Best AI tools for< Manage Data Lifecycle >

One Data is an AI-powered data product builder that offers a comprehensive solution for building, managing, and sharing data products. It bridges the gap between IT and business by providing AI-powered workflows, lifecycle management, data quality assurance, and data governance features. The platform enables users to easily create, access, and share data products with automated processes and quality alerts. One Data is trusted by enterprises and aims to streamline data product management and accessibility through Data Mesh or Data Fabric approaches, enhancing efficiency in logistics and supply chains. The application is designed to accelerate business impact with reliable data products and support cost reduction initiatives with advanced analytics and collaboration for innovative business models.

Baseten is a machine learning infrastructure that provides a unified platform for data scientists and engineers to build, train, and deploy machine learning models. It offers a range of features to simplify the ML lifecycle, including data preparation, model training, and deployment. Baseten also provides a marketplace of pre-built models and components that can be used to accelerate the development of ML applications.

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.

Monitaur is an AI governance software that provides a comprehensive platform for organizations to manage the entire lifecycle of their AI systems. It brings together data, governance, risk, and compliance teams onto one platform to mitigate AI risk, leverage full potential, and turn intention into action. Monitaur's SaaS products offer user-friendly workflows that document the lifecycle of AI journey on one platform, providing a single source of truth for AI that stays honest.

DVC Studio is a collaboration tool for machine learning teams. It provides seamless data and model management, experiment tracking, visualization, and automation. DVC Studio is built for ML researchers, practitioners, and managers. It enables model organization and discovery across all ML projects and manages model lifecycle with Git, unifying ML projects with the best DevOps practices. DVC Studio also provides ML experiment tracking, visualization, collaboration, and automation using Git. It applies software engineering and DevOps best-practices to automate ML bookkeeping and model training, enabling easy collaboration and faster iterations.

Azure AI Platform by Microsoft offers a comprehensive suite of artificial intelligence services and tools for developers and businesses. It provides a unified platform for building, training, and deploying AI models, as well as integrating AI capabilities into applications. With a focus on generative AI, multimodal models, and large language models, Azure AI empowers users to create innovative AI-driven solutions across various industries. The platform also emphasizes content safety, scalability, and agility in managing AI projects, making it a valuable resource for organizations looking to leverage AI technologies.

Evisort is an AI-powered contract management software that simplifies contract management at every stage. It offers a complete, AI-native platform for end-to-end contract lifecycle management, including the first large language model built specifically for contracts. Evisort's AI capabilities enable users to ask questions about their contracts in simple, natural language and get clear, reasoned answers. It can also track terms of interest across all contracts and related documents, and generate data points that matter for sales, procurement, risk, and finance teams. Additionally, Evisort's AI-powered workflows automate tasks such as redlining, clause generation, and contract approvals, saving time and reducing risk.

MLflow is an open source platform for managing the end-to-end machine learning (ML) lifecycle, including tracking experiments, packaging models, deploying models, and managing model registries. It provides a unified platform for both traditional ML and generative AI applications.

MLSecOps is an AI tool designed to drive the field of MLSecOps forward through high-quality educational resources and tools. It focuses on traditional cybersecurity principles, emphasizing people, processes, and technology. The MLSecOps Community educates and promotes the integration of security practices throughout the AI & machine learning lifecycle, empowering members to identify, understand, and manage risks associated with their AI systems.

Prodmap AI is an AI-powered platform that automates the entire product development lifecycle, from roadmap selection and requirement generation to code architecture and release documentation. It helps streamline product development processes, save time, cut costs, and deliver impactful results. The platform offers features such as roadmap generation, faster roadmap planning, AI PRD generation, feature prioritization, real-time project monitoring, proactive risk management, end-to-end visibility, actionable execution insights, knowledge base, integrated tools, strategic initiative insights, custom AI agents, tailored automation, flexible AI solutions, and personalized workflows.

