Machine Learning Market Size (2024 – 2030)

The global machine-learning market was valued at USD 26.03 billion and is projected to reach a market size of USD 226.32 billion by the end of 2030. Over the forecast period of 2024–2030, the market is projected to grow at a CAGR of 36.2%.

Artificial intelligence (AI) has a subset called machine learning (ML). This approach to data analysis entails employing algorithms that have been trained on data sets to create analytical models. With little assistance from humans, it gives machines the capacity to autonomously learn from data and prior experiences to recognize patterns and generate predictions. Previously, this technology was mostly used for restricted purposes at research institutes and multinational corporations located in industrialized nations. Presently, this sector has grown enormously, mostly due to accessibility and technical improvements. This market will be quite promising in the future, especially if R&D efforts and partnerships are heavily prioritized. There is expected to be a significant expansion throughout the projection period.

Key Market Insights:

The market for machine learning will expand by almost 120% globally in 2023. Machine learning is used by 57% of firms to enhance the customer experience. AI and machine learning are used in sales and marketing by 49% of businesses. Due to its machine learning system, Netflix was able to save $1 billion by combining content suggestions with personalization. Dirty data was identified by 60% of data scientists as their largest machine-learning process obstacle. To tackle this, organizations are implementing vigorous data collection practices by using automated data validation tools to ensure high-quality outcomes. Additionally, monitoring systems are being used to continuously assess the data.

Machine Learning Market Drivers:

The growing volume of data has been facilitating the development.

The amount of data produced by organizations, people, and linked devices has increased drastically as a result of the widespread use of digital technology, Internet of Things devices, and online platforms. With machine learning approaches, organizations may extract important insights and derive actionable knowledge from massive and complex datasets, effectively handling the rising volume, velocity, and diversity of data. In today's data-driven economy, machine learning models are invaluable for activities like predictive analytics, personalized recommendations, and real-time decision-making because of their capacity to analyze both structured and unstructured data, including text, photos, videos, and sensor data.

The need for machine learning solutions stems from the value that can be extracted from the data.

Technological advancements have been accelerating the growth rate.

Research, algorithm development, and processing capacity are enabling significant developments in the field of machine learning. Advancements like deep learning, reinforcement learning, and transfer learning are making predictions, classifications, and decision-making more complex and precise. These developments have increased machine learning's usefulness across a variety of industries, such as manufacturing, banking, healthcare, retail, and autonomous systems, opening up new possibilities for value generation and innovation. The need for machine learning solutions that use AI developments to deliver business outcomes is seeing an increase because of these advantages.

The increasing demand for automation has been fueling progress.

Businesses in a variety of sectors are using machine learning solutions more to automate tedious jobs, optimize workflows, and boost productivity. Large-scale data analysis using machine learning algorithms can reveal patterns, trends, and anomalies that people would miss, which helps optimize resource allocation and process optimization. Machine learning technologies are becoming more widely used as a consequence of the cost savings, increased productivity, and enhanced decision-making that come from automating processes like data input, customer support, inventory management, and predictive maintenance.

Machine Learning Market Restraints and Challenges:

Data security, a shortage of skilled talent, access to high-quality data, and integration complexities are the main issues that the market is currently facing.

A lot of sensitive information is often stored by the firms. There have been quite a few cases of leakage and misuse. This raises questions about the privacy of data. Losses have been incurred by the company and the individuals. Organizations need to ensure proper data handling and management to avoid such problems. Secondly, many companies fail to hire the right talent. A lot of people tend to pursue engineering and other courses in computer science fields without any interest. This can be due to parental or societal pressure. This leads to engineers and data scientists who might not necessarily have the required talent to implement specific solutions. Building and maintaining this technology requires expertise in fields like mathematics, programming, statistics, and coding. This lack of personnel can create complexities for the market. Thirdly, access to superior data can be challenging for smaller firms and startups. This leads to the generation of faulty and incomplete data that can yield absurd results. These organizations need to do a lot of work and have a lot of contacts to establish good data. Furthermore, integrating the solutions into older IT systems and complex interfaces is a significant hurdle. This can be time-consuming, requiring extra budgets, a legal framework, and other essential resources. To ensure a smooth integration, it is vital to mandate certain standard rules and protocols.

