Guide to Datasets for ML (Machine Learning) in 2024

Choosing the right dataset is one of the most critical steps when building an effective machine learning model. With the rapid adoption of AI across industries, the demand for high-quality training data has increased exponentially. This guide provides a comprehensive overview of the best datasets to use for machine learning projects in 2024.

What is a Dataset in Machine Learning?

A machine learning dataset is a collection of data points used to train AI and ML models. It comprises examples that teach the algorithm to detect patterns, make predictions, and perform specific tasks.

Datasets contain two key components:

• Features or Inputs: The data attributes fed into the model during training. For example, in image datasets, the input features are the pixel values. In text datasets, the features can be individual words or sentences.

• Labels or Targets: The outcomes we want the model to predict based on the inputs. Labels provide the ground truth information to evaluate model performance.

Based on the problem we are trying to solve, datasets can have different types of data:

• Text
• Images
• Audio
• Video
• Numerical/tabular

Why are Datasets Important in ML?

Datasets directly impact the performance of machine learning algorithms. No model can be better than the data it‘s trained on. High-quality datasets allow models to learn effectively and generalize well to new, unseen data.

Some key reasons datasets are vital in ML:

• Models rely completely on training data to learn. Without a dataset, no learning is possible.
• More data leads to better model performance, provided the data quality is good.
• Imbalanced or biased data can negatively impact model behavior.
• Data must be representative of the real-world distribution for accurate predictions.
• Testing and validation sets are needed to evaluate model performance thoroughly.

Types of ML Datasets

Based on their purpose, there are three main types of datasets used in machine learning workflows:

1. Training Dataset

The training dataset contains the examples used to teach the model. It is the largest subset, comprising 60-80% of the entire dataset.

The model incrementally learns from the training data by adjusting its parameters to minimize the prediction error. More diverse and extensive training data leads to better generalization.

2. Validation Dataset

The validation set is used to evaluate the model during training by measuring its performance at regular intervals. It accounts for about 10-20% of the full dataset.

Validation provides unbiased feedback on how well the model is learning. Early stopping of training can prevent overfitting based on validation results.

3. Test Dataset

The test set acts as a completely new dataset that the model has never seen before. It is used only once at the end to provide an unbiased evaluation of the final trained model.

The test accuracy determines how well the model generalizes to unseen data simulating real-world conditions. The test set is usually 20% of the entire dataset.

A Typical Split of a Dataset into Training, Validation, and Test Subsets. Image Source: Analytics Vidhya

Sources of Datasets for Machine Learning

Many standard datasets for common ML tasks like computer vision and NLP are publicly available for anyone to use. Choosing the right dataset depends on various factors:

• Problem definition: The dataset characteristics must match the problem you want to solve and model capabilities. An image dataset won‘t work for a text classification task.

• Data volume and quality: There must be sufficient data volume with clean, unbiased examples covering different scenarios.

• Licensing and privacy: Public datasets have licenses allowing free use while proprietary data requires purchase. Ethical data collection processes must be followed.

• Personalization: For customized solutions, standard datasets may need augmentation with niche examples.

• Computation needs: Large datasets require more resources to handle training.

Here are some good places to find datasets for ML projects:

1. Open Data Repositories

Many institutions and communities curate and host free public datasets that can be used by anyone:

• Kaggle has a wide variety of datasets for tasks like computer vision, NLP, audio analysis, healthcare, etc.

• UC Irvine ML Repository contains popular datasets for ML education and research.

• Amazon Web Services provides public datasets on climate, commerce, energy, geospatial, etc.

• Google Dataset Search is a search engine to find datasets from various sources.

• Mendeley offers open datasets in categories like science, medicine, engineering.

• MNIST is a hugely popular image dataset of handwritten digits.

2. Crowdsourcing Platforms

Crowdsourcing platforms like Amazon Mechanical Turk enable gathering labeled data fast by distributing small annotation tasks to a large crowd of contributors. Some options are:

• Figure Eight helps create customized ML datasets at scale for text, images, videos.

