6 Things You Need To Know About Data Management And Why It Matters For Computer Vision
This article will explore a few areas that we feel are essential when assessing data management solutions for computer vision.
Photo by Nana Smirnova on Unsplash
The race to Industry 4.0 and accelerated adoption of digital automation are pressure testing organizations across industries. It is becoming increasingly matter-of-fact that an enterprise’s ability to leverage data is a key source of competitive advantage—this principle especially holds true when it comes to building and maintaining computer vision applications. Visual automation models are powered by images and videos that capture a digital representation of our physical world. In most enterprises, media is captured across multiple sensor edge devices and lives in siloed source systems, making the integration of media across environments a core challenge that needs to be solved when building a computer vision system that seeks to automate visual inspection.
Media becomes even more important in a world where model architectures (the code that builds up the neural networks on which an AI model is trained) are increasingly commoditized and stable. This means that there are a lot more industry-trusted model types and now there is less of a focus on tweaking the code of these models to optimize performance and, instead, more of an emphasis on training an industry-standard model with the requisite data to best serve your application. In a data-centric approach to computer vision model development, organizations must continually train models on new ground truth data to protect against domain and data drift. Thus, both large and small computer vision vendors must ensure that organizations can continuously bring together media at scale in a programmatic way. Below, we’ll explore a few areas that we feel are essential when assessing data management solutions for computer vision.
Pre-Built Data Connectors
To manage data, you have to first bring it all to the same place. Your data today probably lives in a variety of commercial cloud environments, on-premises systems, and edge devices. What you need is a a way that simplifies connecting all your hardware and software. Pre-built connections makes these process seamless in just a few clicks instead of a few lines of code. It doesn’t hurt to also have a code editor (Python SDK) that lets you build custom connections when needed. In short, you don’t want to have to go knocking on your IT department’s door every time you need to integrate a new media source. That would be a pain for everyone!
Bringing together data from different sources can be a messy business. To find the data you need quickly, organizational structures like folders, buckets, or datasets will help you sort your media. You might want to organize data based on the date captured, or maybe you want to keep all the data captured by a single production line in the same folder. The choice is yours, just make sure to stay consistent.
All your data is now in the same place, but how can you quickly sift through this data to find media of interest? This is where a visualization tool that makes browsing your large-scale media simple. This might be something you take for granted, but seeing thumbnails that quickly refresh on a page and dynamically appear as you scroll, or the ability to quickly run through a carousel of images that does not take forever to load are problems that organizations face today when trying to use commercial consumer applications like to Google Cloud Platform to store large scale-media. In short, you need to adopt a solution that has a flexible and scalable backend that is built for big data and lets you quickly browse your media with minimum load times. Remember, having all this data in one place will be essential to the success of your computer vision model.
What folder was that one picture in again? You do not want to be stuck with this question when dealing with millions of media files. An easy Google-like search bar will help you quickly search by name and find your media of interest. Some computer vision platforms also let you use a powerful tool called “visual search,” which will automatically detect the content of images and let you search for that, making media even more discoverable. Remember that maintaining quick search performance for large-scale media is a unique problem and requires a system built to scale with increasing dataset size.
Metadata Management and Filters
Speaking of the discoverability of media in large datasets, let’s talk about the blessing that is metadata. Metadata allows you to save “data about your data,” things like this image was captured on October 4th by the Linespex camera in a factory in Antigua exposes so much more information about media than just the file name of an image. What’s more is that some data management platforms let you create filters based on your metadata that will let you more granularly find your media of interest.
Where did this file come from again? Full data provenance will help you easily answer this question by letting you understand where your media is coming from without having to guess or trace down source systems. Data provenance means that you will know what media is coming from what system and how often that system is sending new media to the platform. Most organizations will use this information to then select new training data based on operational needs. For example, if the performance of a model detecting defects on production line 7 is down, let’s first try to get a hold of some fresh media from the camera monitoring that line.
The key to moving computer vision models out of R&D and into production requires better management of your enterprise media. This is critical for companies to have the ability to leverage data to have a competitive advantage. Setting yourself up for success means you’ll take data management seriously when selecting your computer vision partners.
Murtaza Khomusi is an experienced go-to-market leader in the Data and AI space. He builds his experience from successful startup-to-public transitions from large organizations like Palantir and most recently lead product marketing at a series-A computer vision startup, CrowdAI. A builder at heart, he is hyper-focused on thoughtful strategy that aligns with business milestones and day-to-day execution. He is a strong believer in the words of Drucker, "culture eats strategy for breakfast" and "if it is not written down it does not exist." He has technical expertise in digital transformation, cloud migration, AI, system integration, data for decisions, analytics, computer vision, cybersecurity, and SaaS markets.