+100 Datasets for ML & AI Models

Data is required to leverage or build generative AI or conversational AI solutions. You can use existing datasets available on the market or hire a data collection service.

We identified over 100 datasets to train and evaluate machine learning and AI models.

Large Language Models (LLMs) and Agentic AI datasets

• Large language model benchmarks such as MMLU and GPQA measure general and scientific reasoning.
• Multimodal datasets, such as LAION-5B, combine text and images to train models that can handle both formats.
• Frontier evaluations, such as Humanity’s Last Exam, ARC-AGI-2, and AI Idea Bench, test models’ creativity, factual accuracy, and adaptability to complex prompts.
• Agentic and tool-use benchmarks, such as GAIA, BFCL (Berkeley Function Calling Leaderboard), and ComplexFuncBench, evaluate multi-step reasoning, tool calling, and task completion.
• UI grounding benchmarks, such as the AIMultiple UI Grounding Benchmark, evaluate whether computer-use models can identify the correct interface element and predict its location from a natural-language instruction. See the computer use agents benchmark for methodology and model results.
• Pre-training corpora, such as FineWeb, Nemotron-CC, and Essential-Web v1.0, provide large-scale token collections for training base models.

AI coding and software engineering datasets

• Datasets such as The Heap and MADE-WIC contain multilingual and annotated code for evaluating coding accuracy and technical debt.
• HumanEval and APPS provide coding problems with reference solutions to benchmark code-generation quality.
• Repository-level and agentic benchmarks, such as SWE-Bench Pro, SWE-Bench Multilingual, SWE-Lancer, and Terminal-Bench 2.0, evaluate models on real GitHub issues and end-to-end software tasks rather than isolated functions.
• Proprietary datasets, such as those from Amazon CodeWhisperer and GitHub Copilot, support commercial coding assistants.

Older benchmarks like HumanEval and SWE-bench Verified are now widely considered contaminated or saturated; as a result, contamination-resistant successors such as SWE-Bench Pro have emerged.

Cybersecurity and data security datasets

• CICIDS2017 and TON_IoT are widely used for training intrusion and anomaly detection systems.
• EMBER and VirusShare datasets contain labeled malware data for model-based classification.
• The CVE-MITRE database provides structured information on known software vulnerabilities.

Data, synthetic data, and privacy datasets

• Platforms such as Appen, Amazon Mechanical Turk, and Telus International supply human-generated datasets for supervised learning.
• Hazy and Gretel.ai generate synthetic structured data for enterprise use.
• Open repositories like Kaggle Datasets and Google Dataset Search provide publicly accessible data across multiple domains.

Web data datasets

• Commercial web data providers, such as Bright Data and Coresignal, sell pre-collected and custom datasets covering e-commerce, social, real estate, and job posting sources. Marketplaces like Datarade aggregate these across vendors.
• Raw and filtered crawls, such as Common Crawl, C4, RefinedWeb and RedPajama-Data-v2, supply bulk pretraining tokens.
• Multilingual corpora, such as FineWeb-2, OSCAR 23.01 and HPLT v2, extend coverage past English, which most crawl-derived datasets over-represent.
• Openly licensed corpora, such as Common Corpus and Common Pile, restrict sources to public-domain or permissively licensed pages, trading scale for a defensible provenance story.
• Structured web data, such as Web Data Commons and its schema.org table corpora, extracts the machine-readable markup websites embed, which skips HTML parsing for product, event, and organization entities.
• Web agent benchmarks, such as Online-Mind2Web, WebArena and BrowseComp, test whether models can navigate live sites.
• Vertical web datasets, such as Amazon Reviews 2023, the Yelp Open Dataset and GDELT, cover reviews, local business and news, and are commonly used for recommendation and sentiment work.

Domain-specific and industry datasets

• MIMIC-IV and PhysioNet support medical research and healthcare analytics.
• Waymo Open Dataset and KITTI are used for computer vision in autonomous vehicles.
• Robotics and embodied-AI datasets, such as AGIBOT WORLD 2026, EgoDex, and EgoVerse, provide first-person (egocentric) video and manipulation data for training physical-AI and humanoid systems.
• Multimodal medical benchmarks, such as GMAI-MMBench and OmniMedVQA, evaluate clinical visual question answering across many imaging modalities.
• World Bank Open Data and OECD datasets provide economic and financial indicators.
• Common Voice and Free Music Archive support audio and language model development.

