Machine learning is the process of creating systems that can learn from data and make predictions or decisions based on that data. Machine learning models are often trained on large datasets that contain various features and labels. However, not all data is equally useful or relevant for a given machine learning task. Data curation is the process of selecting, organizing, cleaning, and enriching data to make it more suitable for machine learning.
Data curation is important for several reasons:
- Data quality: Data curation can help improve the quality of the data by removing errors, inconsistencies, outliers, duplicates, and missing values. Data quality affects the accuracy and reliability of machine learning models, as garbage in leads to garbage out.
- Data relevance: Data curation can help ensure that the data is relevant for the machine learning goal by selecting the most appropriate features and labels, and filtering out irrelevant or redundant information. Data relevance affects the efficiency and effectiveness of machine learning models, as irrelevant data can lead to overfitting or underfitting.
- Data diversity: Data curation can help increase the diversity of the data by incorporating data from different sources, domains, perspectives, and populations. Data diversity affects the generalization and robustness of machine learning models, as diverse data can help capture the complexity and variability of the real world.
- Data knowledge: Data curation can help enhance the knowledge of the data by adding metadata, annotations, explanations, and context to the data. Data knowledge affects the interpretability and usability of machine learning models, as knowledge can help understand how and why the models work.
Data curation is not a trivial task. It requires domain expertise, human judgment, and computational tools. Data curators collect data from multiple sources, integrate it into one form, authenticate, manage, archive, preserve, retrieve, and represent itAd1. The process of curating datasets for machine learning starts well before availing datasets. Here are some suggested steps2:
- Identify the goal of AI
- Identify what dataset you will need to solve the problem
- Make a record of your assumptions while selecting the data
- Aim for collecting diverse and meaningful data from both external and internal resources
Data curation can also leverage social signals or behavioral interactions from human users to provide valuable feedback and insights on how to use the data3. Data analysts can share their methods and results with other data scientists and developers to promote community collaboration.
Data curation can be time-consuming and labor-intensive, but it can also be automated or semi-automated using various tools and techniques. For example, Azure Open Datasets provides curated open data that is ready to use in machine learning workflows and easy to access from Azure services4. Automatically curated data can improve the training of machine learning models by reducing data preparation time and increasing data accuracy.
In conclusion, curated data is important when training machine learning models because it can improve the quality, relevance, diversity, and knowledge of the data. Data curation can help build more accurate, efficient, effective, generalizable, robust, interpretable, and usable machine learning models that can solve real-world problems.