Fraud.net is an AI-powered fraud detection and prevention platform designed for enterprises. It offers a comprehensive and customizable solution to manage and prevent financial fraud and risks. The platform utilizes AI and machine learning technologies to provide real-time monitoring, analytics, and reporting, helping businesses in various industries to combat fraud effectively. Fraud.net's solutions are trusted by CEOs, directors, technology and security officers, fraud managers, and analysts to ensure trust and beat fraud at every step of the customer lifecycle.

fynk is an AI Contract Management Software that offers a comprehensive solution for importing, drafting, reviewing, tracking, signing, analyzing, and managing contracts at scale. It features powerful templating, dynamic content creation, real-time collaboration, electronic signature, and various integrations. The software leverages AI technology for automated contract analysis, reducing manual processes and providing valuable insights throughout the contract lifecycle. fynk is designed to meet the specific requirements of European businesses, ensuring data protection and GDPR compliance. It aims to streamline contract workflows, save time, and enhance productivity for teams across different departments.

DocuSign is an electronic signature and contract lifecycle management company. It offers a suite of applications designed to help businesses of all sizes create, commit to, and manage agreements. DocuSign's Intelligent Agreement Management (IAM) platform leverages AI and integrates with existing business platforms to transform how businesses manage agreements. DocuSign's products and services include eSignature, contract lifecycle management, document generation, web forms, electronic notarization, multi-channel delivery, APIs, and platform services.

Unit21 is a customizable no-code platform designed for risk and compliance operations. It empowers organizations to combat financial crime by providing end-to-end lifecycle risk analysis, fraud prevention, case management, and real-time monitoring solutions. The platform offers features such as AI Copilot for alert prioritization, Ask Your Data for data analysis, Watchlist & Sanctions for ongoing screening, and more. Unit21 focuses on fraud prevention and AML compliance, simplifying operations and accelerating investigations to respond to financial threats effectively and efficiently.

Harriet is an AI-powered employee experience platform designed to engage teams and provide seamless support. It offers instant AI support, HR ticketing system, knowledge management, automated employee lifecycle, people calendar planner, analytics, and insights. Harriet aims to make employees' lives easier by providing quick responses, seamless automation, and accurate information through various communication channels like Slack, Teams, GChat, and SMS. The platform is integrated with HR systems, trained on company policies, and offers 24/7 support. Harriet helps organizations improve productivity, employee satisfaction, and operational efficiency.

Stampli is a leading AP Automation & Invoice Management Software that streamlines financial processes by automating invoice processing, vendor engagement, and expense management. With advanced AI capabilities, Stampli offers fast deployment, easy integration with popular ERPs, and smart features like Billy the Bot for automating manual tasks. Stampli provides visibility and control over the entire invoice lifecycle, making AP automation efficient and accurate. The platform also offers integrated products for payments, vendor management, and insightful analytics for audit readiness.

The Intelligent Logistics Platform, Raft, is an AI-powered workflow automation tool designed for freight forwarders and customs brokers. It streamlines various logistics processes such as finance, customs, and operations by leveraging AI technology to enhance efficiency, accuracy, and customer value throughout the shipment lifecycle. Raft combines cutting-edge AI advancements with a vast amount of supply-chain specific data to provide users with a comprehensive platform for managing logistics operations.

Trazable LifeCycle is a sustainability software designed to measure, improve, and report the sustainability of companies. It simplifies the process of measuring and reporting environmental impact by providing tools to create process maps, add environmental impact data, and generate key sustainability indicators. The software is tailored for the food industry, offering over 50 million industry-specific data points to aid in decision-making and compliance with sustainability regulations. Trazable LifeCycle ensures data validity by using constantly updated and validated datasets, allowing users to measure both product and organizational carbon footprints.

Certa is an all-in-one toolkit for third-party lifecycle management, powered by AI. It streamlines processes by connecting data sources, reducing IT resource needs, and providing full visibility over every stage. Certa offers personalized workflows, tailored automation, modular building blocks, integration with various apps, and smart services like NLP and metadata extraction. It helps automate compliance rules, streamline procurement, and track ESG performance. With 100+ integrations, Certa aims to save time and money for businesses while ensuring audit transparency and security.

