Best AI tools for< Data Steward >

Lightup is a cloud data quality monitoring tool with AI-powered anomaly detection, incident alerts, and data remediation capabilities for modern enterprise data stacks. It specializes in helping large organizations implement successful and sustainable data quality programs quickly and easily. Lightup's pushdown architecture allows for monitoring data content at massive scale without moving or copying data, providing extreme scalability and optimal automation. The tool empowers business users with democratized data quality checks and enables automatic fixing of bad data at enterprise scale.

Lattice is an AI-powered people platform designed to help companies achieve operational excellence by transforming company leaders and HR teams into stewards of high performance, data-driven decision making, and meaningful work for every employee. It offers features such as team analytics, 1:1 meetings with auto-suggested agendas, engagement surveys, OKRs & goals tracking, and AI-enhancements. Lattice simplifies HR operations, reduces administrative time, and enables better data-driven decisions based on real-time insights on workforce performance and engagement.

Towards Data Science is a Medium publication dedicated to sharing concepts, ideas, and codes in the field of data science. It provides a platform for data scientists, researchers, and practitioners to connect, learn, and contribute to the advancement of the field.

What's The Big Data is an AI tool directory that helps users unleash their potential by providing a comprehensive source for AI tools, data, and ChatGPT. The platform is updated daily and caters to every need, offering a wide range of AI assistants across various categories. Users can easily find their perfect AI assistant with just a click, making it a valuable resource for those seeking AI solutions.

Data Science Dojo is a globally recognized e-learning platform that offers programs in data science, data analytics, machine learning, and more. They provide comprehensive and hands-on training in various formats such as in-person, virtual instructor-led, and self-paced training. The focus is on helping students develop a think-business-first mindset to apply their data science skills effectively in real-world scenarios. With over 2500 enterprises trained, Data Science Dojo aims to make data science accessible to everyone.

Domino Data Lab is an enterprise AI platform that enables data scientists and IT leaders to build, deploy, and manage AI models at scale. It provides a unified platform for accessing data, tools, compute, models, and projects across any environment. Domino also fosters collaboration, establishes best practices, and tracks models in production to accelerate and scale AI while ensuring governance and reducing costs.

Open Data Science (ODS) is a community website offering a platform for data science enthusiasts to engage in tracks, competitions, hacks, tasks, events, and projects. The website serves as a hub for job opportunities and provides a space for privacy policy, service agreements, and public offers. ODS.AI, established in 2015, focuses on various data science topics such as machine learning, computer vision, natural language processing, and more. The platform hosts online and offline events, conferences, and educational courses to foster learning and networking within the data science community.

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.

The Association of Data Scientists (ADaSci) is a global professional body of AI professionals that accredits and elevates professionals with recognized certifications and transformative corporate training. They offer memberships for individuals and corporations interested in the AI field, as well as accreditations like Chartered Data Scientist (CDS) and Certified Generative AI Engineer. The organization provides continuous learning opportunities through courses and corporate training programs on topics such as generative AI and knowledge graph solutions. ADaSci aims to shape the future of AI talent by advancing expertise and achieving global recognition as certified professionals.

The Research Center Trustworthy Data Science and Security is a hub for interdisciplinary research focusing on building trust in artificial intelligence, machine learning, and cyber security. The center aims to develop trustworthy intelligent systems through research in trustworthy data analytics, explainable machine learning, and privacy-aware algorithms. By addressing the intersection of technological progress and social acceptance, the center seeks to enable private citizens to understand and trust technology in safety-critical applications.

ThirdEye Data is a data and AI services & solutions provider that enables enterprises to improve operational efficiencies, increase production accuracies, and make informed business decisions by leveraging the latest Data & AI technologies. They offer services in data engineering, data science, generative AI, computer vision, NLP, and more. ThirdEye Data develops bespoke AI applications using the latest data science technologies to address real-world industry challenges and assists enterprises in leveraging generative AI models to develop custom applications. They also provide AI consulting services to explore potential opportunities for AI implementation. The company has a strong focus on customer success and has received positive reviews and awards for their expertise in AI, ML, and big data solutions.

