Budget limitations, labor demands, maintaining data diversity, and meeting compliance: Which of these dataset-building obstacles are holding back your machine learning project? Perhaps the challenge is not among these. However, in reality, you are stuck because you don’t have a dataset to kickstart the project.
Don’t let your current situation bring you down. Here is how you can get datasets for machine learning. Yes, actual quality pre-existing datasets for you to put together an MVP (Minimum Viable Product), get feedback and make that project a reality. Uncover more!
Sourcing Datasets for Machine Learning
Look through public datasets
Public datasets are freely available for anyone to use. Research institutions, governments, or open data initiatives curate them for various purposes like advancing learning and innovation. Find them by searching through government data portals, open-source repositories, or academic platforms.
Popular platforms to access public or open-source datasets include Google Dataset Search, UCI Machine Learning Repository, and Kaggle. These datasets are ideal for idea validation or prototyping, and training academic-centric models.
Even though free, you may have to clean some public datasets to improve quality before use. Moreover, most datasets are not tailored or suited for particular projects, limiting their application.
Procure datasets from external providers
Compared to public datasets which may require you to modify the data to suit a project, datasets from external providers are built based on your requirements.
Whenever you need datasets for machine learning, you define your project data needs, send them to the providers and they deliver. They are capable of obtaining historical and current information as desired. Moreover, they take care of all legal and ethical requirements.
Securing datasets from data vendors or providers is ideal if you are training time-sensitive, high-accuracy, and industry-specific models. For instance, if you are building a healthcare diagnostics or financial forecasting application, you can obtain quality and timely data affordably compared to collecting it manually.
Search through domain-specific repositories
Planning on building a niche-focused model, you may want to start your hunt for datasets in these repositories. They are a collection of specialized data from particular fields like medicine and environmental science.
Expert researchers and academics of specific fields run and maintain domain-specific repositories. They mostly organize themselves with a specific goal in mind and start building datasets to fulfill their target. Therefore, you can consult in case you face challenges while using one of their datasets.
Find domain-specific data in repositories like GenBank — home for genetic sequences, or Natural Language Toolkit Corpora — a database of text datasets for training natural language processing models.
And, the list goes on! However, keep in mind that there is limited availability of these datasets. Moreover, some may require you to have deeper domain understanding before you can effectively interpret and use them.
Pull data via platform APIs
Platforms including e-Commerce sites, social media networks, and financial websites do allow developers to pull data from their databases using APIs (Application Programming Interfaces). These are tools meant to give authorized users controlled access to specific data points.
APIs give you access to real-time data, making them ideal for building applications in need of up-to-the-minute data. And, they can be official or unofficial.
So, whenever you want to integrate data from a different platform into your project, check for the availability of an official or third-party API. If you can’t find one, you also have the option of building an API.
Despite the advantages, keep in mind that APIs have access restrictions and rate limits. This may limit the amount of data you can get from a website over a specific period. Also, some API are not budget-friendly.
There are also legal and ethical concerns around privacy and data security. Therefore, when possible, build your own API off of scraping data from a specific platform.
Generate synthetic data
Even after searching through all the other sources, you may still not find suitable datasets. In such a case, you can use synthetic data generators. They emulate real-life situations to ensure the data reflects true-to-life behaviors and patterns.
Besides not finding relevant datasets, it is wise to use synthetic data generators when working with highly sensitive data.
For example, a generator like the generative adversarial network (GAN) comes in handy when handling customer or patient personal data. GAN creates a synthetic version of the personal details, enhancing data privacy and security.
You can also use synthetic data generators to augment small datasets. These tools learn from a small dataset and generate additional data points to improve the performance of a model. However, before using these tools or sourcing datasets through the other tactics, remember the following:
What to Consider Before Sourcing Datasets for Machine Learning
Compliance with regulations and ethics
To avoid ethical dilemmas, reputational damage, or legal consequences, go through the relevant ethical and regulations guidelines. The guidelines outline the rights and privacy of the data collection process participants and the accepted way to use the data without compromising anyone’s privacy or rights.
Understanding data usage rights
Are you allowed to use the data commercially? How about modifying the data? These are some of the questions to consider before sourcing or using the datasets.
Check out the source’s defined licenses. They contain specific details on the restrictions and permissions you have. Remember, if you don’t clearly understand the license in place, seek legal advice to avoid breach of contract or copyright infringement.
Source credibility
You don’t want to risk using outdated, inaccurate, or biased data to train a model. Doing so may lead to significant reputational and financial issues due to launching a flawed model into the market.
So, ensure the source has a proven track record, is peer-reviewed or validated, and has an authoritative and reputable community vouching for it.
Intellectual property (IP) rights and data ownership
Always review the IP or data ownership rights of a dataset before using, sharing, modifying, or commercializing it. Failure to do so attracts potential legal disputes.
Even when sourcing data from a reputable provider, clearly define the ownership rights upfront to help with any sort of legal matter in case the provider violates the agreement terms.
Closing Words
Gone are days when you had to manually collect data to make a machine learning project a reality. Now, you can obtain ready-made datasets from various sources or even commission a provider to get you the data.
With the help of this guide, get to understand the ins and outs of five effective ways to get datasets for machine learning. Moreover, remember the considerations outlined at the end of the guide to avoid getting into legal trouble or compromising the success of the project.
