The growth stage of applied AI and MLOps

Applied artificial intelligence tops the list of 14 most influential technology trends in McKinsey & Company’s “Technology Trends Outlook 2022” report

For now, applied AI (which might also be referred to as “enterprise AI”) is mainly the use of machine learning and deep learning models in real-world applications. A closely related trend that also made it to McKinsey’s top-14 list is “industrializing machine learning,” which refers to MLOps platforms and other tools that make it easier to train, deploy, integrate, and update ML models in different applications and environments.

McKinsey’s findings, which are in line with similar reports released by consulting and research firms, show that after a decade of investment, research, and development of tools, the barriers to applied AI are slowly fading.

Large tech companies, which often house many of the top machine learning/deep learning scientists and engineers, have been researching new algorithms and applying them to their products for years. Thanks to the developments highlighted in McKinsey’s report, more organizations can adopt machine learning models in their applications and bring their benefits to their customers and users.

The challenges of applied machine learning

The recent decade has seen a revived and growing mainstream interest in artificial intelligence, mainly thanks to the proven capabilities of deep neural networks in performing tasks that were previously thought to be beyond the limits of computers. During the same period, the machine learning research community has made very impressive progress in some of the challenging areas of AI, including computer vision and natural language processing.

The scientific breakthroughs in machine learning were largely made possible because of the growing capabilities to collect, store, and access data in different domains. At the same time, advances in processors and cloud computing have made it possible to train and run neural networks at speeds and scales that were previously thought to be impossible.

Some of the milestone achievements of deep learning were followed by news cycles that publicized (and often exaggerated) the capabilities of contemporary AI. Today, many companies try to present themselves as “AI first,” or pitch their products as using the latest and greatest in deep learning.[…]