3 Simple Ways to Acquire Training Data for Your AI/ML Models

We don’t have to tell you the value of AI training data for your ambitious projects. You know that if you feed garbage data to your models, they will produce coinciding results, and training your models with quality datasets will result in an efficient and autonomous system capable of delivering accurate results.

While this concept is easy to understand, finding the most helpful dataset source and data to train your machine learning (ML) projects can be challenging.

We created this post to help businesses find helpful solutions that are catered to their specific needs. Regardless of whether your project requires:

• Tailored datasets that are of the most recent origin
• Generic data to kickstart your AI training process
• Highly niched datasets that might be difficult to find online

We have a solution to every problem you could encounter in this article.

Let’s get started.

3 Simple Ways to Acquire Training Data For Your AI/ML Models

As an aspiring data scientist or an AI specialist, you can find data from three primary sources:

• Free sources
• Internal sources
• Paid sources

1. Free Sources

Free sources offer data sets (you guessed it) for free. There are several popular directories, forums, portals, search engines, and websites to source your datasets. These sources could be public, archives, data made public after several years of data with explicit permissions. We’ve outlined a quick list of examples of free resources below:

Kaggle –

A treasure chest for data scientists and machine learning enthusiasts. With Kaggle, you can find, publish, access, and download datasets for your projects. Data sets from Kaggle are of good quality, available in diverse formats, and easily downloadable.

UCI Database –

Machine learners and data scientists have been using the UCI database since 1987. This resource offers domain theories, databases, archives, data generators, and more for specific projects. The UCI Databases are classified and displayed based on their problems or tasks such as Clustering, Classification, and Regression.

Market Player Data Sources –

Resources from tech giants such as Amazon (AWS), Google Dataset Search Engine, and Microsoft Datasets.

• AWS resource offers datasets that have been made public. Accessible through AWS, datasets from government agencies, businesses, research institutions, and individuals are curated and maintained within AWS.
• Google offers a search engine that retrieves free datasets relevant to your search queries.
• Microsoft’s Open Data Repository Initiative provides data scientists and machine learners with datasets from projects such as computer vision, NLP, and more.

Public and Government Datasets –

Public Datasets are a prominent resource offering datasets from industries such as complex networks, biology, and agriculture agencies. The categories are sequential and neatly organized for quick view, and readily available for download. It is worth noting that some of the datasets are license-based while others are free. We recommend thoroughly reading through the documentation before downloading datasets.

A data scientist will commonly look for historical data for their projects that could be geography-bound. In such instances, a helpful resource is maintained by international governments. Relevant datasets are available through government websites from India, the US, the EU, and other countries.

Pros of Free Resources

• No expenses involved whatsoever
• Tons of resources to find relevant datasets

Cons of Free Resources

• Involves hours of manual intervention to look through resources, download, categorize and compile datasets
• Data annotation processes are still manual tasks
• Licensing limitations and compliance constraints
• Finding relevant datasets can be time-consuming