Machine Learning Market Analysis and Forecast 2021

The machine learning (ML) market, a subset of artificial intelligence (AI) that focuses on training computer algorithms to automate data processes, not only is growing rapidly, but is consolidating its position in business and personal environments.

Machine learning benefits users by automating a mix of business operations and everyday consumer use cases, and more and more people are realizing these benefits as businesses continue to adopt and optimize ML solutions.

Read on to learn more about the technology behind machine learning, its applications, and what the market looks like today:

A closer look at machine learning (ML)

Read also : Best Machine Learning Companies 2021

Machine Learning Market

Although machine learning is part of the largest AI market, it is the most commonly implemented and rapidly growing form of AI in business.

The machine learning market reached a value of around $ 1.41 billion in 2020 and is expected to reach $ 8.81 billion by 2025, according to 360 research reports.

Machine learning features

  • 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 dataset, 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 designed to function like the human brain, allowing these models to closely mimic human behaviors in different scenarios.

Learn more about deep learning: AI vs Machine Learning vs Deep Learning: subsets of artificial intelligence

Benefits of machine learning

Real-time support for user experience (UX)

Text analysis is one of the primary goals of machine learning, with the goal of mimicking actual customer service actions through data learning algorithms. As a result, tools such as chatbots and recommendation engines have been developed, providing users with real-time support and tailored user experiences when they need it. As a bonus, these tools continue to learn as they interact with customers, and they also provide strategic demographics to a company’s data scientists.

Efficient big data analysis

Data scientists and traditional data tools routinely successfully extract meaningful insights from business data, but there are still challenges of limited time and human error when filtering out huge sets of data. big data. Machine learning models are often trained to do the hard work of data analysis. Once they are trained to understand a set of data, they can work behind the scenes and work with large amounts of structured and unstructured data.

Automation of business operations

Big data analytics is just one area of ​​business operations made easier by 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, expert staff who devote so much time to these tasks may have more time to work on specialized tasks for the business.

David P. Mariani, Founder and CTO of At scale, a BI and data analytics company, believes that machine learning, especially autoML, is making data democratization and machine learning possible for more employees:

“AutoML tools now automate the process of data integration, model selection, training and adjustment to help data citizens do the job of an advanced data scientist,” said Mariani. “Using AutoML tools, ordinary analysts can now generate models to predict future sales and inventory levels or to create models to anticipate customer churn rate.”

Voice and text accessibility

Whether it is a barrier related to language or a user’s disability, text, voice and sentiment analysis is increasingly used to improve users’ multimedia and web experiences. Especially with assistive technology users in mind, machine learning algorithms are trained to listen to or otherwise translate digital content, so that automated captioning can be provided with accessibility in mind.

Learn more about data analysis: Data Analytics Market Trends 2021

Machine learning use cases

In virtually any case where volumes of unstructured big data must be combed through to program a system, companies are looking for ways to create machine learning algorithms that automate the process of data analysis.

Some of the most common use cases focus on customer service and user experience, with natural language processing algorithms used to program chatbots, virtual assistants, and customer service agents, along with recommendations from search engine.

Other machine learning models are at 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 and more bad actors adopt contradictory machine learning to hack and recycle machine learning models to their advantage.

Experts on machine learning use cases

“Currently there are markets that are gaining momentum in many industries, particularly in text analytics, customer sentiment analysis, referral systems and fraud detection. Additionally, machine learning has transformed data analytics, making it more accessible, more actionable, and more efficient, enabling rich insights to be extracted from data. -Bartowsz Wojtowicz, Machine Learning Engineer at Netguru

“The most obvious use cases in NLP and machine learning are voice assistants and chatbots. But behind the walls of the company, other systems are put in place to improve the overall efficiency of a company: route emails to the right recipient and flag those requiring an immediate response; extract key information from legal agreements or other documents for better compliance and better risk management; compare historical offers and competitor documentation to quickly create personalized quotes. … The range of applications is very wide. -Marie Pierre Garnier, vice-president at Cortical.io

Machine learning providers

Dozens of technology vendors offer prepackaged products, advice, and other services in the area of ​​machine learning.

In many cases, specialist startups appear to focus on a 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 large portfolio of enterprise-level solutions, these large companies provide a high volume of quality solutions:

  • IBM
  • Microsoft Azure
  • AWS
  • Google cloud
  • SAS
  • Selling power
  • H2O.ai
  • Alteryx
  • Netguru
  • Data bricks
  • Intelligence
  • RapidMiner

The majority of companies in this space are young, specialized, and are constantly changing models and methodologies to create better machine learning products. With so much momentum and development in the market, expect to see the best players and machine learning products evolve in the years to come.

Read more : Ask an executive: enterprise data analytics


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