Artificial Intelligence
Machine Learning as a Service Market Size Growing at 37.9% CAGR Set to Reach USD 173.5 Billion By 2032
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TOKYO, April 24, 2023 (GLOBE NEWSWIRE) — The Global Machine Learning as a Service Market Size accounted for USD 7.1 Billion in 2022 and is projected to achieve a market size of USD 173.5 Billion by 2032 growing at a CAGR of 37.9% from 2023 to 2032.
Machine Learning as a Service Market Research Report Highlights and Statistics:
- The Global Machine Learning as a Service Market Size in 2022 stood at USD 7.1 Billion and is set to reach USD 173.5 Billion by 2032, growing at a CAGR of 37.9%
- MLaaS allows users to access and utilize pre-built algorithms, models, and tools, making it easier and faster to develop and deploy machine learning applications.
- Adoption of cloud-based technologies, the need for managing the huge amount of data generated, and the rise in demand for predictive analytics and natural language processing are driving the growth of the Machine Learning as a Service market.
- North America is expected to hold the largest market share in the Machine Learning as a Service market due to the presence of large technology companies and the increasing demand for advanced technologies in the region.
- Some of the key players in the Machine Learning as a Service market include Amazon Web Services, IBM Corporation, Google LLC, Microsoft Corporation, and Oracle Corporation.
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Machine Learning as Service Market Report Coverage:
| Market | Machine Learning as a Service Market | |
| Machine Learning as a Service Market Size 2022 | USD 7.1 Billion | |
| Machine Learning as a Service Market Forecast 2032 | USD 173.5 Billion | |
| Machine Learning as a Service Market CAGR During 2023 – 2032 | 37.9% | |
| Machine Learning as a Service Market Analysis Period | 2020 – 2032 | |
| Machine Learning as a Service Market Base Year | 2022 | |
| Machine Learning as a Service Market Forecast Data | 2023 – 2032 | |
| Segments Covered | By Component, By Application, By Organization Size, By End-Use Industry, And By Geography | |
| Metabolomics Market Regional Scope | North America, Europe, Asia Pacific, Latin America, and Middle East & Africa | |
| Key Companies Profiled | Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), IBM Watson, Oracle Cloud, Alibaba Cloud, SAS, PREDICTRON labs LTD, FICO, and HEWLETT Packard Enterprise | |
| Report Coverage | Market Trends, Drivers, Restraints, Competitive Analysis, Player Profiling, Regulation Analysis | |
Machine Learning as a Service Market Overview:
The increasing adoption of cloud-based technologies and the need for managing the enormous amount of data generated has led to the rise in demand for MLaaS solutions. MLaaS provides pre-built algorithms, models, and tools, making it easier and faster to develop and deploy machine learning applications. This service is being used in various industries such as healthcare, retail, BFSI, manufacturing, and others.
The healthcare industry is using MLaaS for patient monitoring and disease prediction. In retail, MLaaS is being used for personalized recommendations and fraud detection. MLaaS is also being utilized for financial fraud detection, sentiment analysis, recommendation systems, predictive maintenance, and much more.
The Natural Language Processing (NLP) segment is expected to grow rapidly during the forecast period. NLP is being used by organizations to analyze customer feedback, improve customer experience, and automate customer service. MLaaS vendors such as Amazon Web Services, IBM Corporation, Google LLC, Microsoft Corporation, and Oracle Corporation offer various pricing models and features, making the Machine Learning as a Service market competitive.
Trends in the Machine Learning as a Service Market:
- Automated Machine Learning (AutoML): The development of AutoML algorithms is reducing the need for expert data scientists to develop machine learning models, allowing non-experts to develop and deploy models with less effort and cost.
- Edge Computing: Machine learning models are being deployed on edge devices such as smartphones, IoT sensors, and other devices to reduce latency and improve privacy.
- Explainable AI: Machine learning models are becoming more transparent, and algorithms are being developed that can explain how the model arrived at its decisions.
- Federated Learning: Machine learning models are being developed to train on data that is distributed across multiple devices, allowing for privacy protection and faster training.
- Synthetic Data: Synthetic data is being used to augment training data, reducing the need for large amounts of real data and improving model accuracy.
- Time Series Analysis: Machine learning models are being developed to analyze and predict time series data, which is important in industries such as finance and transportation.
- Personalization: Machine learning models are being developed to provide personalized recommendations, content, and experiences to users.
- Generative Models: Generative models are being developed to create new data based on existing data, which can be used for various applications such as image and text generation.
Machine Learning as a Service Market Dynamics
- Increased demand for advanced analytics: Businesses are looking for ways to extract insights from their data to improve decision-making, and MLaaS provides a fast and efficient way to do so.
- Quantum Machine Learning: Machine learning algorithms are being developed that can run on quantum computers, which offer significant speed improvements over classical computers.
- Interpretable Machine Learning: Machine learning models are being developed to provide interpretable results, allowing users to understand how the model arrived at its decisions.
- Reinforcement Learning: Reinforcement learning algorithms are being developed to teach machines how to make decisions based on feedback from their environment.
- Multi-Task Learning: Machine learning models are being developed to perform multiple tasks simultaneously, reducing the need for multiple models.
- Transfer Learning: Machine learning models are being developed that can transfer knowledge learned from one task to another, reducing the need for large amounts of training data.
