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AI-powered visual data platform Akridata has announced the launch of its flagship product Data Explorer in the Azure marketplace. Data Explorer is specifically designed to process visual data in the machine learning (ML) lifecycle, enabling data science teams to easily explore, search, analyze, and compare visual data to improve datasets and model training.
The Data Explorer platform provides virtual connections to multiple data sources, enables the exploration of visual data on untagged datasets, enables image-based similarity searching, supports viewing model performance from multiple perspectives, and enables data comparison across numerous sets .
“One of the standout features of our platform is the ability to process massive amounts of visual data without performance issues or infrastructure limitations. This allows companies to store and analyze data at scale without having to worry about the usual headaches of managing large data sets,” Vijay Karamcheti, CEO and co-founder of Akridata, told VentureBeat. “With our secure and scalable platform, users can finally get the insights they need to improve business and gain a competitive advantage.”
By making Data Explorer available in the Microsoft Azure Marketplace, Akridata aims to provide a higher level of accessibility and ease of use for data scientists seeking insights from complex data sets and accelerate the path to production-level AI model building.
“We are thrilled that Data Explorer is now available on the Microsoft Azure Marketplace,” said Sanjay Pichaiah, VP of products and GTM at Akridata. “With this collaboration, we are strengthening global access to a cloud-based tool that helps data scientists explore, manage and use visual data at scale.”
“Azure offers a variety of platform integrations, including Azure Data Factory, Azure Databricks, and Azure Synapse Analytics, that integrate seamlessly with Data Explorer,” said Karamcheti. “Customers could get even more value from their data by seamlessly integrating our platform into their existing Azure-based data processing and analytics workflows.”
Akridata is also on the AWS marketplace. The company said that by being a regular AWS partner, Akridata could reach a wider audience and increase its impact in the technology industry.
Improving AI development pipelines
Data Explorer is designed to help data science teams use visual data to improve datasets and model training. The company claims that it is the first platform to focus solely on processing visual data in the machine learning (ML) lifecycle.
“As the amount of visual data has exploded, the need to manage and select training sets has become paramount,” says Karamcheti. “Data Explorer enables data scientists to quickly and easily explore, search, compare, and analyze over one million frames of visual data. By dramatically reducing the time spent on data selection and curation, organizations can stop wasting time labeling data and focus on accelerating their path to model accuracy.”
Karamcheti said another benefit of using the platform is the ability to explore visual data on unlabeled datasets by combining traditional metadata-based filtering with content-based latent structure exploration. This allows users to better understand the inherent clustering or segmentation structure of the dataset.
The platform can also perform image-based searches on millions of images in seconds, which can be further refined through interactive scoring on a subset of data to search for domain-specific attributes by combining active search techniques.
A data-centric way to manage visual data
Karamcheti believes that the key to controlling the growth of visual data will be moving from a model-centric approach to a data-centric approach.
“Despite the ever-growing amount of visual data in our world, AI continues to rely on a model-centric approach. The problem with this was that it relied largely on rules and heuristics. Rather, data should be at the root of every decision made,” he explained. “The potential use of visual data to improve real-world AI applications is only huge if we can find the algorithmic resources to assess, store, manage and select visual data.”
The company said the platform addresses the issue of data privacy and security by providing users with granular control over access to data and compliance with legal requirements. It provides end-to-end data encryption in transit and at rest and integrates with existing authorization mechanisms to ensure secure access to data.
In addition, the company aims to be a leader in visual data analytics, offering seamless integration with existing workflows and tools, and providing customers with a comprehensive and powerful solution for managing and analyzing visual data.
“Advanced analytics capabilities, such as computer vision and deep learning, can help companies extract valuable insights from visual data,” said Karamcheti. “By unlocking the potential of visual data, we aim to empower businesses to make data-driven decisions with confidence.”
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