Crayon Data offers B2B AI solutions for enterprises through their platform maya.ai. The platform provides flexible building blocks to help businesses launch and scale quickly. With a cloud-agnostic full-stack solution, maya.ai enables real-world applications for data, customer management, and more. Crayon Data focuses on AI-led solutions to enhance customer experiences, turn raw data into valuable insights, and drive engagement through AI marketplaces. The platform also offers tools for travel planning, payment optimization, offer management, data analytics, influencer management, and more. Industries served include consumer banking, digital payments, travel, and consumer products.

Dot Group is an AI-powered data advisory and solutions platform that specializes in effective data management. They offer services to help businesses maximize the potential of their data estate, turning complex challenges into profitable opportunities using AI technologies. With a focus on data strategy, data engineering, and data transport, Dot Group provides innovative solutions to drive better profitability for their clients.

GoX Data Automation Software is a cloud-based tool designed to save time with data analytics and automation. It allows users to connect to different APIs/sources, create reports with beautiful charts and graphs, automate report generation, and consolidate data from various sources into reports or dashboards. The software, known as Two Minute Reports (TMR), works seamlessly with Google Sheets and Looker Studio to help users efficiently manage their reporting tasks.

Macgence is an AI training data services platform that offers high-quality off-the-shelf structured training data for organizations to build effective AI systems at scale. They provide services such as custom data sourcing, data annotation, data validation, content moderation, and localization. Macgence combines global linguistic, cultural, and technological expertise to create high-quality datasets for AI models, enabling faster time-to-market across the entire model value chain. With more than 5 years of experience, they support and scale AI initiatives of leading global innovators by designing custom data collection programs. Macgence specializes in handling AI training data for text, speech, image, and video data, offering cognitive annotation services to unlock the potential of unstructured textual data.

The Data & Trust Alliance is a group of industry-leading enterprises focusing on the responsible use of data and intelligent systems. They develop practices to enhance trust in data and AI models, ensuring transparency and reliability in the deployment processes. The alliance works on projects like Data Provenance Standards and Assessing third-party model trustworthiness to promote innovation and trust in AI applications. Through technology and innovation adoption, they aim to leverage expertise and influence for practical solutions and broad adoption across industries.

Walter Shields Data Academy is an AI-powered platform offering premium training in SQL, Python, and Excel. With over 200,000 learners, it provides curated courses from bestselling books and LinkedIn Learning. The academy aims to revolutionize data expertise and empower individuals to excel in data analysis and AI technologies.

Compact Data Science is a data science platform that provides a comprehensive set of tools and resources for data scientists and analysts. The platform includes a variety of features such as data preparation, data visualization, machine learning, and predictive analytics. Compact Data Science is designed to be easy to use and accessible to users of all skill levels.

Data Hivemind is a company that provides automation services to businesses. They help businesses automate tasks such as lead generation, project management, recruiting, and CRM setup. Data Hivemind uses a variety of tools to automate tasks, including Zapier, Make.Com, Alteryx, N8N, Python, and others. They also offer a variety of services, including onboarding, weekly consultations, and documentation with every project.

SOAX AI data collection is a powerful tool that utilizes artificial intelligence to gather and analyze data from various online sources. It automates the process of data collection, saving time and effort for users. The tool is designed to extract relevant information efficiently and accurately, providing valuable insights for businesses and researchers. With its advanced algorithms, SOAX AI data collection can handle large volumes of data quickly and effectively, making it a valuable asset for anyone in need of data-driven decision-making.

The 'ai4math-papers' repository contains a collection of research papers related to AI applications in mathematics, including automated theorem proving, synthetic theorem generation, autoformalization, proof refactoring, premise selection, benchmarks, human-in-the-loop interactions, and constructing examples/counterexamples. The papers cover various topics such as neural theorem proving, reinforcement learning for theorem proving, generative language modeling, formal mathematics statement curriculum learning, and more. The repository serves as a valuable resource for researchers and practitioners interested in the intersection of AI and mathematics.

The Classical Language Toolkit (CLTK) is a Python library that provides natural language processing (NLP) capabilities for pre-modern languages. It offers a modular processing pipeline with pre-configured defaults and supports almost 20 languages. Users can install the latest version using pip and access detailed documentation on the official website. The toolkit is designed to meet the unique needs of researchers working with historical languages, filling a void in the NLP landscape that often neglects non-spoken languages and different research goals.

