As noted in this column: "If data is the oil of the information age, Machine Learning is the engine."
A study by Machine Learning Market indicates that the global machine learning market size reached US$ 6.9 billion in 2018 and is projected to reach US$ 96.7 billion by 2025.
In a landscape where understanding how to work with data is increasingly critical, the science behind it—data science—is becoming more accessible. This is driving the gradual "democratization" of data science.
Looking toward 2022, momentum is growing around "small data." This paradigm emerged to facilitate fast cognitive analysis of the most vital data at the network edge—situations where time, bandwidth, or energy consumption are critical, and there is no time to send data back and forth to a centralized cloud server (as is the case with autonomous vehicles).
Closely linked to small data is another emerging concept: TinyML. This refers to machine learning algorithms designed to minimize memory footprint so they can run on low-power hardware right where the action happens (i.e., at the network edge). According to the author of the article, "In 2022, we will see these algorithms appear across a growing number of embedded systems, from wearable devices to home appliances, automobiles, industrial equipment, and agricultural machinery."
Predictive Analytics
In the coming year, Machine Learning, Deep Learning, and data mining will play a pivotal role in optimizing data-driven customer service and customer experience (CX), ensuring increasingly valuable, enjoyable, and hyper-personalized user journeys. Advances in predictive analytics will be key to achieving this.
Furthermore, machine learning will be central to the convergence of AI, the Internet of Things (IoT), cloud computing, and ultra-fast networks like 5G. This synergy will enable IoT devices "to act intelligently and interact with each other with minimal human intervention, driving a wave of automation and giving rise to smart homes, factories, and cities."
From this perspective, ML algorithms will also be essential "to enable new forms of data transfer across ultra-fast 5G networks, facilitating everything from traffic routing to ensure optimal transfer speeds to full automation."
Artificial Intelligence
AutoML—short for Automated Machine Learning—is another key trend aiming to create tools and platforms that anyone can use to build their own ML applications. This is especially tailored for domain experts across various fields who lack the coding skills needed to apply AI to their specific challenges.
AutoML automates data preparation, cleaning tasks, model building, and the creation of algorithms and neural networks. The goal is that, very soon, "anyone with a problem to solve or an idea to test will be able to apply machine learning through simple, user-friendly interfaces."
To delve deeper into the expected impact of ML for 2022, we invite you to read the full article here.