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What Business Leaders Need to Know About Machine Learning in 2022

February 9, 2022


Machine learning (ML) is already a big deal in the business world. Why? Journalist Nick Kolakowski (@nkolakowski) explains, “Companies widely expect that artificial intelligence (AI) and machine learning will fundamentally change their operations in coming years. To hear executives talk about it, apps will grow ‘smarter,’ tech stacks will automatically adapt to vulnerabilities, and processes throughout organizations will become entirely automated.”[1] Eric Siegel (@predictanalytic), a former computer science professor at Columbia University, insists business leaders should be focusing more on machine learning than AI. He explains, “A.I. is a big fat lie. Artificial intelligence is a fraudulent hoax — or in the best cases it’s a hyped-up buzzword that confuses and deceives. The much better, precise term would instead usually be machine learning — which is genuinely powerful and everyone oughta be excited about it.”[2] It is important to note that Siegel writes “usually” machine learning the best term. There are other technologies that fall under the AI umbrella, like cognitive computing, that use machine learning in combination with other techniques. These technologies are also proving their value in a business setting. Nevertheless, understanding more about machine learning is probably the best place for business leaders to start their journey down the AI path.


What is machine learning?


The U.S. Department of Energy explains, “Machine learning is the process of using computers to detect patterns in massive datasets and then make predictions based on what the computer learns from those patterns. This makes machine learning a specific and narrow type of artificial intelligence. … All machine learning is based on algorithms. In general, algorithms are sets of specific instructions that a computer uses to solve problems. In machine learning, algorithms are rules for how to analyze data using statistics. Machine learning systems use these rules to identify relationships between data inputs and desired outputs — usually predictions. To get started, scientists give machine learning systems a set of training data. The systems apply their algorithms to this data to train themselves how to analyze similar inputs they receive in the future.”[3] Tech writer Cynthia Harvey explains there are four ways algorithms can be used to train machines.[4] They are:


• Supervised Learning: “Supervised learning requires a programmer or teacher who offers examples of which inputs line up with which outputs.”


• Unsupervised Learning: “Unsupervised learning requires the system to develop its own conclusions from a given data set.”


• Semi-supervised Learning: “Semi-supervised learning, as you probably guessed, is a combination of supervised and unsupervised learning.”


• Reinforcement Learning: “Reinforcement learning involves a system receiving feedback analogous to punishments and rewards.”


One of the most oft-used machine learning techniques is called “deep learning.” The following video contains an excellent explanation of deep learning.



There are other machine learning techniques besides deep learning that can be used to gain actionable insights. For example, the Enterra Autonomous Decision Science™ platform utilizes the world’s largest, commonsense ontology to ensure its machine learning results understand real-world relationships.


Machine Learning Trends


Evgeniy Krasnokutsky, an AI/ML Solution Architect at MobiDev, writes, “Understanding the possibilities and recent innovations of ML technology is important for businesses so that they can plot a course for the most efficient ways of conducting their business. It is also important to stay up to date to maintain competitiveness in the industry.”[5] Krasnokutsky suggests the following ML trends are among the most important for business leaders to know:


No-Code Machine Learning. Krasnokutsky notes, “No-code machine learning is a way of programming ML applications without having to go through the long and arduous processes of pre-processing, modeling, designing algorithms, collecting new data, retraining, deployment, and more.” No-code ML advantages include quick implementation, lower costs, and simplicity. Because no-code/low-code offerings are becoming more popular, they can, in some instances, be used by non-technical personnel. Business leaders need to ensure that any new program doesn’t interfere with legacy systems, especially when they are programmed by non-technical employees.


TinyML. Because bandwidth is at a premium and generated data volume is increasing, more analysis is being done at the edge. As a result, tinyML is growing in importance. Krasnokutsky explains, “By running smaller scale ML programs on IoT edge devices, we can achieve lower latency, lower power consumption, lower required bandwidth, and ensure user privacy. Since the data doesn’t need to be sent out to a data processing center, latency, bandwidth, and power consumption are greatly reduced. Privacy is also maintained since the computations are made entirely locally.”


AutoML. Krasnokutsky writes, “Similar in objective to no-code ML, AutoML aims to make building machine learning applications more accessible for developers. Since machine learning has become increasingly more useful in various industries, off-the-shelf solutions have been in high demand. Auto-ML aims to bridge the gap by providing an accessible and simple solution that does not rely on the ML-experts.”


Machine Learning Operationalization Management (MLOps). According to Krasnokutsky, “Machine Learning Operationalization Management is a practice of developing machine learning software solutions with a focus on reliability and efficiency. … MLOps provides a new formula that combines ML systems development and ML systems deployment into a single consistent method. One of the reasons why MLOps is necessary is that we are dealing with more and more data on larger scales which requires greater degrees of automation. One of the major elements of MLOps is the systems life cycle, introduced by the DevOps discipline.” The systems life cycle includes: Designing a model based on business goals; acquiring and preparing data; training and tuning the ML model; validating the ML model; deploying the software solution with the integrated model; and monitoring and revising the process to improve the ML model. At Enterra Solutions® we call this a “crawl, walk, run” approach.


Unsupervised ML. As noted above, unsupervised machine learning is one of the four techniques that can be used to feed data to a computer. Krasnokutsky writes, “As automation improves, more and more data science solutions are needed without human intervention. Unsupervised ML is a trend that shows promise for various industries and use cases.” At Enterra® we are advancing the field of Autonomous Decision Science™ because we agree that more decisions are going to be made by machines in the years ahead. The following video explains a little more about how ADS™ works.



In his article, Krasnokutsky also discusses full-stack deep learning; generative adversarial networks; reinforcement learning; and few-shot, one-shot, and zero-shot machine learning.


Concluding Thoughts


According to business writer Paramita (Guha) Ghosh, “ML enables businesses to perform tasks on a scale and scope previously impossible to achieve. This unique technology enhances speed, reduces errors, and improves accuracy. Business owners and operators using ML have not only have enhanced their business efficiency with ML tools, but have also discovered new opportunities to outpace their competitors.”[6] Tech writer Jelani Harper adds, “The goal has always been to equip the enterprise with tailored solutions spanning technological approaches that not only justify, but also maximize the use of data for fulfilling the most meaningful business objectives at hand.”[7] The more familiar business leaders become with machine learning, the more likely they are to discover new opportunities to put it to use. That’s why Siegel enthusiastically declares machine learning “is genuinely powerful and everyone oughta be excited about it.”


[1] Nick Kolakowski, “Here’s How Companies are Using A.I., Machine Learning,” Dice, 16 August 2021.
[2] Eric Siegel, “Why A.I. is a big fat lie,” Big Think, 23 January 2019.
[3] U.S. Department of Energy, “Science Made Simple: What Is Machine Learning?” SciTechDaily, 25 December 2021.
[4] Cynthia Harvey, “What is Machine Learning?” Datamation, 3 January 2018.
[5] Evgeniy Krasnokutsky, “Machine Learning Technology Trends To Impact Business in 2022,” Mobidev Blog, 27 August 2021.
[6] Paramita (Guha) Ghosh, “Machine Learning for Organizations: Where Is It Now?” Dataversity, 23 November 2021.
[7] Jelani Harper, “2022 Trends in Data Science: Newfound Ease and Accessibility,” insideBIGDATA, 12 November 2021.

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