Visual Data and AI Modeling, Meet Akridata Data Explorer

AI and ML have made incredible strides in recent years, but we are rapidly approaching a critical impasse.

For modeling and AI/ML infrastructure to continue progressing and succeed in real-world applications, improving the quality of data is the only way to make it possible. As Andrew Ng has recently campaigned, we now need to make the crucial shift to Data-Centric AI. In the same way that vehicles need high-quality refined oil to perform at their best, AI models need top-quality and refined data to truly meet their potential and surpass what we envision.

The challenge is that even as the volume of visual data continues to increase, and training infrastructure becomes more powerful, the tools to source, select training data, and diagnose model output have not kept up. The task has often fallen back on skilled data scientists who have to spend time searching for images manually, diagnosing edge cases with very basic or homegrown tools. Given these frictions, it takes much longer to train and refine production-ready models, and consumes valuable time when data scientists could be doing something else.

Enter Akridata Data Explorer.

Who is Akridata?

Akridata is the AI platform for visual data.

Akridata was founded by serial entrepreneurs with deep technical expertise in solving image processing challenges.  Akridata initially focused on processing visual data at the edge (in fact, Akri is the Greek word for edge), but quickly realized from working with CV data science teams that the bigger challenges lay in searching, clustering and selecting visual data to accelerate model accuracy. Pretty soon, data scientist customers were asking for support in model error analysis and other features.

Thus Akridata Data Explorer was born.

As volumes of visual data have exploded, the need to manage and select training sets has become paramount.

Guesstimates on class imbalances and model training issues don't cut it when a company's future rides on AI related products.  To date, data scientists and engineers have had to work with hacked together tools, or work late nights doing manual work that they'd rather not do.

There’s no need to any longer. Akridata Data Explorer is the first developer-friendly workbench that curates complex images and video data to improve AI model building. Data Explorer enables data scientists to quickly and easily explore, search, compare, and analyze more than one million frames of visual data. By drastically reducing the time needed to be spent on data selection and curation, organizations avoid wastage on data labeling spend and accelerate their path to model accuracy.

Why Akridata Data Explorer?

The need for data scientists is growing at a rapid rate. It is currently projected that the Data Science field will grow by over 28% through 2026. In the US alone, there is a need for more than one million data scientist roles to be filled with roughly only three-hundred thousand data scientists.

For society to continue moving forward towards a more autonomous and connected world, scalable solutions and processes must exist. Data scientists don’t want to be bogged down by the manual input and drudgery that often comes with labeling and AI training, nor is it a valuable use of their time and resources.

Let’s say you have 1,000,000 images from a traffic camera and you want to search through them to identify 1,000 frames that have yellow cars. Sure, you could downsample to get to around 100,000 images, but how would you still go about identifying the 1,000 frames that have yellow cars? Unless someone has already gone through the images to label each of them that contain the car, there is no ability to search for these without taking the time to manually comb through the visual data. Until now.

Data scientists can leverage Akridata Data Explorer to reduce hours of work to minutes, reduce costs and time needed for labeling, and accelerate the process to model accuracy.

Improve labeling spend and effectiveness

Easily identify novelty visual data sets and compare label quality across sources. Users can easily surface and explore interesting data clusters to continue improving data sets.

Simplify model and training set analysis

Data Explorer makes it easy to visualize and quantify data drifts, identify and resolve class imbalances within ML, and adjust bias and de-bias training sets.

Improve model accuracy

Unique visualizations combining errors, stats and content features helps users identify image clusters that contribute to model errors and help streamline the process of increasing model accuracy.

To date, we have processed over twenty petabytes (and growing) of visual data. The amount of visual data will continue to increase, meaning the need to be able to digest and utilize this data will increase hand-in-hand. The abilities of AI and ML are beyond that of human comprehension and capabilities, the time is now to leverage these tools before we’re drowning in data.

In short, Akridata Data Explorer will transform the way data scientists work with visual data.

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