Don’t miss our benchmarks and data-driven insights. The button opens Google; selecting AIMultiple confirms that you wish to see AIMultiple more often in Google search results.

Add as preferred source

What are ML datasets?

A machine learning dataset is a structured data collection specifically gathered and prepared to train machine learning models. These datasets for ML act as examples that help the model learn patterns, extract meaningful features, and make predictions on unseen data.

Depending on the task, the machine learning dataset may consist of various data types, including:

• Text data: Used in applications like natural language processing, sentiment analysis, and machine translation.
• Image data: Mostly used in computer vision and convolutional neural networks for tasks like handwritten digits recognition or steel plate faults detection.
• Audio data: For speech recognition or sound classification tasks.
• Video data: For object tracking or real-time video analysisç
• Numeric data: Used in regression or classification tasks, sometimes coming from mass spectrometry data or time stamp logs.

Most machine learning projects begin with raw data, which is then labeled or annotated. This labeling helps the machine learning system understand the expected outcome for classification, regression, or other predictive tasks.

A good dataset, often sourced from open, public, or specialized machine learning repositories, can significantly improve model performance.

Why prepare datasets for machine learning?

Preparing and choosing high-quality datasets is one of the most crucial steps in developing artificial intelligence systems. Many organizations recognize that data preparation can make or break their machine learning projects.

The quality of the training data affects how well models generalize to real-world scenarios and how accurately they handle specific problems. There are three key purposes of a machine learning dataset:

To train the model

The training set teaches the machine the relationships and patterns within the data. This involves feeding annotated or labeled data, allowing the model to adjust its parameters and improve its predictions on similar inputs.

To measure model accuracy

After training, the testing dataset (or test set) is used to evaluate the model’s performance. This helps determine how well the model handles unseen data, and whether it’s overfitting to the training set or learning meaningful patterns.

To improve the model post-deployment

Once deployed, teams often refine machine learning models using additional collected data, helping them adapt to new conditions or classes. Validation sets also help tune and prevent overfitting.

Cite this research

Pick the format that matches where you're publishing. Pasting the link version into your CMS preserves the backlink.

@misc{dilmegani2026,
  author = {Dilmegani, Cem and Ermut, Sıla},
  title  = {{+100 Datasets for ML & AI Models}},
  year   = {2026},
  month  = sep,
  howpublished    = {\url{https://aimultiple.com/datasets-for-ml}},
  note   = {AIMultiple. Retrieved September 2, 2026}
}

Changelog

11 updates

1. 2026Jun 10

   Removed the "Types of ML datasets" section covering training, validation, and test subsets.

2. 2025Oct 9

   Replaced the "Dataset categories" section with new categories: Large Language Models (LLMs) & Agentic AI, AI coding and software engineering, Cybersecurity and data security, Data, synthetic data, and privacy, and Domain-specific and industry datasets.

3. Jun 4

   Updated the URL for Figure 1.

4. May 9

   Expanded the introduction with a list of dataset types and their descriptions.

5. 2024Sep 23

   Added a question and answer to the FAQs section.

6. Jul 17

   Added the "FAQs" section.

7. 2023Dec 4

   Added a table of ML datasets and data sources to the introduction.

8. Oct 10

   Added Generative AI to the "Where can ML datasets be sourced from?" section.

9. Jul 17

   Added a new vendor, Clickworker, to the list of ML dataset providers.

10. Jul 10

    Removed the sponsored section from the "What is the purpose of AI/ML datasets?" section.

11. May 5

    Added Clickworker as a sponsored product.

Cem Dilmegani

Principal Analyst

Follow On

Cem has been the principal analyst at AIMultiple since 2017.

Cem's work at AIMultiple has been cited by leading global publications including Business Insider, Forbes, Morning Brew, and Washington Post, global firms like Deloitte and HPE, NGOs like World Economic Forum, and supranational organizations like European Commission. [1], [2], [3], [4], [5]

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

View Full Profile

Researched by

Sıla Ermut

Industry Analyst

Follow On

Sıla Ermut is an industry analyst at AIMultiple covering AI models, AI infrastructure, AI governance, and enterprise AI applications. Her research focuses mostly on the use of AI in marketing, healthcare, supply chains, and sustainability.
She previously worked as a recruiter in project management and consulting firms. Sıla holds a Master of Science degree in Social Psychology and a Bachelor of Arts degree in International Relations.

View Full Profile

Be the first to comment

Your email address will not be published. All fields are required. Comments are left in their original language.

Name

Email Address

Comment

0/450

Post Comment