Today, there are over 45 billion cameras worldwide – and that number is only set to increase. Why such an explosion? 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.
The computer vision market is growing steadily and is expected to maintain a CAGR of 6.4% from 2020 through 2027.
As new uses for cameras and the data they create become available, the need to be able to sift through these datasets to find the most valuable subsets becomes increasingly essential. Data science teams simply do not have the time or resources to do it all manually.
That’s why we need to change the way we manage and engage with visual data sets, through the application of data-centric AI.
Download our eBook to learn why it is time to adopt a Data-Centric AI approach to working with visual data and how to combat the challenges currently facing data science teams, including:
- The Challenges of Managing Visual Data in a Data-Centric World
- The Trouble with Current Tools
- The Support Data Science Teams Need
- The Akridata Solution