U.S. Machine Learning (ML) Market Size, Share & COVID-19 Impact Analysis, By Enterprise Type (Small and Mid-Sized Enterprises (SMEs) and Large Enterprises), By Deployment (Cloud and On-premise), By End-use Industry (Healthcare, Retail, IT and Telecommunication, BFSI, Automotive and Transportation, Advertising and Media, Manufacturing, and Others), and Country Forecast, 2023-2030

The U.S. Machine Learning (ML) market size was valued at USD 4.74 billion in 2022. The market is expected to grow from USD 6.49 billion in 2023 to USD 59.30 billion by 2030, exhibiting a CAGR of 37.2% during the forecast period.

A report from the Center for Security and Emerging Technology (CSET) states that 167 investors from the U.S. participated in 401 transactions, providing Chinese AI businesses with 37% (or USD 40.2 billion) in funding from 2015 to 2021. Qualcomm Ventures and Intel Capital made 13 and 11 investments, respectively, as Washington's sanctions got worse.

In the U.S., the field of Artificial Intelligence (AI) is expanding at an exponential rate. Deep learning is a subset of ML in AI technology. The machine learning process is carried out using the hierarchical level of Artificial Neural Networks (ANN). Due to advancements in deep learning algorithms, it is anticipated that the market in the U.S. will expand. In addition, it is being predicted that the ML market will expand across all end-use industries as a result of numerous businesses strengthening their deep learning capabilities to encourage innovation.

COVID-19 IMPACT

COVID-19 Pandemic to Create Unprecedented Market Opportunities for Leading Companies

Healthcare organizations were compelled to swiftly reevaluate their technologies and move forward with plans for digital transformation as the coronavirus began to spread across the U.S. In addition, during the COVID-19 pandemic, companies that invested in and implemented disruptive technologies, such as edge computing, Artificial Intelligence (AI), connected devices, and ML, began to realize that these technologies were assisting them in recovering from the initial effects of the outbreak and driving future business automation.

These factors contributed significantly toward the U.S. Machine Learning market growth.

LATEST TRENDS

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Rapid Adoption of Big Data in Various End-use Industries to Surge Demand for ML

Data volumes have grown to the point where more data was created in the last few years than ever before in human history. The BFSI, healthcare, IT & telecom, and automotive industries are among the most affected by Big Data. The adaptation to Big Data services has grown at a commendable rate in the U.S. The ever-increasing demand for data from both structured and unstructured sources is the primary driver of the nation's robust Big Data market, which is made possible by the growing public and private utilities and services.

To begin research on ML, Big Data analytics, predictive technologies, and AI—all of which are necessary to give the U.S. agriculture a strong edge in food and agricultural production—the USDA's National Institute of Food and Agriculture awarded 11 grants in the AFRI Food and Agriculture Non-formal Education (FANE) program supports content development and activities for non-formal education to foster development of technology-savvy youth, totaling more than USD 7 million in December 2021. The Food and Agriculture Cyberinformatics and Tools (FACT) program under the USDA-NIFA Agriculture and Food Research Initiative (AFRI) provides funding for these grants.

DRIVING FACTORS

Increase in Unstructured Data will Boost Development of Machine Learning Solutions

The use of unstructured data in analytical, regulatory, and decision-making processes is on the rise. In marketing campaigns and business intelligence, human decision-making is frequently influenced by unstructured data analysis. This form of data can be seen in the analytics produced by machine learning algorithmic processes, data from Internet of Things (IoT) devices, such as tickers, sensors, and other functional ones, as well as rich media, including weather, surveillance, and geospatial data.

According to Forbes, unstructured data is predicted to increase by 175 billion zettabytes by 2025, paving the way for strong demand for AI and ML solutions in the coming years.

RESTRAINING FACTORS

Lack of Coding Skills Likely to Limit Market Growth

Experts in the coding field are hard to find in the U.S. due to lack of IT talent. Even though modern technology is present everywhere, it is nearly impossible to implement digital transformation today without the help of new tech specialists. This stark reality will only get worse as the gap between the demand and supply of highly skilled IT experts grows. A global McKinsey survey found that by 2030, there will be a shortage of approximately 82.5 million coders. As of now, 87% of businesses are having trouble finding the coding talent they require.

