Data-Centric AI

Data and AI have Evolved. Accelerate Machine Vision Innovation with Data-Centric AI.

“Instead of focusing on the code, companies should focus on developing systematic engineering practices for improving data in ways that are reliable, efficient, and systematic. In other words, companies need to move from a model-centric approach to a data-centric approach.”

— Andrew Ng, CEO and Founder of LandingAI

What Is Data-Centric AI?

Think of a Data-Centric AI system as programming with focus on data instead of code. Industries of all types continue to adopt AI solutions, and while AI models have improved over the years, a fundamental shift is needed to truly unleash AI’s full potential.

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Why Does Data-Centric AI Matter?

By adopting a Data-Centric AI approach, companies from diverse industries such as automotive, electronics, and medical device production have seen improvements in deploying AI and deep learning–based solutions in computer vision scenarios compared to traditional, rules-based implementations. Some improvements we’ve seen from adoption of a data-centric approach can make AI benefits accessible to most companies.

  • Build computer vision applications 10x faster
  • Reduced time to deploy application
  • Improved yield and accuracy
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Challenges:
Rule-Based Computer Vision

Rules-based machine vision algorithms may struggle with inconsistencies that are the result of working with parts with complex features or ambiguous part defects.

Inconsistencies and High-Rejection Rates

Producing high-quality products remains a top priority for companies. Defective parts present:

  • safety issues
  • damaged customer relationships
  • lost revenue

Identifying defective parts early in the process helps mitigate these risks. Machine vision technologies offer an effective method for doing so but may come up short in certain instances.

Introducing new or custom parts or working in an environment that constantly changes, such as lighting conditions in a plant with large windows, can also be problematic. In these scenarios, rules-based algorithms cam produce a high rejection rate, as the technology cannot distinguish between actual defective parts and acceptable variation, which necessitates a high rate of human follow-up inspection, increasing costs and slowing down production lines.

Images of sheet metal

Challenges:
Conventional Model-centric AI Approach

If human experts disagree on a label, how can the AI system be expected to make such a determination?
For applications with small datasets and rare defects, this becomes even more problematic.

“Is this really a scratch?”

In scenarios where rules-based algorithms will not suffice, many companies turn to AI and deep learning solutions yet still encounter challenges. Without a consistent data management approach, for instance, an AI system cannot accurately inspect products. While the system can analyze images of products on an assembly line and identify defects such as scratches and dents, this can present a challenge as well because human experts won’t always agree on appropriate labels when it comes to describing the damage.

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Taking Long time to Develop and Deploy

Developers often work with subject matter experts to define defects. Maintaining models and adapting to new circumstances such as new parts or environmental changes presents challenges and delays in development and deployment. In many cases, it may take several months to create and deploy an AI model.

Challenges to Standardize workflow and Scale projects

Standardizing a workflow and scaling AI solutions also presents challenges. Different teams use different methods to develop AI solutions, work with quality teams, and manage data, making it difficult for teams to learn from other groups and standardize workflow. Given such circumstances, scaling even one team to develop a solution can be onerous.
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Data-Centric AI Improves Data and Model Accuracy

Data-Centric AI Impacts Performance

A Data-Centric AI approach involves building AI systems with quality data — with a focus on ensuring that the data clearly conveys what the AI must learn. Doing so helps teams reach the performance level required and removes unnecessary trial-and-error time spent on improving the model without changing inconsistent data.

Chart showing Clean and Noisy data
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Data-Centric AI Promotes Collaboration

Quality managers, subject matter experts, and developers can work together during the development process to:

  • reach a consensus on defects and labels
  • build a model
  • analyze results
  • make further optimizations
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Data-Centric AI Reduces Development Time

With such an approach, teams can work in parallel and directly influence the data used for the AI system. By removing unnecessary back and forth among groups and looping in human input at the point where it’s needed most, the result is reduced development time.

Let’s compare the Data-Centric approach to the model centric approach in terms of which one is more approachable.

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Even More Benefits of Data-Centric AI

Additional benefits of Data-Centric AI include the ability for teams to develop consistent methods for collecting and labeling images and for training, optimizing, and updating the models. Teams easily learn from a past project’s success and can apply that knowledge to quickly scale new projects.

Image of Train Model Error Analysis

LandingLens is Data-Centric AI in Action

LandingLens, an industry-leading Data-Centric AI Computer Vision software platform, helps ensure product quality by improving inspection accuracy and reducing false rejections. The platform standardizes developing deep learning solutions that reduce development time and scale projects quickly to multiple facilities. Ultimately, LandingLens makes deep learning deployment easier, faster, and more effective, helping to increase throughput, maintain product quality, and drive revenue.

Images showing an example of LandingLens

Data-Centric Resources

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Overcoming hurdles to developing AI-powered visual inspection in manufacturing