The term ‘curation’ is commonly associated with museums or libraries, not data science. However, much like the work that’s done on rare paintings or books, data curation tools make the most important data easily accessible to engineers as they build complex machine learning models.

Without curation, data is difficult to find, analyze, and interpret. Data curation tools provide meaningful insights and enduring access to all your data in one place. In this article, we’ll dive into the importance of data curation for computer vision specifically, as well as review the top data curation tools on the market today.

What is data curation?
Data curation is the act of organizing, enhancing, and preserving data for future use. In machine learning, data curation describes the management of data throughout its lifecycle: from its collection and initially storage, to the time it is archived for future re-use.

This process is all the more important for computer vision engineers, who deal with massive amounts of visual data on a daily basis. Instead of using manual methods such as writing ETL jobs to extract insights, data curation tools provide a streamlined way to access the right data whenever you need to.

The importance of data curation for machine learning
Under the hood, data curation tools directly influence computer vision model performance. Using data curation tools, engineers can get a better understanding of the data they’ve collected, identify the most important subsets and edge cases, and curate custom training datasets to feed back into their models.

The role data curation tools play in machine learning
The role data curation tools play in machine learning

The best data curation tools enable you to:

Visualize large scale data: Make it easy to obtain insights on key metrics, as well as the general distribution and diversity of your datasets regardless of sensor type and format.

Enable data discovery and retrieval: Quickly search, filter, and sort through the entire data lake by making all features queryable and easily accessible.

Curate diverse scenarios: Identify the most interesting segments within your dataset, and manipulate them within the tool to create completely customized training sets.

Seamlessly integrate: The tool should fit well within your existing workflows and toolset.

What are the best data curation tools for computer vision?
With an overwhelming amount of AI products and platforms popping up year after year, how do you know which will provide the most value? Based on our experience, we are sharing our honest reviews of the top tools, hoping that this will be of use for engineers searching for a data curation solution.

Read on below to find out which data curation tool is the best fit for your computer vision project.

Originally published by SiaSearch at: https://www.siasearch.io/blog/best-data-curation-tools-for-computer-vision

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