The growth stage of applied AI and MLOps – TechTalks

This article is part of our series that explores the business of artificial intelligence

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.

However, bringing ML from research labs to actual products presents several challenges, which is why most machine learning strategies fail. Creating and maintaining products that use machine learning requires different infrastructure, tools, and skill sets than those used in traditional software. Organizations need data lakes to collect and store data, and data engineers to set up, maintain, and configure the data infrastructure that makes it possible to train and update ML models. They need data scientists and ML engineers to prepare the data and models that will power their applications. They need distributed computing experts that can make ML models run in a time- and cost-efficient manner and at scale. And they need product managers who can adapt the ML system to their business model and software engineers who can integrate the ML pipeline into their products.

The data, hardware, and talent costs that come with enterprise AI have been often too prohibitive for smaller organizations to make long-term investments in ML strategies.

It is against this backdrop that the McKinsey & Company report’s findings are worth examining.

Growth in applied AI

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The report ranks tech trends based on five quantifiable measures: search engine queries, news publications, patents, research publications, and investment. It is worth noting that such quantitative measures don’t always paint the most accurate picture of the relevance of a trend. But tracking them over time can give a good estimate of how a technology goes through the different steps of hype, adoption, and productivity cycle.

McKinsey further corroborated its findings through surveys and interviews with experts from 20 different industries, which gives a better picture of what the opportunities and challenges are.

The report is based on 2018-2021 data, which does not fully account for the downturn that capital markets are currently undergoing. According to the findings, applied AI has seen growth in all quantifiable measures except for the “search engine queries” category (which is a grey area, since AI terms and trends are constantly evolving). McKinsey gives applied AI the highest innovation score and top-five investment score with $165 billion in 2021.

(Measuring investment is also very subjective and depends on how you define “applied AI”—e.g., if a company that secures a huge round of funding uses machine learning as a small part of its product, will it count as an investment in applied AI?)

In terms of industry relevance, some of the ML applications mentioned in the report include use cases such as recommendation engines (e.g., content recommendation, smart upselling), detection and prevention (e.g., credit card fraud detection, customer complaint modeling, early disease diagnosis, defect prediction), and time series analysis (e.g., managing price volatility, demand forecasting). Interestingly, these are some of the areas of machine learning where the algorithms have been well-developed for years. Though computer vision is only mentioned once in the use cases, some of the applications might benefit from it (e.g., document scanning, equipment defect detection).

The report also mentions some of the more advanced areas of machine learning, such as generative deep learning models (e.g., simulation engines for self-driving cars, generating chemical compounds), transformer models (e.g., drug discovery), graph neural networks, and robotics.

This further drives the point that the main hurdle for the adoption of applied AI has not been poor machine learning algorithms but the lack of tooling and infrastructure to put well-known and -tested algorithms to efficient use. These constraints have limited the use of applied AI to companies that don’t have enormous resources and access to scarce machine learning talent.

In recent years, there has been tremendous advances in some of these fronts. We’ve seen the advent and maturity of no-code ML platforms, easy-to-use ML programming libraries, API-based ML services (MLaaS), and special hardware for training and running ML models. At the same time, the data storage technologies underlying ML services have evolved to become more flexible, interoperable, and scalable. Meanwhile, some enterprise AI companies have started to develop and provide ML solutions for specific sectors (e.g., financial services, oil and gas, retail).

All these developments reduce the financial and technical barriers to adopting machine learning in their business models. In many cases, companies can integrate ML services into their applications without having in-depth knowledge of the algorithms running in the background.

According to McKinsey’s 2021 survey of industry experts, “56 percent of respondents said their organizations had adopted AI, up from 50 percent in the 2020 survey. The 2021 survey also indicated that adopting AI can have financial benefits: 27 percent of respondents attributed 5 percent or more of their companies’ EBIT to AI.”

Advances in MLOps

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The second AI-related tech trend included in the McKinsey & Company report is the “industrialization of machine learning.” This is a vague term and has much overlap with the applied AI category, so the report defines it as “an interoperable stack of technical tools for automating ML and scaling up its use so that organizations can realize its full potential.”

The technologies underlying advances in this field are mostly the same that have led to the growth of applied AI (better data storage platforms, hardware stacks, ML development tools and platforms, etc.). However, one specific field that has seen impressive developments in recent years is machine learning operations (MLOps), the set of tools and practices that streamline the training, deployment, and maintenance of ML models.

MLOps platforms provide tools for curating, processing, and labeling data; training and comparing different machine learning models; versioning control for dataset and models; deploying ML models and monitoring their performance; and updating ML models as their performance decays, their environment changes, and new data becomes available. MLOps platforms, which are growing in number and maturity, bring together several different tasks that were previously carried out desperately and in an ad hoc fashion.

According to the report, the industrialization of machine learning “can shorten the production time frame for ML applications by 90 percent (from proof of concept to product) and reduce development resources by up to 40 percent.”

Challenges remain for enterprise AI

machine learning programming

Despite the advances in applied AI, the field still has some gaps to bridge. The McKinsey report states that the availability of resources such as talent and funding remain two of the hurdles for the further growth of enterprise AI. Currently, the capital markets are in a downturn, and all sectors, including AI, are facing problems funding their startups and companies.

However, despite the AI capital pie becoming smaller, funding has not stopped altogether. According to a recent CB Insights report, companies that have already achieved product/market fit and are ready for aggressive growth are still managing to secure mega-funding rounds (above $100 million). This suggests that companies that don’t have the margins to launch new ML strategies will have a hard time receiving outside funding. But applied ML platforms that have already cornered their share of the market will continue to draw interest from investors.

Another important challenge that the report mentions is data risks and vulnerabilities. This is becoming an increasingly critical issue for applied machine learning. Like its development lifecycle, the security threat landscape of machine learning is different from that of traditional software. The security tools used in most software development platforms are not designed to detect adversarial examples, data poisoning, membership inference attacks, and other types of threats against ML models.

Fortunately, the security and machine learning communities are coming together to develop tools and practices for creating secure ML pipelines. As applied AI continues to grow, we can expect other sectors to speed up their adoption of ML, which will in turn further accelerate the pace of innovation in the field.

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