The Master Guide to Data Curation and Auto-Curation for Computer Vision

With the global market for annotation tools predicted to reach $3 billion by 2028, it's a clear indication that the developmental needs of sustaining artificial intelligence, computer vision, and machine learning systems of today require robust data management solutions. These needs naturally correlate with the exponential rate of growth for AI applications and the increasing number of industries they are employed in - from transportation and manufacturing to healthcare and agriculture.

 

From an industry-standard perspective, data processing methods were mainly manual until recent years, but with higher-scale ML projects demanding more precise data and a bulk amount of it - specific to a model's purpose and application - times are changing. 

 

Learn all you need to know about these disruptive and informative methods in our latest whitepaper, a master guide to data curation and auto-curation detailing the major challenges of meeting the demands in a growing number of industries and sectors.
 


What you'll learn in this whitepaper:

  • ⭐The right tools to curate data for high-scale applications.
  • 💯Data curation best practices for a modernized approach to computer vision development.
  • 💰How to automate the curation aspect of data preparation to save time and money.
  • 👨‍💻How to optimize your overall ML team workflow.

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