6 Things You Need To Know About Data Management And Why It Matters For Computer Vision
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. So there is less of a focus on tweaking the code to optimize performance and, instead, a larger emphasis on training an industry-standard model with your unique media to teach it how 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.
Connect with known industry database architectures or cloud providers without the need for custom python scripts. 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!
Organize your large-scale data into folders and make sure your data lake does not become a data swamp. 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.
