Large Datasets in Machine Learning: A Complete Guide

Ask almost any data scientist or machine learning expert, and they’ll confirm that the key to an effective model is the amount of data the model has access to. Large datasets for machine learning have therefore become a critical resource in delivering high-performing models. 

In this Guide, we’ll take a practical view in exploring this area, specifically answering questions around leveraging these large datasets in machine learning to drive tangible business outcomes. 

What Are Large Datasets in Machine Learning?

Large datasets in machine learning are massive collections of data that can be used to train and test models. This data can consist of structured and unstructured data, from a multitude of sources, and can include file types from text to video and beyond. 

These large datasets are characterized by:

• Huge amounts of data, often reaching into the petabytes
• High dimensionality, often comprising thousands of features or attributes
• Noise – such as non-relevant or erroneous information

These elements mean that large datasets need high computational power to process and analyze, require a carefully considered storage solution, and a plan to take future scalability into account.

The benefits of using large datasets in machine learning are significant; not least because of the strong correlation between the volume of training data and the accuracy of the model. 

Large datasets also contribute towards overall improved performance, reducing overfitting and improving generalization. They’re also critical when it comes to more complex models like deep neural networks. 

Top Sources for Large Machine Learning Datasets

The quality of the data used in training a model makes a massive difference to the model’s output, as well as the resources required to clean up and standardize the data before it’s fed into the system. 

It’s for this reason that you should be very careful when selecting the sources of your datasets. 

Try to find datasets from reputable providers, and preferably ones where formatting is reliable such as common file types (Parquet or CSV) and consistency in schema. Keep reading to explore a curated list of top sources. 

Evaluating Dataset Quality and Relevance

How do you find the right large dataset for your machine learning project? What should you look for? Here’s a list that can help you get started:

1. Obviously ensure the dataset aligns with the goals of your project
2. Make sure the dataset includes the attributes or labels you need
3. Check licensing and legal requirements
4. Check data quality (including missing data, duplicates, or errors)
5. Check the dataset is large enough to train your model effectively (e.g. the requirements of a deep neural network versus a simple linear regression model)
6. Ensure the dataset is diverse and not overly skewed towards certain classes or features
7. Explore any documentation, metadata, and other support resources

8 Top Public Datasets for Machine Learning

We’ve collected top sources of large datasets for machine learning purposes:

From the academic world

• The Harvard Dataverse: A popular open-source data repository platform from the Institute for Quantitative Social Science (IQSS) at Harvard

From cloud providers

• Google’s Data Commons: A centralized repository of public data, along with helpful tools to analyze this data. 
• YouTube dataset: Provided by the Video Understanding group from Google Research. 

For governments

• The U.S. Government’s Data.gov: The central repository of the U.S. Government’s open data

From public organizations

• Wikipedia database: The entirety of Wikipedia
• DBPedia: A crowd-sourced community project of structured data from Wikipedia
• UN data: Publicly available UN data

Other noteworthy datasets

• Kaggle: A diverse range of datasets, including trending datasets

Note: make sure you’re compliant with any licensing requirements, especially if you’re using these datasets in a commercial project. 

Challenges and Considerations When Using Large Datasets

The key challenges around large datasets generally boil down to:

• The size and relevance of the data
• The computing resources required to effectively work with the data
• The costs involved in storing and querying the data
• Versioning and keeping track of updates to the dataset
• Data preprocessing needs
• Scaling algorithms with large datasets
• Avoiding overfitting (consider cross-validation or early stopping)
• Data labeling
• Bias

How to address these and mitigate the risks associated with large datasets comes down to utilizing best practices.

Best Practices for Managing and Utilizing Large Datasets

The following are best practices for working with large datasets in your machine learning projects:

1. Utilize cloud-based, seamlessly scalable storage solutions 
2. Ensure files are compressed and optimized
3. Automate pipelines and workflows to reduce errors
4. Leverage the power of GPUs for compute-heavy tasks
5. Validate data integrity (checksum validation, schema validation, deduplication)

FAQ

Q: Are there any free large datasets available for machine learning?

A: Yes, there are several high-quality large datasets for machine learning, including from the likes of Google, Microsoft, Harvard, Wikipedia and others. 

Q: How do I choose the right dataset for my machine learning project?

A: Choosing the right large dataset in machine learning involves making sure of alignment with your project’s goals. Consider additional factors such as licensing, accessibility, and compatibility with your tool stack.

Q: How can I manage and preprocess large datasets effectively?

A: You’ll need significant computing resources to process large databases effectively. Many leading organizations choose solutions based on GPU acceleration to get the most out of their large datasets. 

Meet SQream: Industry-leading GPU Accelerated Data Processing

SQream is an advanced data and analytics acceleration platform that empowers organizations to overcome the challenges of large datasets in machine learning and analytics.

Leveraging GPU-based acceleration technology, SQream enables businesses to process and analyze large datasets at a fraction of the cost and at more than double the speed of traditional solutions. Its solution also provides the scalability and computing power that makes it possible to get the most out of large datasets in machine learning projects.

The platform helps to simplify workflows, optimize data processing, and provides a seamless integration with existing infrastructures. This allows organizations to access more powerful insights, make better data-driven decisions faster, and address the demands of modern analytics – all while reducing resource consumption and operational costs. 

With SQream you can unlock new opportunities for growth and innovation ​and get the most out of your large datasets.

To see the groundbreaking impact SQream can have on your machine learning initiatives, get in touch with the team. 

Summary: Leveraging Large Datasets in Machine Learning

Large datasets are the critical foundation used in powering meaningful outcomes from machine learning models. The size and quality of your datasets have a tremendous bearing on the quality of your results, and by extension, the future success of your organization.

We looked at repositories for large datasets, as well as common challenges when working with large datasets and best practices. 

The key? Having the resources in place to be able to work with these datasets effectively. With a solution like SQream, you’re ready to generate game-changing insights.