The machine learning (ML) market, a subset of artificial intelligence (AI) that focuses on training computer algorithms to automate data processes, is not only growing quickly but solidifying its position in both professional and personal settings.
Machine learning benefits users by automating a mix of business operations and everyday use cases for consumers, and more people are realizing these benefits as companies continue to adopt and optimize ML solutions.
Read on to learn about the technology behind machine learning, its applications, and what the market looks like today:
A Closer Look at Machine Learning (ML)
- Machine learning market
- Machine learning features
- Benefits of machine learning
- Machine learning use cases
- Machine learning providers
Also read: Top Machine Learning Companies 2021
Although machine learning is one part of the greater AI market, it is the most commonly implemented form of AI and growing rapidly in business.
The machine learning market reached a value of about $1.41 billion in 2020 and is expected to reach $8.81 billion by 2025, according to 360 Research Reports.
- Algorithm: A program that uses math and logic to adjust its performance and behavior based on training data.
- Training data: This data, often unstructured and made up of thousands of data points, is used as a sample data set, so ML algorithms can learn what to expect and build habits before encountering real-world data.
- Supervised and unsupervised learning: Supervised learning provides labeled data and expected outputs, while unsupervised learning provides unlabeled data and requires the ML model to learn potential outputs from the training data.
- Deep learning: Deep learning is a type of ML that is designed to work like the human brain, allowing these models to closely mimic human behaviors in different scenarios.
More on deep learning: AI vs. Machine Learning vs. Deep Learning: Subsets of Artificial Intelligence
Real-time user experience (UX) assistance
A big focus of machine learning is text analysis, with the purpose of mimicking real customer service actions through data training algorithms. As a result, tools like chatbots and recommendation engines have been developed, providing users with real-time assistance and catered user experiences when they need it. As a bonus, these tools continue to learn as they interact with customers, and they also provide strategic demographic data to a company’s data scientists.
Efficient big data analytics
Data scientists and traditional data tools successfully extract meaningful insights from company data on a regular basis, but there are always the challenges of limited time and human error when sifting through huge sets of big data. Machine learning models are often trained to do the hard work of data analytics. Once they’re trained to understand a data set, they can run behind the scenes and work through large sums of structured and unstructured data.
Automation of business operations
Big data analytics is only one area of business operations that is simplified through machine learning. MLOps, or the practice of automating business operations with machine learning tools, has eliminated many of the routine tasks involved in database management, network security monitoring, and business intelligence (BI). As a result, the expert staff that focuses so much time on these tasks can have more time to work on specialized tasks for the company.
David P. Mariani, founder and CTO of AtScale, a BI and data analytics company, believes that machine learning, specifically autoML, makes data democratization and machine learning possible for a larger number of employees:
“AutoML tools now automate the process of data integration, model selection, training, and fitting to help data citizens to do the job of an advanced data scientist,” Mariani said. “Using AutoML tools, ordinary analysts can now generate models to predict future sales and inventory levels or to create models to anticipate customer churn.”
Voice and text accessibility
Whether it’s a barrier related to language or a user’s disability, text, voice, and sentiment analysis are increasingly being used to improve multimedia and web experiences for users. Particularly with assistive technology users in mind, machine learning algorithms are trained to listen to or otherwise translate digital content, so automated captioning can be provided with accessibility in mind.
More on data analytics: Data Analytics Market Trends 2021
In virtually any case where volumes of unstructured big data need to be combed through to program a system, companies are looking for ways to create machine learning algorithms that automate the data-parsing process.
Some of the most common use cases are customer service- and user experience-driven, with natural language processing algorithms used to program chatbots, virtual assistants and customer service agents, and search engine recommendations.
Other machine learning models are on the frontiers of new technology development, with algorithms used to program computer vision, smart factory operations, and other back-office operations.
But in some cases, machine learning can be used maliciously. More bad actors are adopting adversarial machine learning to hack into and retrain machine learning models to their benefit.
Experts on machine learning use cases
“Currently, some markets are gaining momentum across many industries, particularly in text analytics, customer sentiment analysis, recommendation systems, and fraud detection. In addition, machine learning has transformed data analytics, making it more accessible, more actionable, and more efficient, allowing for extracting rich insights from data.” -Bartowsz Wojtowicz, machine learning engineer at Netguru
“The most obvious use cases in the field of NLP and machine learning are voice assistants and chatbots. But behind the corporate walls, other systems are put in place to improve the overall efficacy of an enterprise: routing emails to the correct recipient and flagging those necessitating an immediate reaction; extracting key information from legal agreements or other documents for better compliance and risk management; comparing historical offerings and competitors’ documentation to quickly craft customized quotes. … The range of applications are very broad.” -Marie Pierre Garnier, VP at Cortical.io
Dozens of technology providers offer pre-packaged products, consulting, and other services in the realm of machine learning.
In many cases, specialized startups are popping up to focus on one key area or use case for machine learning, such as customer sentiment analysis or search recommendation engines.
However, when it comes to a long-term machine learning strategy and a wide portfolio of enterprise-level solutions, these top companies provide a high volume of quality solutions:
- Microsoft Azure
- Google Cloud
The majority of companies in this space are young, specialized, and constantly changing their models and methodologies to create better machine learning products. With so much momentum and development happening in the market, expect to see the top machine learning players and products evolve in the coming years.
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