However, some industries, particularly those involving data analytics, manage to simultaneously be in high demand and grow rapidly while also being in desperate need of staff. Additionally, the U.S. Bureau of Labor Statistics estimates that there will be a shortage of engineers exceeding 1.2 million by 2026. It is anticipated that this will temporarily impede the expansion of the U.S. Machine Learning market share.

SEGMENTATION

By Enterprise Type Analysis

Proliferation of AI and ML Technologies among SMEs to Drive Market Growth

The market is split into Small & Mid-sized Enterprises (SMEs) and large enterprises by enterprise type. In the coming years, SMEs in the U.S. are likely to use machine learning solutions more. In today's economy, artificial intelligence systems have the potential to reduce costs, especially for SMEs.

While some large U.S. companies are leading the global adoption of AI and machine learning, policymakers now face the challenge of helping these technologies spread throughout the economy. Machine learning tools need to be made available to 89% of U.S. businesses with fewer than 20 employees and 98% of those with fewer than 100 employees to help the country reach its full productivity potential. SMEs are still recovering from the effects of the ongoing COVID-19 crisis, so an AI-enabled productivity boost would be quite handy.

By Deployment Analysis

Cloud-based Machine Learning Platforms to Augment Market Progress

Based on deployment, the market has been divided into on-premise and cloud. Some of the major players in the market provide cloud-based or on-premise machine learning solutions. The cloud segment is expected to witness a remarkable growth. Flexibility, automatic software upgrades, disaster management via cloud-based backup systems, and enhanced efficiency are the key advantages that have prompted the implementation of cloud-based delivery models for deep learning software solutions and services.

Google Cloud, for instance, is provided by Alphabet, Inc. A wide range of AI and ML tools are available on Google Cloud. BigML provides on-premise deployments for businesses that require configuration, upkeep, and management of their own installations.

By End-use Industry Analysis

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BFSI and Automotive and Transportation Segments to Witness a Noteworthy Growth Rate due to Adoption of ML Solutions

Banks and other monetary organizations influence advancements in machine learning technologies to detect misrepresentation and point out significant insights in data. In the U.S., e-commerce has proven to be a major driver of retail trade business practices. Retailers use machine intelligence to collect data, analyze it, and use it to provide customers with personalized shopping experiences. The financial and retail sectors' demand for this technology is fueled by these factors.

The automotive and transportation industry is expected to grow considerably in the coming years. The demand for cutting-edge solutions is driven by research & development on self-driving cars and autonomous transportation.

KEY INDUSTRY PLAYERS

Key Players Are Focusing on Expanding their Geographical Presence to Compete in the Market

The competitive landscape of the U.S. ML market is consolidated with a few key players operating globally and regionally. To strengthen their positions in the U.S. market and broaden their respective portfolios, major players are forming strategic alliances.

List of the Key Companies Profiled:

• IBM Corporation (U.S.)


• Oracle Corporation (U.S.)


• Hewlett Packard Enterprise Company (U.S.)


• Microsoft Corporation (U.S.)


• Amazon, Inc. (U.S.)


• Fair Isaac Corporation (U.S.)


• RapidMiner Inc. (U.S.)


• H2O.ai (U.S.)


• Teradata (U.S.)


• TIBCO Software Inc. (U.S.)

KEY INDUSTRY DEVELOPMENTS

• June 2022– The integration of the Teradata Vantage multi-cloud data and analytics platform with Amazon SageMaker and its general availability were made public by Teradata. This action is in support of Teradata's Analytics 123 analytics framework, which offers organizations struggling with production-level AI/ML initiatives a step-by-step approach to scaling their analytical model deployment.


• October 2022 – IBM's System-on-Chip (SoC) artificial intelligence system was recently made available to the public. The device is designed to train and run deep learning models more efficiently and significantly faster than CPUs. The system has 32 processing cores and 23 billion transistors on the SoC due to a 5 nm process node.

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The research report provides a comprehensive analysis of the market. It focuses on key aspects such as prominent companies and leading applications of the product. Besides this, the report highlights key industry developments and offers insights into the market trends. In addition to the above-mentioned factors, the report includes other factors that have contributed to the market growth in recent years.

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