In recent decades, deep learning-fueled technologies like machine learning and computer vision have developed at a steady and exponential rate, with the CV and AI hardware market predicted to reach a value of 48.6 billion in 2022, a feat attributed to separate but equally impactful contributions from both industry experts and academic research efforts, focused on furthering comprehension to competently implement AI in numerous real-world applications on a global scale.
Due to those impacts, many manual development processes were rightfully reformed in today's world to match the rapid pace of adopting enterprise AI in modern technologies. Many are now active and playing a crucial role or fulfilling an important societal function - among those reformations, most notably, the inclusion and widespread use of diverse tooling and a sound testing infrastructure that data scientists and machine learning engineers can utilize alike.
Rather than exhausting their time and resources on coding every single execution or rule for CV models to follow, AI teams can now use MLOps platforms and other tools designed to be seamlessly integrated into the traditional manual procedures. A procedure currently undergoing a transitional period of alteration to enable support of next-gen AI performance standards.
Through a two-pronged approach of a capable data annotation platform and configuration tools that specifically meet the needs of data scientists on one side of the CV pipeline to collect and store data; and engineers to funnel that data into models and configure it accordingly to power applications for specific use cases on the other end - the industry is poised to surpass previous financial and technical limitations to deploying accurate and high-performing architectures to enable evolutionary AI.
The modernized methods of building robust and flexible AI models require the right MLOps platform. That platform should offer tools that streamline or otherwise automate manual tasks, freeing data practitioners to focus on programming needs and less on data quality and selection - which remains a significant determinant of model performance. The Superb AI Suite and the DataOps module automate the most challenging aspects of executing data-driven machine learning.
Learn all you need to know about these disruptive and transformative methods in our master guide to data curation and auto-curation for computer vision applications, an inclusive whitepaper detailing the major constraints data scientists and ML engineers face when working with the data that is vital to training and upgrading their CV models for the demands of 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 cost.
How to optimize your overall ML team workflow.