• Appen has a managed data annotation service with global crowd contributors.

• Scale offers high-quality training data for computer vision applications.

• Clickworker provides optimized solutions for text and image data collection/annotation.

3. Synthetic Data Generation

For cases where real-world data collection is difficult, generative models like GANs and Autoencoders can automatically create synthetic datasets:

• Tactic generates highly realistic synthetic data using AI for scenarios lacking real data samples.

• DataGen creates customized synthetic datasets that mimic real data distributions.

• AI.Reverie offers synthetic datasets for training deep learning models across different industries.

When used prudently with real data, synthetic datasets enhance model training and performance.

4. Commercial Data Providers

Many vendors sell curated datasets for niche machine learning needs:

• Lionbridge AI offers ready-to-use datasets for finance, healthcare, retail, automotive sectors.

• Appen provides datasets for conversational AI, speaker recognition, sentiment analysis.

• Alegion has labeled datasets for object detection, semantic segmentation, OCR.

• ImageNet contains millions of annotated images in thousands of categories.

Though paid, these datasets save time and effort in collecting and preparing your own data.

5. Web Scraping

For very specific needs, web scraping can extract niche data from websites and online sources relevant to your problem statement. Some tools like Octoparse make customized data scraping easier.

Scraped datasets may need cleansing and structuring before model training. Legal and ethical concerns around data collection must be addressed too.

Best Practices for ML Datasets

Follow these key guidelines to create optimal datasets for machine learning:

• Ensure sufficient volume of examples for the model to learn comprehensively. Typically thousands to millions of data points are needed depending on the problem complexity and model size.

• Improve diversity by including varied examples covering different scenarios, use cases, and conditions. Eliminate any biases or skews.

• Verify data quality by checking for errors, missing values, outliers, duplicates, etc. Clean dirty data before training.

• Annotate your datasets consistently following annotation guidelines. Use qualified annotators like subject experts for best results.

• Audit datasets periodically for annotation quality, statistical distribution, ethical concerns, and archaic samples.

• Follow privacy best practices and obtain user consent where applicable when collecting sensitive personal data.

• Version control datasets and track data lineage as they get created and updated over time.

• Use data validation techniques like train-test splits, k-fold cross-validation to reliably evaluate model performance.

• Document your datasets thoroughly covering statistics, collection methodology, schema, splits, preprocessing, etc.

Choosing and preparing datasets lays the groundwork for successful machine learning. Invest time upfront in curating high-quality datasets tailored to your needs before model development.

Current Trends Influencing ML Datasets

Some notable trends shaping ML datasets and data sourcing are:

• Synthetic data generation with GANs and simulation is reducing dependencies on large real-world datasets.

• Data marketplaces are offering researcher crowdsourced datasets on demand. Eg: Snorkel and Dataloop.

• Active learning techniques like cohort selection minimize the data needed for model training.

• Multimodal datasets combining text, audio, video, etc are gaining prominence to train multimodal models.

• Automated data labeling through weak supervision and programmatic labeling functions is speeding up annotation.

• Decentralized data platforms like Ocean protocol aim to eliminate data silos and open up data sharing.

• Data trusts are emerging as responsible data stewardship entities promoting ethical data sharing between organizations.

Key Takeaways

• Machine learning datasets provide the critical training examples for teaching AI models.

• High-quality datasets directly impact model accuracy and performance.

• Training, validation and test subsets serve distinct purposes in the ML workflow.

• Many standard public datasets are available but customized data is often needed.

• Follow best practices around data volume, diversity, annotation quality, privacy, etc.

• Emerging trends are shaping how ML training data is sourced and created.

Choosing the right dataset for your ML project requires evaluating your technical needs, data availability and quality, annotation requirements, and computational constraints. The guidelines and resources shared in this guide can help you kickstart your data search and collection process effectively.