Data curation: your ML success formula

Learn Nov 18, 2021

Imagine acquiring Harley Davidson Cosmic Starship: 2002 V-Rod, coated with Jack Armstrong’s signature art. A one-off monster. Then, imagine running it on cheap water-based fuel. Listen up, you may have the most excellent model out there, but without quality data, it will backfire gravely and twice as hard. “So, how do I manage my data quality?” Data curation—to have and to hold from this day forward, for better, for worse, to love and to cherish until you are parted—jokes aside, just as Harley needs pure unleaded fuel, your AI, too, requires faultless input data to operate in its full capacity. Let’s jump in to find out how data curation can become a keystone to model success.

In this article, we’ll walk you through the following:

What is data curation?

In a broader perspective, curation is the round-the-clock maintenance of data throughout its life cycle. In practice, curation revolves around sharing loads of data among teams and utilizing relevant tooling and filtering to identify pain points in the dataset. When exercised in due course, it will put an end to endless quests for faster data throughput, unleashing your data’s full potential over time. A curation system will enable you to perfect the data and your understanding of it, making your model perform just as intended in the end. Think of it as a domino effect. You have to be able to hand-stop the collapsing sequence (within the pipeline) as and when needed⁠—that’s your curation.

data curation in machine learning

The importance of data curation in ML and AI

A common misconception among tech-savvies is that AI only needs to be fed the data collected, all until they are confronted with a grim reality of impurities and data bias in later stages of development. The way out in such instances would be to go back to square one, apply the necessary modifications to the training set, retrain the model, and monitor the output. Way too convenient, right? Curation would have prevented the extra hustle. By providing on-time data quality monitoring, data curation improves the prediction accuracy, becoming a highly demanded and substantial element for your pipeline.

Data curation vs. data governance

Although fundamentally different, curation is sometimes confused with data governance. Data curation refers to data management and maintenance, while data governance ensures that data-related roles, policies, and processes are clearly defined and communicated. It is rather a collection of metrics to ensure effective use of information to help the organization or company achieve its objectives.

When a company lacks a proper governance framework, it may end up in a data swamp, which essentially describes little to no data organization system as well as unusable or hardly usable data sources. Why is this a concern? It’s an easy time and money sink, taking up your storage space.

Top benefits in data curation

Faulty datasets are triggered by multiple factors and can be a result of incorrect instruction, knowledge gaps on the annotators’ end, and much more. Parallel to these problems, curation comes in as a modern necessity for a number of reasons:

Ease of access

Imagine having several datasets with thousands of annotated images and not being able to filter out those with an X or Y attribute: agreed, both daunting and incredibly inconvenient. Advanced filtering and data visualization come in handy when analyzing chunks of data. With curation, you can easily access the data you want without having to manually check out images one by one, which leads to the next advantage.

Increase in production speed

An effective curation system accelerates model development in a few ways: first, it allows companies to spend less time on data collection, preparation, and organization. Besides, identifying flaws in data and preventing those at the right time can cut off a fair amount of back and forth revision of the training data. Finally, curation provides room for dataset and model versioning as well as comparison. Goes a long way! You have the flexibility to refer back and use this or that version at any given moment.

Redundancy and bias detection

Annotators’ perceptions of the data and choices they make undergird AI. That explains why bias, ambiguity, and data imbalance are often pervasive or unavoidable the least. Curation is the ultimate control over the quality, as it points to the weaknesses or gaps within your dataset before it’s late.

challenges in data curation

Challenges in data curation

Data undergoes phases of transformation throughout its lifecycle. The role of curation here is to ensure it is securely stored, reusable, and error-free.

Data accuracy

Contrary to common belief, curation begins way before availing the datasets. Massive volumes of unstructured data can indeed be a challenge, especially if data inaccuracy creeps in during collection or creation (in case you’re generating your own synthetic datasets), cleaning, and annotation. Defining the aim of your AI, understanding which sets would meet your project goals, and fine-tuning those to eliminate the imbalance in advance can eventually become the turning point for your curation strategy. Here is a checklist on how to build SuperData with more insights on curation staples. After all, your data quality can even be a decisive factor between life and death, particularly if your AI gets approved for public use.

Security and privacy

With data leakage and hacking on the rise, security appears a major point of concern even for data at source, let alone curation. The worst of nightmares for companies is their data falling into the wrong hands. Pressure intensifies when taking on projects proposed by the government or the public sector, in general. A common approach to tackle the issue of security is providing integrity controls for the quality and accuracy of datasets—data security governance. So, it doesn’t come as a surprise how companies with strict privacy regulations earn a spotlight amid the evolving digitization and mounting threats in security.

Curating data with SuperAnnotate

Without being hyperbolic, not that we impose it on you, but check out yourself: Is the functionality below not worth a shot, the least? You can curate data with a user-intuitive interface and exclusive access to the following features:

Data visualization: Easily track your dataset distribution through data visualization. Identify recurring annotation biases immediately to cut off the production time.

Query system: Use data queries to filter and visualize relevant chunks of your data. Finding and managing data with selected attributes has never been easier.

SuperAnnotate's query system

Dataset and model versioning: Generate dataset and model versions, saving the older ones for future use and revision. Your work will not be lost! Besides, you can easily conduct a visual comparison of multiple models and annotations to understand where your models fail.

Shared datasets: Share datasets with the community and allow them to filter and explore data. Oversee the curation process simultaneously with other team members.

shared datasets on SuperAnnotate

Privacy-centric approach: SuperAnnotate is committed to being the most trusted annotation platform and service provider for AI and computer vision companies.

Interested in data curation?

With the escalating tendency in manipulating data, as opposed to solely the model, to achieve better prediction results, curation lends itself as the special sauce! If changes largely pertain to the training set, a complete overview of data will prove useful not once. Establishing a data curation process may seem costly to begin with, yet it’s a worthy investment for the long term, especially if you’re going to deal with tens of thousands of samples at once. Join hundreds of leading companies who build super high-quality training data up to 5x faster with SuperAnnotate.

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