Machine Learning Market Opportunities:

This market has an ample number of opportunities in practically every industry. For instance, its use in fields like network security, fraud detection, and threat intelligence is highly beneficial. By using ML, abnormalities in the pattern and other behavioral signals can be detected. This can be used in business intelligence software that is used in industries like finance, sales, marketing, and operations. Business data is analyzed, and future trends are predicted using machine learning. It makes choices based on data and offers suggestions for enhancing corporate performance. It is also used in NLP (natural language processing) to comprehend human language. This is used in chatbots and virtual assistants. Furthermore, robotics is used to make robots capable of decision-making and understanding perceptions. In the industrial sector, it can be used to reduce downtime, optimize operations, and predict the possible failures of equipment. Apart from this, research and developmental activities are being emphasized to broaden the human understanding of this technology. To encourage this, many research institutes and academic institutions are providing scholarships and grants to students to enable breakthroughs.

MACHINE LEARNING MARKET REPORT COVERAGE:

Machine Learning Market Segmentation: By Deployment Mode

• Cloud-based

• On-premise

Based on deployment mode, the cloud-based segment is the largest and fastest-growing, with a share of around 55%. These solutions offer automation, which means that IT teams will look after the deployment, monitoring, and securing of applications. This renowned expertise encourages a lot of companies to go for this mode. Employers can benefit from employee data by centralizing various data sources using cloud-based solutions. These solutions also offer advanced security measures and managed access features. All the upgrades are automatic. This flexibility, scalability, and versatility help in gathering a broader client base.

Machine Learning Market Segmentation: By Component

• Hardware

• Software

• Services

The service segment is the largest, with a share of around 51%. By utilizing outside infrastructure and resources for ML model building and deployment, organizations may save time and money by utilizing machine learning services. Organizations can delegate these responsibilities to ML service providers for developing and maintaining internal ML capabilities, freeing them up to concentrate on their primary business operations. The hardware segment is the fastest-growing. To train huge models on massive quantities of data, machine learning techniques sometimes demand a substantial amount of processing power. They offer the computational power needed to speed up model training and inference processes.

Machine Learning Market Segmentation: By Organization Size

• Large-Scale Organization

• Small and medium-scale organizations

Large-scale organizations are the largest growing segment, with a share of around 65%. They have the necessary investments and backup support. These businesses analyze larger, more complex data sets and produce faster, more accurate answers by utilizing machine learning. These organizations are also investing in research and developmental activities to improve the existing algorithms. Small and medium-scale organizations are the fastest-growing. These firms are collaborating with larger companies by providing unique solutions. Governmental involvement in the form of various schemes, funds, and grants is helping these companies. Besides, a lot of companies have been offering pre-built models that offer various services, like training and management. This reduces the cost, which makes it an attractive option.

Machine Learning Market Segmentation: By End-User

• BFSI

• Healthcare & Life Sciences

• Retail

• IT & Telecommunications

• Government and Defense

• Manufacturing

• Energy and Utilities

• Agriculture

• Automotive

• Others

BFSI is the largest growing end-user. This is because of the extensive application of ML in this industry. Complex transaction data patterns that might point to money laundering schemes are recognized by ML algorithms. This requires recognizing strategies such as layering, in which several transactions are carried out to mask the source of money, and smurfing, in which substantial transactions are divided up into smaller, less suspicious sums. Through the analysis of client data, transaction history, and other crucial information, machine learning generates dynamic consumer risk profiles. More precise identification of high-risk clients allows banks to implement stronger procedures and more effectively use their resources. Healthcare & life sciences are the fastest-growing end-users. ML is increasingly seen as a vital tool in this industry because it can analyze enormous volumes of data considerably more accurately than humans can, finding patterns and making astonishingly accurate predictions. Hard-to-diagnose illnesses and conditions, such as cancer and hereditary disorders, can be identified and diagnosed with the use of machine learning. By virtually accurately diagnosing, suggesting the right medications, anticipating readmissions, and detecting high-risk patients, machine learning (ML) can enhance patient safety.