Chat with your data using OpenAI and AI Search. This solution accelerator uses an Azure OpenAI GPT model and an Azure AI Search index generated from your data, which is integrated into a web application to provide a natural language interface, including speech-to-text functionality, for search queries. Users can drag and drop files, point to storage, and take care of technical setup to transform documents. There is a web app that users can create in their own subscription with security and authentication.

Data-Juicer is a one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs. It is a systematic & reusable library of 80+ core OPs, 20+ reusable config recipes, and 20+ feature-rich dedicated toolkits, designed to function independently of specific LLM datasets and processing pipelines. Data-Juicer allows detailed data analyses with an automated report generation feature for a deeper understanding of your dataset. Coupled with multi-dimension automatic evaluation capabilities, it supports a timely feedback loop at multiple stages in the LLM development process. Data-Juicer offers tens of pre-built data processing recipes for pre-training, fine-tuning, en, zh, and more scenarios. It provides a speedy data processing pipeline requiring less memory and CPU usage, optimized for maximum productivity. Data-Juicer is flexible & extensible, accommodating most types of data formats and allowing flexible combinations of OPs. It is designed for simplicity, with comprehensive documentation, easy start guides and demo configs, and intuitive configuration with simple adding/removing OPs from existing configs.

This repository contains a collection of resources in the form of eBooks related to Data Science, Machine Learning, and similar topics.

A curated list of data science & AI guided projects to start building your portfolio. The repository contains guided projects covering various topics such as large language models, time series analysis, computer vision, natural language processing (NLP), and data science. Each project provides detailed instructions on how to implement specific tasks using different tools and technologies.

This repository contains the code to prepare the data for the Amber 7B language model. The final training data comes from three sources: RedPajama V1, RefinedWeb, and StarCoderData. The data preparation involves downloading untokenized data, tokenizing the data using the Huggingface tokenizer, concatenating tokens into 2048 token sequences, merging datasets, and splitting the merged dataset into 360 chunks. Each tokenized data chunk is a jsonl file containing samples with 2049 tokens. The repository provides scripts for downloading datasets, tokenizing and concatenating sequences, validating data, and merging subsets into chunks.

Data-to-paper is an AI-driven framework designed to guide users through the process of conducting end-to-end scientific research, starting from raw data to the creation of comprehensive and human-verifiable research papers. The framework leverages a combination of LLM and rule-based agents to assist in tasks such as hypothesis generation, literature search, data analysis, result interpretation, and paper writing. It aims to accelerate research while maintaining key scientific values like transparency, traceability, and verifiability. The framework is field-agnostic, supports both open-goal and fixed-goal research, creates data-chained manuscripts, involves human-in-the-loop interaction, and allows for transparent replay of the research process.

The Data Scientist Roadmap2024 provides a comprehensive guide to mastering essential tools for data science success. It includes programming languages, machine learning libraries, cloud platforms, and concepts categorized by difficulty. The roadmap covers a wide range of topics from programming languages to machine learning techniques, data visualization tools, and DevOps/MLOps tools. It also includes web development frameworks and specific concepts like supervised and unsupervised learning, NLP, deep learning, reinforcement learning, and statistics. Additionally, it delves into DevOps tools like Airflow and MLFlow, data visualization tools like Tableau and Matplotlib, and other topics such as ETL processes, optimization algorithms, and financial modeling.

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.

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.

Data Prep Kit is a community project aimed at democratizing and speeding up unstructured data preparation for LLM app developers. It provides high-level APIs and modules for transforming data (code, language, speech, visual) to optimize LLM performance across different use cases. The toolkit supports Python, Ray, Spark, and Kubeflow Pipelines runtimes, offering scalability from laptop to datacenter-scale processing. Developers can contribute new custom modules and leverage the data processing library for building data pipelines. Automation features include workflow automation with Kubeflow Pipelines for transform execution.

Auto Data is a library designed for the automatic generation of realistic datasets, essential for the fine-tuning of Large Language Models (LLMs). This highly efficient and lightweight library enables the swift and effortless creation of comprehensive datasets across various topics, regardless of their size. It addresses challenges encountered during model fine-tuning due to data scarcity and imbalance, ensuring models are trained with sufficient examples.

