At the heart of any machine learning initiative is data—it’s required to train models and serves as the base on which models are applied.
It follows, then, that to effectively train and implement machine learning models, you must have good data. Unfortunately, curating high-quality data often isn’t an easy task.
This article explains how open source dataset initiatives contribute to the development of machine learning models. You’ll also learn about popular open source dataset initiatives for machine learning, and discover what challenges to expect when using publicly-available data.
Datasets and Machine Learning
Obtaining data that’s relevant to your goal can be difficult if you aren’t sure where to look or only have access to limited sources. It can also be expensive, for example, if you have to purchase data.
After you’ve collected enough data, you need to devote time to verifying, cleaning, and possibly reformatting your dataset. These processes can be incredibly time-consuming and can significantly slow down your efforts to train and test a model.
Because of this, many organizations and researchers are interested in at least starting with existing datasets. Existing datasets can help you start the training process right away while you devote time to collecting more specific data or building more detailed sets. Even better, many of these existing datasets are open source, meaning you can freely access and use them.
What Are Some of the Commonly-Used Open Source Datasets?
When looking for open source datasets to use, there are many sources you can turn to. Below are some of the most popular and reliable places to start your search.
Kaggle is a machine learning community that provides access to more than 19k public datasets and 200k public Jupyter Notebooks. When you sign up for Kaggle, you also gain access to free GPU resources and a lot of community support.
Within each dataset, you can find a description of the set, a usability rating, licensing information, usage stats, and metadata. If other data scientists have worked with the set, you may also find Kernels, or Notebooks of methods tried. Each dataset page also has a quick view where you can scan included data files and formats and a community forum to discuss issues or applications.
UCI Machine Learning Repository
The UCI Machine Learning Repository is a public dataset repository owned and managed by the University of California, Irvine. It currently contains 497 datasets available for download.
Each dataset description contains information defining dataset characteristics, attribute characteristics, number of lines, and whether values are missing. It also includes source and collection information, attribute information, and a listing of citations for studies using the set.
Google Dataset Search
Google Dataset Search is a search engine for publicly-available datasets. It works in a similar manner to Google Scholar and enables you to locate datasets based on keywords. It includes sets from publishers, digital libraries, and personal pages.
When you perform a search, you can filter datasets by last update, download format, usage rights (including only free sets), and topics. In the returned results, you’re provided with a summary of who created the dataset, a brief description, and links to where the dataset can be downloaded.
Open Source Datasets for Machine Learning: Challenges and Solutions
When using public and open source datasets, there are several challenges you may face. Below are some of the most common.
Before using a public dataset, you need to check its licensing to verify that you are going to use data in a way that meets open source license compliance. This is especially important if you plan to publish the results of your model training or plan to include the finished model in a product that is not open source.
Additionally, keep in mind that not all datasets that are publicly-available are open source. There is a chance that a dataset you find has been made public against its license. Taking the time to first verify you’re using it in compliance can save you costs and time down the line.
Lack of data integrity
When you decide to use an open source dataset, you need to ensure that the integrity of the data you’re working with is protected. Generally, the best way to do this is to work from a private copy of the data.
The downside of this solution is that it’s more difficult to benefit from updates to the dataset. However, in exchange, you don’t have to worry about others modifying values, inserting malicious code, or reprocessing your set.
Lack of data standards
Depending on the source of the dataset you find, the quality and usability of the data can vary widely. Because of this, it’s important that you carefully evaluate any set you’re considering. This can include verifying that formatting matches your needs, checking that collection methods were reliable, or running preliminary tests to check the internal consistency of data.
Privacy concerns can be tricky to navigate when working with open datasets. It’s a possibility that datasets have been made public without concerns for privacy. It’s also possible that multiple datasets can be linked together to expose private information that was otherwise hidden. Both of these situations can present ethical and possibly legal issues.
Ideally, the set providers will make it clear whether data has been anonymized or otherwise de-identified. If not, you may need to take steps to perform this process yourself. You should also take steps to encrypt any datasets you’ve stored. However, be aware that this encryption will likely slow your model training pipeline by at least a little.
Data Management Best Practices For Machine Learning
Once you’ve navigated any challenges, there are several best practices you should consider adopting when working with open source datasets. These practices can help you ensure that datasets remain reliable and save you time rather than demanding additional effort.
- Define dataset conditions — Try to determine upfront how frequently your dataset needs to be updated. When using open source data, it may not be practical to try to stream data from live sources, which means you need to set aside time to periodically pull updates, process data to match your current use, and integrate it into your set.
- Consider model testing requirements — When testing for model fit, you’ll often need to make numerous adjustments to datasets. This is much easier when you have access to a history of other’s attempts, since you can narrow down your own efforts. You also need to keep track of any changes you make to ensure that you can revert to previous versions if needed.
- Centralize data processes — Regardless of the source of your data, you need to ensure that it remains available to your training and production pipelines. This is easiest to do when you store, process, and monitor data centrally (for example, in a data lake). This centralization also makes it easier for teams to collaborate on model testing and training since all members have access to the same resources, including each other’s efforts.
Open source datasets can help you gain the data needed to improve your machine learning projects. However, each dataset comes with its own set of characteristics, which should be assessed prior to usage. Open source licensing and privacy concerns are a particularly important challenge.
Non-compliance may result in monetary fines, and sometimes it can also cause significant damage to your brand name or reputation. To ensure you’re legally covered, you should make sure that you have the needed permissions to use this data, and that the licensing applies to what you’re planning to do with the data.