Machine Learning Market Segmentation: Regional Analysis

• North America

• Asia-Pacific

• Europe

• South America

• Middle East and Africa

Based on region, North America is the largest market. The United States and Canada are the two leading countries. The primary reason for the growth in this region is economic stability. This helps in the easier allocation of infrastructure, funds, and other resources. A lot of major companies are present in this area. They have a global presence, contributing to a tremendous amount of revenue. Industries like healthcare, finance, and IT are diverse and large in this area, contributing to its success. Important key players include Google, Microsoft, Amazon, and IBM. Asia-Pacific is the fastest-growing region. China, India, Japan, and South Korea are at the forefront. The economy in Asian countries has seen a lot of progress. Many prestigious research and academic institutions are advancing in this technology. Training programs are being implemented to gather specialized expertise. Many companies in North America have the largest hubs in countries like India and Japan. R&D activities are given high prominence.

COVID-19 Impact Analysis on the Global Machine Learning Market:

The outbreak of the virus had a positive impact on the market. Lockdowns, social isolation, and movement restrictions were imposed. This led to a shift towards remote work. The healthcare sector was a major driver. ML technologies were used in tracking the infected patients and predicting the possible outcomes. Drug development, patient monitoring, and medical imaging were some of the crucial applications. ML was used here to create accurate results by analyzing the data and providing better insights. Few research papers published indicate the role of ML in predicting mortality using datasets. As per a report by the G2 Learning Hub, machine learning helps reach up to 95% accuracy for COVID-19-related physiological decline. Additionally, areas of predictive analysis, automation, and customer experience faced an upsurge in demand. Digitalization was the new norm, causing people to gravitate towards online shopping. Products could conveniently be delivered to the doorstep. ML was used to create personalized recommendations, detect fraud, and optimize the supply chain. Besides, this was used in almost every industry for tracking logistics, transportation, and other supply chains. Remote collaboration tools gained prominence. Virtual assistants and chatbots have become popular. This technology was used to enhance and enable smooth communication. Post-pandemic, the market has continued to grow because industries have realized the importance of this technology.

Latest Trends/ Developments:

Federated learning has become a prominent method for maintaining privacy while training machine learning models using decentralized data sources. By resolving privacy issues and legal restrictions, this distributed learning system allows organizations to cooperatively train models across devices, IoT sensors, and cloud servers without sharing raw data.

Key Players:

1. Google

2. Microsoft Corporation

3. Amazon Web Services (AWS)

4. IBM Corporation

5. NVIDIA Corporation

6. Intel Corporation

7. SAS Institute, Inc.

8. SAP SE

9. Oracle Corporation

10. Salesforce.com, Inc.

• In March 2024, the Department of Science and Technology (DST), a department of the Indian Ministry of Science and Technology, and T-Hub, the country's top startup incubator, joined forces to launch the first-ever Machine Learning and Artificial Intelligence Technology Hub (MATH). MATH has set lofty targets to raise the AI industry in India to new heights with its strategic vision to support AI research, provide employment opportunities, and build a favorable ecosystem for AI businesses. The Center of Excellence wants to establish itself as the nation's premier hub for AI-driven projects by creating over 500 AI-related jobs by 2025 and supporting over 150 startups each year.

• In August 2023, the third iteration of Amazon India's Machine Learning (ML) Summer School was introduced. This intensive course seeks to provide students with the chance to study important ML technologies from Amazon scientists, preparing them for a future in the field. The free educational course will cover eight courses, giving students the chance to become proficient in important machine-learning areas.

• In March 2023, the lake house firm Databricks announced the release of Databricks Model Serving, which is a native feature of the Databricks Lakehouse Platform that offers easier production machine learning (ML). The intricacy of creating and managing complex infrastructure for intelligent applications is eliminated with model serving.