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New AI workbench helps data scientists explore, search, curate and refine visual data at scale
Akridata, a software company with an end-to-end product suite that supports both smart ingestion and smart exploration of visual data, has officially launched Akridata Data Explorer, which reduces cost and complexity while accelerating business value. announced its launch. provides data science teams with tools to easily explore, search, analyze, and compare visual data to improve data sets and improve model training.
With the number of cameras deployed worldwide more than doubling in the last five years, the amount of visual data is growing at an unprecedented rate. However, the tools and procedures for handling this vast amount of data have not kept up with growth. For many people, data ingestion and curation remains a largely manual and time-consuming project. On average, data scientists spend more than 45% of their time manipulating and cleaning data.
Akridata CEO and co-founder Vijay Karamcheti said: “With 50 billion cameras around the world and the data they generate growing exponentially, he has the tools to help scientists efficiently explore and analyze this data. is essential.”
Within the first two months of the launch of Data Explorer in late 2022, Akridata’s platform subscribers increased more than 5x. This highlights the demand for tools that help improve the efficiency and productivity of data scientists working with visual data and accelerate production-grade AI models. Accuracy.
Helge Jacobsen, Senior Deep Learning Engineer at Veo, said: “There is a high threshold for when it starts to make sense, but once that threshold is reached, the investment starts to pay off at a high rate.”
Akridata Data Explorer is the first platform designed specifically for processing visual data in the ML lifecycle. Founded by a team of entrepreneurs with deep technical expertise in solving image processing challenges, Akridata collaborates with computer vision data science teams to find solutions for larger challenges to improve model accuracy. I quickly realized it was in visual data retrieval, clustering, and selection.
Akridata Data Explorer provides data scientists with tools across the AI and MLops lifecycle, including:
- Visualize and drill down large data sets as clusters based on embeddings (e.g. discover different behaviors by vehicles, people, etc.).
- Finding data (e.g. finding additional instances of objects found within a user-specified bounding box)
- Identify representative deduplicated datasets to reduce class imbalance and reduce data labeling costs.
Technical Lead Bikram Baruah said: – AI at the University of Sheffield Advanced Manufacturing Research Centre. “Data scientists typically visualize and understand data before building models and performing statistical analyses, but this is not as easy as with numerical or tabular data.”
Sanjay Pichaiah, Vice President of Products and GTM at Akridata, said: “Selection of training datasets is more important than model parameters to bring AI models to production grade. There is a desperate need for tools to help data scientists make informed and intelligent data selections. It has been.”
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