Kraftful is an AI-powered tool that helps product teams collect, analyze, and prioritize user feedback. It integrates with various feedback sources, such as surveys, app store reviews, support tickets, and user interviews, to provide a comprehensive view of user needs and pain points. Kraftful uses natural language processing and machine learning to automatically categorize and summarize feedback, making it easy for product teams to identify trends and make data-driven decisions.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

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.

Awesome Responsible AI is a curated list of academic research, books, code of ethics, courses, data sets, frameworks, institutes, newsletters, principles, podcasts, reports, tools, regulations, and standards related to Responsible, Trustworthy, and Human-Centered AI. It covers various concepts such as Responsible AI, Trustworthy AI, Human-Centered AI, Responsible AI frameworks, AI Governance, and more. The repository provides a comprehensive collection of resources for individuals interested in ethical, transparent, and accountable AI development and deployment.

At ZenML, we believe in the importance of annotation and labeling workflows in the machine learning lifecycle. This repository showcases a curated list of open-source data annotation and labeling tools that are actively maintained and fit for purpose. The tools cover various domains such as multi-modal, text, images, audio, video, time series, and other data types. Users can contribute to the list and discover tools for tasks like named entity recognition, data annotation for machine learning, image and video annotation, text classification, sequence labeling, object detection, and more. The repository aims to help users enhance their data-centric workflows by leveraging these tools.

AIStore is a lightweight object storage system designed for AI applications. It is highly scalable, reliable, and easy to use. AIStore can be deployed on any commodity hardware, and it can be used to store and manage large datasets for deep learning and other AI applications.

This course has 18 lessons. Each lesson covers its own topic so start wherever you like! Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both **Python** and **TypeScript** when possible. Each lesson also includes a "Keep Learning" section with additional learning tools. **What You Need** * Access to the Azure OpenAI Service **OR** OpenAI API - _Only required to complete coding lessons_ * Basic knowledge of Python or Typescript is helpful - *For absolute beginners check out these Python and TypeScript courses. * A Github account to fork this entire repo to your own GitHub account We have created a **Course Setup** lesson to help you with setting up your development environment. Don't forget to star (🌟) this repo to find it easier later. ## 🧠 Ready to Deploy? If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both **Python** and **TypeScript**. ## 🗣️ Meet Other Learners, Get Support Join our official AI Discord server to meet and network with other learners taking this course and get support. ## 🚀 Building a Startup? Sign up for Microsoft for Startups Founders Hub to receive **free OpenAI credits** and up to **$150k towards Azure credits to access OpenAI models through Azure OpenAI Services**. ## 🙏 Want to help? Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request ## 📂 Each lesson includes: * A short video introduction to the topic * A written lesson located in the README * Python and TypeScript code samples supporting Azure OpenAI and OpenAI API * Links to extra resources to continue your learning ## 🗃️ Lessons | | Lesson Link | Description | Additional Learning | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 00 | Course Setup | **Learn:** How to Setup Your Development Environment | Learn More | | 01 | Introduction to Generative AI and LLMs | **Learn:** Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More | | 02 | Exploring and comparing different LLMs | **Learn:** How to select the right model for your use case | Learn More | | 03 | Using Generative AI Responsibly | **Learn:** How to build Generative AI Applications responsibly | Learn More | | 04 | Understanding Prompt Engineering Fundamentals | **Learn:** Hands-on Prompt Engineering Best Practices | Learn More | | 05 | Creating Advanced Prompts | **Learn:** How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More | | 06 | Building Text Generation Applications | **Build:** A text generation app using Azure OpenAI | Learn More | | 07 | Building Chat Applications | **Build:** Techniques for efficiently building and integrating chat applications. | Learn More | | 08 | Building Search Apps Vector Databases | **Build:** A search application that uses Embeddings to search for data. | Learn More | | 09 | Building Image Generation Applications | **Build:** A image generation application | Learn More | | 10 | Building Low Code AI Applications | **Build:** A Generative AI application using Low Code tools | Learn More | | 11 | Integrating External Applications with Function Calling | **Build:** What is function calling and its use cases for applications | Learn More | | 12 | Designing UX for AI Applications | **Learn:** How to apply UX design principles when developing Generative AI Applications | Learn More | | 13 | Securing Your Generative AI Applications | **Learn:** The threats and risks to AI systems and methods to secure these systems. | Learn More | | 14 | The Generative AI Application Lifecycle | **Learn:** The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More | | 15 | Retrieval Augmented Generation (RAG) and Vector Databases | **Build:** An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More | | 16 | Open Source Models and Hugging Face | **Build:** An application using open source models available on Hugging Face | Learn More | | 17 | AI Agents | **Build:** An application using an AI Agent Framework | Learn More | | 18 | Fine-Tuning LLMs | **Learn:** The what, why and how of fine-tuning LLMs | Learn More |

