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
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
Another crucial data source is from internal databases. You may not be able to find what you are looking for in a free resource; in this situation, you may want to look within your organization across multiple data generation touchpoints you’ve established. Precise, recent data relevant to your project should be readily available internally.
With internal sources, you can customize the data for various use cases. Internal sources could be data produced from your CRM, social media handles, or website analytics.
Pros of Internal Resources
- Minimal expenses involved
- Modify parameters to generate required information directly
Cons of Internal Resources
- Countless hours of manual work
- Interdepartmental and intradepartmental collaborations are inevitable
- Not ideal for projects with limited time to market
- Data generated in-house would be irrelevant for your AI models
Unfortunately, unique datasets aren’t available on free or internal resources but can be obtained through paid resources. Paid sources are built by companies that work on getting the datasets you require for your projects through their own specific data sourcing techniques.
What is Data Annotation?
The process of adding additional information such as descriptions and metadata to your datasets to make them machine-understandable is known as data annotation. Regardless of where your data is coming from, it will be in raw form. It has to be cleaned and annotated using precision techniques to ensure it can become AI training data for your models.
Data annotation is where paid resources become ideal. When you outsource AI training data to 3rd party experts, they extract, compile, annotate, and present the data to you as ML-ready deliverables. When outsourcing, you can also be sure of compliances, licenses, and other legal concerns you may overlook when using internal or free resources.
Dealing with raw data from internal or free resources is time-consuming and a financial burden. We always recommend outsourcing training datasets when possible.
Pros of Paid Resources
- Annotated and QAed datasets reach you quickly
- Flexible deadlines
- Customized datasets available based on your requirements
- Regulatory compliance in sourcing data is always taken care of by the vendor
Cons of Paid Resources
- Involves expenses
If you have limited time to market or have very niche specifications concerning datasets, we suggest utilizing a paid resource or outsourcing to an industry expert like us. We have years of experience delivering AI training data for key market players such as MSME businesses.
Contact us today to talk about how we can help you source AI training data.