The Global Machine Learning (ML) Market Size Was Valued At USD 15.44 Billion In 2021. The Market Is Expected To Grow From USD 21.17 Billion In 2022 To USD 209.91 Billion By 2030, Exhibiting A CAGR Of 38.8% During The Forecast Period.

Machine Learning (ML) Market Overview:

The global effects of the COVID-19 pandemic have been unprecedented and shocking. For example, the demand for machine learning technology is higher than expected in all regions compared to levels before the pandemic. Based on our research, the machine learning (ML) market around the world grew by 36.1% more in 2020 than it did in 2019.

Artificial intelligence includes machine learning as one of its parts (AI). It is a method for analysing data that teaches computers to learn quickly from algorithms and data, just like humans do. The growth of the market is largely due to the growing use of artificial intelligence (AI) and machine learning (ML) technology in end-use industries like healthcare, automotive, retail, and manufacturing, among others. Also, in 2020, a number of countries have put in place quarantine and social isolation policies to lessen the effects of the pandemic. Developers and researchers plan to use ML tools to look at how these changes work. For example, in April 2020, researchers at the Massachusetts Institute of Technology made a model based on the COVID-19 pandemic. Machine intelligence algorithms are used in the model to figure out or predict how the virus will spread and how well quarantine measures will work. Such progress is likely to increase the need for more advanced machine intelligence.

All over the world, the field of artificial intelligence is growing at a very fast rate. Deep learning is part of the artificial intelligence (AI) technology called machine learning (ML). Machine learning is done with the help of the hierarchical level of artificial neural networks (ANN). Because deep learning algorithms are getting better, the global market is likely to grow. A lot of companies are also improving their deep learning skills to drive innovation, which is expected to drive the growth of the machine learning (ML) market across industries and regions.

Market Dynamics:

Latest Trends:

Integration of machine intelligence with analytics-driven solutions will be a growing market trend.

In the past few years, retail analytics has grown by leaps and bounds. Many e-commerce businesses, like Amazon, Alibaba, and eBay, use advanced data analytics technologies to increase sales and improve customer satisfaction. As speech and voice recognition technologies have been researched and developed, cognitive speech coding methods based on ML have come about.

As companies adopt more advanced security frameworks, machine learning will become an important trend in security analytics. With so much data being created and shared across so many networks, it is very hard for cyber professionals to track and analyse possible cyber threats and attacks. As cyber threats become more widespread and complicated, machine learning algorithms also help organisations and security teams predict, track, and recognise cyber-attacks more quickly. So, putting advanced learning features into solutions that are driven by analytics is becoming a major industry trend.

Driving Factors:

The market is likely to grow because there are more and more uses in healthcare.

ML technology is already helping in a number of ways in the healthcare field. In healthcare, this technology can be used to look at millions of different data points and predict outcomes. It can also be used to give fast risk scores and figure out the best way to use resources, among other things.

Finding diseases and making a diagnosis: One of the most important ways this technology is used in healthcare is to find and diagnose diseases and conditions that can be hard to spot. This can be anything from cancers that are hard to spot early on to other diseases that run in families. IBM Watson Genomics is a well-known example of how cognitive computing and genome-based sequencing of tumours can be used together to help find cancer. Berg, a big name in biopharmaceuticals, uses AI to make treatments for diseases like cancer.

Medical Imaging Diagnosis: Computer Vision is a revolutionary technique that combines machine learning and deep learning. Microsoft's InnerEye programme, which focuses on image diagnostic tools for image analysis, has agreed to this.

These things should help the market grow.

Restraining Factors:

The growth of the market will be slowed down by technical problems and a lack of accuracy.

The ML platform has a lot of benefits that help drive the growth of the market. But the platform is missing some parameters that are likely to slow the growth of the market. One of the main things holding the market back is that these algorithms aren't always accurate and are sometimes not well-developed. Precision is very important for businesses that make things and use big data and machine learning. If the algorithm isn't quite right, the wrong things might be made. So, a person has to work with the system until all of the parameters are set and the margin of error is close to or equal to zero. So, this factor may slow the growth of the market.

Segmentation Analysis:

By Component Analysis:

The global market is divided into solutions and services based component. The solution has the largest share of the market in 2021, and it is expected to keep its lead in the years to come. Some of the solutions offered by the market players are IBM's Watson Machine Learning, Oracle's ML, and Microsoft's Azure. These solutions help researchers and data scientists speed up the use of artificial intelligence and machine learning. In the coming years, machine-learning-as-a-service is likely to grow as a market trend.

Professional services and services that are managed are thought of as services. During the forecast period, it is expected that the services will grow a lot. These services help businesses make smart, actionable decisions that improve workflow, end-to-end risk management, and compliance management.

By Enterprise Size Analysis:

By enterprise size, the global market is bifurcated into small and mid-sized enterprises and large enterprises. In 2021, the largest share of the market came from large businesses. This is attributed to the growing implementation of artificial intelligence, and data science technology to introduce quantitative insights into enterprise operations. Large companies are working to improve their services by using deep learning, artificial learning, and decision optimization.

Small and medium-sized businesses are expected to grow at a fast rate over the next few years. AI and ML are expected to be the key technologies that help small and medium-sized enterprises (SMEs) reduce their ICT investments and access digital resources.

By Deployment Analysis:

The market has been split into two groups based on deployment: the cloud and on-premise. Machine intelligence solutions can be set up in the cloud or on-premise by the market players. Alphabet, Inc., for example, makes Google Cloud. The Google cloud has a full set of tools for machine learning and artificial intelligence (AI). In the same way, enterprises that want to configure, maintain, and manage their installations within the enterprise can use on-premise deployments with BigML.