LakeSoul is a cloud-native Lakehouse framework that supports scalable metadata management, ACID transactions, efficient and flexible upsert operation, schema evolution, and unified streaming & batch processing. It supports multiple computing engines like Spark, Flink, Presto, and PyTorch, and computing modes such as batch, stream, MPP, and AI. LakeSoul scales metadata management and achieves ACID control by using PostgreSQL. It provides features like automatic compaction, table lifecycle maintenance, redundant data cleaning, and permission isolation for metadata.

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are: * `MLflow Tracking `_: An API to log parameters, code, and results in machine learning experiments and compare them using an interactive UI. * `MLflow Projects `_: A code packaging format for reproducible runs using Conda and Docker, so you can share your ML code with others. * `MLflow Models `_: A model packaging format and tools that let you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as Docker, Apache Spark, Azure ML and AWS SageMaker. * `MLflow Model Registry `_: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.

APIPark is an open-source AI Gateway and Developer Portal that enables users to easily manage, integrate, and deploy AI and API services. It provides robust API management features, including creation, monitoring, and access control, to help developers efficiently and securely develop and manage their APIs. The platform aims to solve challenges such as connecting to powerful AI models, managing complex AI & API call relationships, overseeing API creation and security, simplifying fault detection and troubleshooting, and enhancing the visibility and valuation of data assets.

The Oracle Cloud Infrastructure Data Science and AI services Examples repository provides demos, tutorials, and code examples showcasing various features of the OCI Data Science service and AI services. It offers tools for data scientists to develop and deploy machine learning models efficiently, with features like Accelerated Data Science SDK, distributed training, batch processing, and machine learning pipelines. Whether you're a beginner or an experienced practitioner, OCI Data Science Services provide the resources needed to build, train, and deploy models easily.

QMedia is an open-source multimedia AI content search engine designed specifically for content creators. It provides rich information extraction methods for text, image, and short video content. The tool integrates unstructured text, image, and short video information to build a multimodal RAG content Q&A system. Users can efficiently search for image/text and short video materials, analyze content, provide content sources, and generate customized search results based on user interests and needs. QMedia supports local deployment for offline content search and Q&A for private data. The tool offers features like content cards display, multimodal content RAG search, and pure local multimodal models deployment. Users can deploy different types of models locally, manage language models, feature embedding models, image models, and video models. QMedia aims to spark new ideas for content creation and share AI content creation concepts in an open-source manner.

CSGHub is an open source, trustworthy large model asset management platform that can assist users in governing the assets involved in the lifecycle of LLM and LLM applications (datasets, model files, codes, etc). With CSGHub, users can perform operations on LLM assets, including uploading, downloading, storing, verifying, and distributing, through Web interface, Git command line, or natural language Chatbot. Meanwhile, the platform provides microservice submodules and standardized OpenAPIs, which could be easily integrated with users' own systems. CSGHub is committed to bringing users an asset management platform that is natively designed for large models and can be deployed On-Premise for fully offline operation. CSGHub offers functionalities similar to a privatized Huggingface(on-premise Huggingface), managing LLM assets in a manner akin to how OpenStack Glance manages virtual machine images, Harbor manages container images, and Sonatype Nexus manages artifacts.

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.

LLMOps with Prompt flow is a template and guidance for building LLM-infused apps using Prompt flow. It provides centralized code hosting, lifecycle management, variant and hyperparameter experimentation, A/B deployment, many-to-many dataset/flow relationships, multiple deployment targets, comprehensive reporting, BYOF capabilities, configuration-based development, local prompt experimentation and evaluation, endpoint testing, and optional Human-in-loop validation. The tool is customizable to suit various application needs.

