGPT, Whisper, and DALL-E APIs through building innovative AI projects. Readers will learn to develop AI applications using ChatGPT APIs, integrate them with frameworks like Flask and Django, create AI-generated art with DALL-E APIs, and optimize ChatGPT models through fine-tuning.

Generative AI with LangChain is a code repository for building large language model (LLM) apps with Python, ChatGPT, and other LLMs. The repository provides code examples, instructions, and configurations for creating generative AI applications using the LangChain framework. It covers topics such as setting up the development environment, installing dependencies with Conda or Pip, using Docker for environment setup, and setting API keys securely. The repository also emphasizes stability, code updates, and user engagement through issue reporting and feedback. It aims to empower users to leverage generative AI technologies for tasks like building chatbots, question-answering systems, software development aids, and data analysis applications.

Spice is a portable runtime written in Rust that offers developers a unified SQL interface to materialize, accelerate, and query data from any database, data warehouse, or data lake. It connects, fuses, and delivers data to applications, machine-learning models, and AI-backends, functioning as an application-specific, tier-optimized Database CDN. Built with industry-leading technologies such as Apache DataFusion, Apache Arrow, Apache Arrow Flight, SQLite, and DuckDB. Spice makes it fast and easy to query data from one or more sources using SQL, co-locating a managed dataset with applications or machine learning models, and accelerating it with Arrow in-memory, SQLite/DuckDB, or attached PostgreSQL for fast, high-concurrency, low-latency queries.

Sema4.ai Action Server is a tool that allows users to build semantic actions in Python to connect AI agents with real-world applications. It enables users to create custom actions, skills, loaders, and plugins that securely connect any AI Assistant platform to data and applications. The tool automatically creates and exposes an API based on function declaration, type hints, and docstrings by adding '@action' to Python scripts. It provides an end-to-end stack supporting various connections between AI and user's apps and data, offering ease of use, security, and scalability.

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ChatOpsLLM is a project designed to empower chatbots with effortless DevOps capabilities. It provides an intuitive interface and streamlined workflows for managing and scaling language models. The project incorporates robust MLOps practices, including CI/CD pipelines with Jenkins and Ansible, monitoring with Prometheus and Grafana, and centralized logging with the ELK stack. Developers can find detailed documentation and instructions on the project's website.

TagUI is an open-source RPA tool that allows users to automate repetitive tasks on their computer, including tasks on websites, desktop apps, and the command line. It supports multiple languages and offers features like interacting with identifiers, automating data collection, moving data between TagUI and Excel, and sending Telegram notifications. Users can create RPA robots using MS Office Plug-ins or text editors, run TagUI on the cloud, and integrate with other RPA tools. TagUI prioritizes enterprise security by running on users' computers and not storing data. It offers detailed logs, enterprise installation guides, and support for centralised reporting.

SuperDuperDB is a Python framework for integrating AI models, APIs, and vector search engines directly with your existing databases, including hosting of your own models, streaming inference and scalable model training/fine-tuning. Build, deploy and manage any AI application without the need for complex pipelines, infrastructure as well as specialized vector databases, and moving our data there, by integrating AI at your data's source: - Generative AI, LLMs, RAG, vector search - Standard machine learning use-cases (classification, segmentation, regression, forecasting recommendation etc.) - Custom AI use-cases involving specialized models - Even the most complex applications/workflows in which different models work together SuperDuperDB is **not** a database. Think `db = superduper(db)`: SuperDuperDB transforms your databases into an intelligent platform that allows you to leverage the full AI and Python ecosystem. A single development and deployment environment for all your AI applications in one place, fully scalable and easy to manage.

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.

Xyne is an AI-first Search & Answer Engine for work, serving as an OSS alternative to Glean, Gemini, and MS Copilot. It securely indexes data from various applications like Google Workspace, Atlassian suite, Slack, and Github, providing a Google + ChatGPT-like experience to find information and get up-to-date answers. Users can easily locate files, triage issues, inquire about customers/deals/features/tickets, and discover relevant contacts. Xyne enhances AI models by providing contextual information in a secure, private, and responsible manner, making it the most secure and future-proof solution for integrating AI into work environments.

FoundationaLLM is a platform designed for deploying, scaling, securing, and governing generative AI in enterprises. It allows users to create AI agents grounded in enterprise data, integrate REST APIs, experiment with large language models, centrally manage AI agents and assets, deploy scalable vectorization data pipelines, enable non-developer users to create their own AI agents, control access with role-based access controls, and harness capabilities from Azure AI and Azure OpenAI. The platform simplifies integration with enterprise data sources, provides fine-grain security controls, load balances across multiple endpoints, and is extensible to new data sources and orchestrators. FoundationaLLM addresses the need for customized copilots or AI agents that are secure, licensed, flexible, and suitable for enterprise-scale production.

Guides in data engineering tasks with a focus on practical solutions.

A formal Python teacher with a focus on industry applications.

Expert in backend development and file handling.

Your Dedicated Assistant for Streamlined Python and Django App Development

Your wise R Shiny sage, offering enlightened guidance.

This GPT can read all Streamlit Documantation and helps you about Streamlit.

I'm here to make your Streamlit apps go viral!

Expert in cloud-native solutions, provides tailored tech advice and cost estimates.

日本語Nutanix Bibleから回答するチャット。

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

Friendly guide for GPT pipeline development

Expert in all aspects of Google Cloud Platform.

Adaptable full-stack dev guide, expert in Python and R.

Chat with my resume to see if I am a good fit for your AI related job.

Your go-to Django development assistant.

Friendly Azure guide for beginners

Evolves with user interactions in data science.

I create React apps with Fireproof.

Compresses & decompresses sparse priming data.

I will create a quality data set for a personality AI, just dive into each module by saying the name of it and do so for all the modules. If you find it useful, share it to your friends