During the forecast period, cloud deployments are expected to grow at a very fast rate. Cloud-based delivery models for deep learning software solutions and services are used because they offer many benefits. Some of the most important ones are flexibility, automatic software updates, disaster management through cloud-based backup systems, and increased efficiency.

In 2020, a big part of the market went to on-premises. This segment would grow because it can do a lot of calculations and meet security standards for data.

By End-user Analysis:

ML technology is used by industries that deal with a lot of data to get insights from the data in real time. The real-time insights give businesses an edge over their competitors and help them work better.

In the healthcare industry before COVID-19, the rise of wearable devices and sensors that can check a patient's health in real time has increased the need for machine intelligence applications. Medical experts can find trends and analyse data with the help of technology, which makes diagnosis and treatment better. But the spread of the new Coronavirus has led to more uses of artificial learning technology in the sector as a whole.

Machine intelligence is used by banks and other financial businesses to stop fraud and find important insights in data. E-Commerce has been a major force in changing the way retail businesses work. Machine intelligence is used by retailers to collect data, analyse it, and use it to give customers a more personalised shopping experience. These are some of the things that make the financial and retail industries want to use ML technology.

In the next few years, the automotive and transportation industries are expected to grow by a lot. Research and development on self-driving cars and other forms of autonomous transportation are driving the need for new solutions.ML technology is used by industries that deal with a lot of data to get insights from the data in real time. The real-time insights give businesses an edge over their competitors and help them work better.

In the healthcare industry before COVID-19, the rise of wearable devices and sensors that can check a patient's health in real time has increased the need for machine intelligence applications. Medical experts can find trends and analyse data with the help of technology, which makes diagnosis and treatment better. But the spread of the new Coronavirus has led to more uses of artificial learning technology in the sector as a whole.

Machine intelligence is used by banks and other financial businesses to stop fraud and find important insights in data. E-Commerce has been a major force in changing the way retail businesses work. Machine intelligence is used by retailers to collect data, analyse it, and use it to give customers a more personalised shopping experience. These are some of the things that make the financial and retail industries want to use ML technology.

In the next few years, the automotive and transportation industries are expected to grow by a lot. Research and development on self-driving cars and other forms of autonomous transportation are driving the need for new solutions.

Regional Insights:

In 2020, North America had the largest market share. Big companies that invest in research and development, like IBM, Amazon.com, and Oracle, make the market in the region bigger. In North America, the market is likely to grow because of high investments and a well-developed IT infrastructure. For example, the U.S. Defense Advanced Research Projects Agency (DARPA) put $2 billion into developing AI technologies like machine learning.

On the world market, Europe is expected to show strong growth. This is because ML technology is being used more and more in new markets with a lot of skilled workers, like the U.K. and Germany. Better customer access to services and goods that use AI is also having an effect on the regional market. In June 2018, the European Union proposed a programme called "Digital Europe" that would run from 2021 to 2027 and cost 10.4 billion USD. The goal of the programme is to improve AI technology and spread its uses across society and the economy. These kinds of active steps are likely to open up new market opportunities and help the European market grow.

Over the next few years, Asia-Pacific economies are expected to grow faster. Developing economies in the region, like China, India, and the Philippines, have a strong startup ecosystem that is helped by a growing number of skilled workers. This helps the markets in the region grow as a whole. Also, the Japanese government is doing a lot to promote artificial intelligence across the country. This is happening at the same time that machine learning services are becoming more popular, which is a big part of what drives the market in Japan. These are some of the things that make the Asia-Pacific market grow.

The oil-rich Gulf States in the Middle East and Africa are working hard to diversify their economies with the help of artificial intelligence. Most Gulf countries have realised how important advanced technology is and are always working to create new technologies. UAE is the most innovative and tech-savvy country in the Arab world. Also, smart city projects and self-driving cars are driving the need for AI skills in the region. Latin American countries like Brazil, Mexico, and Uruguay are making new policies and strategies for artificial intelligence (AI) to help more countries in the region use advanced technologies. In the future, the area is likely to offer new, profitable market opportunities.

Scope Analysis

Recent Development:

• April 2021: Microsoft Corporation launched open databases for transportation, health and genomics, population and safety, labor and economics, and more to improve the accuracy of ML models using publicly available datasets. This enables companies to leverage Azure Open Datasets along with Azure's ML and data analytics solutions to deliver hyperscale insights to increase MLaaS sales.
• February 2020: Oracle Corporation, a leading technology company, launched the Oracle Cloud Data Science Platform. The newly launched platform will help enterprises collaboratively build, train, manage and deploy ML models to improve the performance of data science programs.

Market Segmentation

By Component:

• Solution
• Services
• Others

By Enterprise Size:

• SMEs
• Large Enterprises
• Others

By Deployment:

• Cloud
• On-premise
• Others

By End-user:

• Healthcare
• Retail
• IT and Telecommunication
• Banking, Financial Services and Insurance (BFSI)
• Automotive & Transportation
• Advertising & Media
• Manufacturing
• Others

By Companies:

• IBM Corporation (New York, U.S.)
• SAP SE (Walldorf, Germany)
• Oracle Corporation (Texas, U.S.)
• Hewlett Packard Enterprise Company (Texas, U.S.)
• Microsoft Corporation (Washington, U.S.)
• Amazon, Inc. (Washington, U.S.)
• Intel Corporation (California, U.S.)
• Fair Isaac Corporation (California, U.S.)
• SAS Institute Inc. (North Carolina, U.S.)
• BigML, Inc. (Oregon, U.S.)
• Others