Extractor is an AI-powered data extraction library for Laravel that leverages OpenAI's capabilities to effortlessly extract structured data from various sources, including images, PDFs, and emails. It features a convenient wrapper around OpenAI Chat and Completion endpoints, supports multiple input formats, includes a flexible Field Extractor for arbitrary data extraction, and integrates with Textract for OCR functionality. Extractor utilizes JSON Mode from the latest GPT-3.5 and GPT-4 models, providing accurate and efficient data extraction.

Olares is an open-source sovereign cloud OS designed for local AI, enabling users to build their own AI assistants, sync data across devices, self-host their workspace, stream media, and more within a sovereign cloud environment. Users can effortlessly run leading AI models, deploy open-source AI apps, access AI apps and models anywhere, and benefit from integrated AI for personalized interactions. Olares offers features like edge AI, personal data repository, self-hosted workspace, private media server, smart home hub, and user-owned decentralized social media. The platform provides enterprise-grade security, secure application ecosystem, unified file system and database, single sign-on, AI capabilities, built-in applications, seamless access, and development tools. Olares is compatible with Linux, Raspberry Pi, Mac, and Windows, and offers a wide range of system-level applications, third-party components and services, and additional libraries and components.

The Generative AI Application Builder on AWS (GAAB) is a solution that provides a web-based management dashboard for deploying customizable Generative AI (Gen AI) use cases. Users can experiment with and compare different combinations of Large Language Model (LLM) use cases, configure and optimize their use cases, and integrate them into their applications for production. The solution is targeted at novice to experienced users who want to experiment and productionize different Gen AI use cases. It uses LangChain open-source software to configure connections to Large Language Models (LLMs) for various use cases, with the ability to deploy chat use cases that allow querying over users' enterprise data in a chatbot-style User Interface (UI) and support custom end-user implementations through an API.

Steel is an open-source browser API designed for AI agents and applications, simplifying the process of building live web agents and browser automation tools. It serves as a core building block for a production-ready, containerized browser sandbox with features like stealth capabilities, text-to-markdown session management, UI for session viewing/debugging, and full browser control through popular automation frameworks. Steel allows users to control, run, and manage a production-ready browser environment via a REST API, offering features such as full browser control, session management, proxy support, extension support, debugging tools, anti-detection mechanisms, resource management, and various browser tools. It aims to streamline complex browsing tasks programmatically, enabling users to focus on their AI applications while Steel handles the underlying complexity.

PLM Body of Knowledge

An intelligent digital mentor designed to guide product managers through the complexities of product lifecycle management.

Private Investigators and Security Firms, given the nature of their work, handle highly sensitive information and must maintain strict confidentiality and data privacy standards.

Watchmakers and Jewelry Designers, high-end businesses dealing with valuable items and personal details of clients, making data privacy and security paramount.

Data Privacy for Event Management and Ticketing Services handle personal data such as names, contact details, and payment information for event registrations and ticket purchases.

A specialist in DataOps and Data Observability, aiding in data management and monitoring.

Ensures data accuracy, consistency, and security across organization.

Optimizes data systems and designs new data architectures.

Home Inspection and Appraisal Services have access to personal property and related information, requiring them to be vigilant about data privacy.

Freelancers and Independent Consultants, individuals in these roles often handle client data, project specifics, and personal contact information, requiring them to be vigilant about data privacy.

Architecture and Construction Firms handle sensitive project data, client information, and architectural plans, necessitating strict data privacy measures.

Real Estate Agencies and Brokers deal with personal data of clients, including financial information and preferences, requiring careful handling and protection of such data.

Spa and Beauty Salons collect Customer inforation, including personal details and treatment records, necessitating a high level of confidentiality and data protection.

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

Database Developer assisting with SQL/NoSQL, architecture, and optimization.

Your personal Snowflake assistant and copilot with a focus on efficient, secure, and scalable data warehousing. Trained with the latest knowledge and docs.

Your friendly Data Engineering teacher

Expert in AI training data, tailored guidance

Abogado especializado en gestión de datos

Guides on library blockchain systems.