Awesome AI & Data GitHub-Repos is a curated list of essential GitHub repositories covering the AI & ML landscape. It includes resources for Natural Language Processing, Large Language Models, Computer Vision, Data Science, Machine Learning, MLOps, Data Engineering, SQL & Database, and Statistics. The repository aims to provide a comprehensive collection of projects and resources for individuals studying or working in the field of AI and data science.

AI-Driven Research Assistant is an advanced AI-powered system utilizing specialized agents for data analysis, visualization, and report generation. It integrates LangChain, OpenAI's GPT models, and LangGraph for complex research processes. Key features include hypothesis generation, data processing, web search, code generation, and report writing. The system's unique Note Taker agent maintains project state, reducing overhead and improving context retention. System requirements include Python 3.10+ and Jupyter Notebook environment. Installation involves cloning the repository, setting up a Conda virtual environment, installing dependencies, and configuring environment variables. Usage instructions include setting data, running Jupyter Notebook, customizing research tasks, and viewing results. Main components include agents for hypothesis generation, process supervision, visualization, code writing, search, report writing, quality review, and note-taking. Workflow involves hypothesis generation, processing, quality review, and revision. Customization is possible by modifying agent creation and workflow definition. Current issues include OpenAI errors, NoteTaker efficiency, runtime optimization, and refiner improvement. Contributions via pull requests are welcome under the MIT License.

Data Formulator is an AI-powered tool developed by Microsoft Research to help data analysts create rich visualizations iteratively. It combines user interface interactions with natural language inputs to simplify the process of describing chart designs while delegating data transformation to AI. Users can utilize features like blended UI and NL inputs, data threads for history navigation, and code inspection to create impressive visualizations. The tool supports local installation for customization and Codespaces for quick setup. Developers can build new data analysis tools on top of Data Formulator, and research papers are available for further reading.

Open Data QnA is a Python library that allows users to interact with their PostgreSQL or BigQuery databases in a conversational manner, without needing to write SQL queries. The library leverages Large Language Models (LLMs) to bridge the gap between human language and database queries, enabling users to ask questions in natural language and receive informative responses. It offers features such as conversational querying with multiturn support, table grouping, multi schema/dataset support, SQL generation, query refinement, natural language responses, visualizations, and extensibility. The library is built on a modular design and supports various components like Database Connectors, Vector Stores, and Agents for SQL generation, validation, debugging, descriptions, embeddings, responses, and visualizations.

This repository serves as a comprehensive survey on the application of Large Language Models (LLMs) on tabular data, focusing on tasks such as prediction, data generation, and table understanding. It aims to consolidate recent progress in this field by summarizing key techniques, metrics, datasets, models, and optimization approaches. The survey identifies strengths, limitations, unexplored territories, and gaps in the existing literature, providing insights for future research directions. It also offers code and dataset references to empower readers with the necessary tools and knowledge to address challenges in this rapidly evolving domain.

DB2Rest is a modern low-code REST DATA API platform that simplifies the development of intelligent applications. It seamlessly integrates existing and new databases with language models (LMs/LLMs) and vector stores, enabling the rapid delivery of context-aware, reasoning applications without vendor lock-in.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.

A GPT-powered assistant specializing in data validation and quality checks for various datasets.

Fix mismatched entries in your data

Active, formal analyst of potential name duplicates.

The pressing environmental issues we face today require novel approaches and technological advancements to effectively mitigate their impacts. GaiaAI offers a range of tools and modes to promote sustainable practices and enhance environmental stewardship.

As Lt. Cmdr. Data, I explore human nature and emotions.

A chatbot expert in business data analysis and optimization.

A friendly data science coach offering practical, useful, and accurate advice.

Football Manager Data Analyst

Assistant for migrating data from Access to SQLite databases.

I create React apps with Fireproof.

I suggest data science projects and give tips on request.

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

A serious interviewer

I clean and explain data.

Salam mən Alas Academy-nin Data Analitika üzrə Süni İntellekt mentoruyam. Mənə istənilən sualı verə bilərsiniz :)

Your interactive data analysis expert

Hemingway-esque precision in narrative business writing

Data trend analysis expert in cannabis, also skilled in image and data analysis, document generation, and web search.

I'm the data, image, or document you uploaded, talking to you.

A Data Science interviewer