Download our newest ebook The Ultimate Guide to Data-Centric AI for Visual Data to see why data scientists are placing more emphasis on the data.
How Is Visual Data Used Today
Today, there are over 50 billion cameras worldwide – and that number is only set to increase. Many of these cameras are to support use cases enabled by AI in a wide range of fields: automotive, healthcare, retail, security, and materials inspection, just to name a few.
For example, let’s say you’re a data scientist with a data set of 1,000,000 images that were collected by test vehicles. Out of all the images collected, you want to search and identify the 100 frames that represent a construction zone. One way to do this would be to downsample the dataset (100:1) to get to around 10,000 images and inspect for construction zone images. But sifting through 10,000 images is tedious and no trivial task.
Unless another coworker has already gone through the images to label each one with a Construction Zone, there is no way to search for the Construction Zone images exhaustively without manually combing through all of the visual data.
That’s a lot of time and effort to find a few select images.
What are the challenges for companies working with visual data?
While training models have become more powerful, the tools to source, select training data, and diagnose model output have simply not kept up. From a management or organizational standpoint, in-demand, expensive data scientists and engineers are stuck on demoralizing and tedious work.
For both data scientists, and organizations that rely on visual data and data scientists, the challenges of visual data science and visual data management are only increasing.
Analyzing Visual Data
One of the most significant challenges for many data scientists is getting a comprehensive view of a visual dataset. Visualizing large volumes of image and video data is no small feat, but it can be instrumental for understanding what the data set truly entails – latent structures, natural clusters, patterns, common characteristics, and more. This becomes even more challenging when trying to map these concepts across multiple datasets.
Transforming high-dimensional images to clustered 2D embedded views presents a better way to build intuition on the dataset.
Biases in Visual Data Sets
Another significant challenge data scientists have to overcome is the fact that data sets often have biases. For example, if you were to collect visual images of people in smaller cities compared to the largest cosmopolitan metros, it is likely that the data from the smaller cities will be much more homogenous. If a model were to be trained on such a data set, it will be naturally biased and would not perform as desired when deployed in a major metropolitan area. So to train the model one has to be aware of how the data is skewed and make deliberate decisions to unbias the training data set.
While this can be done by qualified data scientists and engineers, the scale of the task is very challenging given the volumes of data.
The Need for Visual Data Tools
Historically, the tools to automate and help with many of the processes associated with visual data haven’t existed, at least not commercially. Due to this lack of tools, data science teams have had to turn to tools built-in house and piecemealed solutions. These homegrown solutions, however, aren’t designed and built for scalability or longevity. These are usually made to solve the problem in the short term.
Depending on how the solution was created or implemented, it can also be a rigorous process to maintain and replicate. There are also challenges with training and educating others on how the solution was built and how it works, in case another individual eventually needs to take over or step in.
How Can Data Science Teams Better Use Visual Data
Another major issue with managing visual data lies within the creation of training data sets.
Data scientists must find the kinds of images they need more of and identify new, novelty data to properly create an effective data set to train the model on. In order to remedy this, data scientists must collate the images needed to address imbalances and novelty data sets, but this is far from a simple “one and done” exercise. Scientists need to look at what’s in the data, perhaps run an embedding clustering exercise again to identify, say, only crowded crosswalks where it’s raining, then visually confirm each cluster. Then and only then can scientists select as many images as are needed to properly train the model.
The Akridata Solution
The tools to help data science teams with the challenges of visual data now exist.
Akridata Data Explorer is a custom-developed ML platform built for exploring, analyzing, and curating exascale visual data training sets.
Data Explorer is a developer-friendly AI and MLOps platform specifically designed to handle the challenges of visual data. Data Explorer was built from the ground up by a team of engineers and data scientists who were wrestling with large volumes of data from multiple camera systems using an edge AI compression system and there was still too much data coming back to the servers.
The Akridata platform is designed to:
- Cluster, select, and compare visual data at a massive scale
- Cut time on data selection and curation – up to 15x
- Improve labeling spend effectiveness – up to 25%
- Accelerate path to model accuracy by visualizing data drift, class imbalances
The Akridata platform is used in diverse areas like autonomous and assisted driving, smart cities, medical imaging, genomic analysis, cashier-less retail, and manufacturing. It is available as a SOC 2-compliant SaaS product and as an on-premise solution for customers with the most exacting security requirements.