- Increasing adoption of IoT devices: The growing number of IoT devices is generating massive amounts of data that can be analyzed with machine learning algorithms, driving demand for MLaaS services.
- Speech Recognition: Machine learning models are being developed that can accurately recognize speech, which is important for applications such as virtual assistants and speech-to-text.
- Low barriers to entry: MLaaS provides a low barrier to entry for businesses that want to incorporate machine learning into their operations but lack the resources to do so in-house.
- Explainable Deep Learning: Deep learning models are being developed that can provide interpretable results, allowing users to understand how the model arrived at its decisions, which is important for applications such as healthcare and finance.
Growth Hampering Factors in the Market for Machine Learning as a Service:
- Concerns about data security and privacy: Many businesses are hesitant to use MLaaS due to concerns about data security and privacy, which may hamper the growth of the market.
- Complexity of machine learning models: Developing and deploying machine learning models can be complex, which may limit the adoption of MLaaS by businesses.
- Limited interpretability of machine learning models: Many machine learning models are not easily interpretable, which may make it difficult for businesses to understand the underlying logic and decision-making process of these models.
- Limited availability of training data: Machine learning models require large amounts of high-quality training data, and if this data is not available, it may limit the ability of businesses to develop accurate models.
- Cost: MLaaS can be expensive, especially for small and medium-sized businesses, which may limit adoption.
- Lack of trust in machine learning models: If businesses do not trust the accuracy and reliability of machine learning models, they may be hesitant to adopt MLaaS.
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Market Segmentation:
By Type of component
- Services
- Solution
By Application
- Security and surveillance
- Augmented and Virtual reality
- Marketing and Advertising
- Fraud Detection and Risk Management
- Predictive analytics
- Computer vision
- Natural Language processing
- Other
By Size of Organization
- SMEs
- Large Enterprises
End User
- Retail
- BFSI
- Healthcare
- Public sector
- Manufacturing
- IT and Telecom
- Energy and Utilities
- Aerospace and Defense
Machine Learning as a Service Market Overview by Region:
North America’s Machine Learning as a Service market share is the highest globally, due to the high adoption of cloud computing and the presence of several major players in the region. The United States is the largest market for MLaaS in North America, driven by the increasing demand for predictive analytics, the growing use of deep learning, and the rising adoption of artificial intelligence (AI) across various industries. For instance, companies in the healthcare sector are using MLaaS for predicting patient outcomes, and retailers are using it to analyze customer behavior and preferences to deliver personalized experiences.
The Asia-Pacific region’s Machine Learning as a Service Market share is also huge and is growing at the fastest rate, due to the increasing adoption of cloud computing, the growth of IoT devices, and the rise of e-commerce in the region. China is the largest market for MLaaS in the Asia Pacific region, with several major companies investing in AI and machine learning technologies. For example, Alibaba, the largest e-commerce company in China, is using MLaaS for predictive analytics and recommendation engines. Japan is another significant market for MLaaS in the region, with companies using it for predictive maintenance and fraud detection.
Europe is another key market for Machine Learning as a Service, with countries such as the United Kingdom, Germany, and France driving growth in the region. The adoption of MLaaS in Europe is being driven by the growth of e-commerce and the increasing demand for personalized experiences. For example, companies in the retail sector are using MLaaS to analyze customer data and make personalized product recommendations. The healthcare sector is also a significant user of MLaaS in Europe, with providers using it for predictive analytics and diagnosis.
The MEA and South American regions have a growing Machine Learning as a Service market share, however it is expected to grow at a steady pace.
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Machine Learning as a Service Market Key Players:
Some of the major players in the Machine Learning as a Service market include Amazon Web Services, Google LLC, IBM Corporation, Microsoft Corporation, SAP SE, Oracle Corporation, Hewlett Packard Enterprise Development LP, Fair Isaac Corporation (FICO), Fractal Analytics Inc., H2O.ai, DataRobot, Alteryx Inc., Big Panda Inc., RapidMiner Inc., SAS Institute Inc., Angoss Software Corporation, Domino Data Lab Inc., TIBCO Software Inc., Cloudera Inc., and Databricks Inc. These companies offer a wide range of MLaaS solutions, including predictive analytics, machine learning algorithms, natural language processing, deep learning, and computer vision.
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LONDON, May 9, 2024 /PRNewswire/ — CyXcel, a leading cybersecurity business with operations in the UK and North America, announces the appointment of Simon Church as Chief Strategy Officer. A 35-year veteran of the technology industry, Church brings a wealth of cyber expertise and commercial development to the role. Church’s appointment solidifies CyXcel as a pioneering force in combining security, regulatory, and legal expertise for established companies and those expanding into new markets, ensuring protection and recovery of their critical business assets.
As Chief Strategy Officer, Church will spearhead CyXcel’s strategic initiatives to drive innovation, foster partnerships, and accelerate growth opportunities. His appointment underscores CyXcel’s commitment to fortifying its strategic offering and enhancing its position as a leader at the nexus of risk analysis, response management and incident resolution. His vast experience in go-to-market strategies and M&A will be instrumental in driving CyXcel’s growth and expansion initiatives.
Church has held executive leadership positions at market-leading cybersecurity and technology companies such as Maxive Cyber Security (acquired by Thales), Optiv, Vodafone, NTT Security, Verisign, and NetIQ. His experience includes strategic roles in identity management, networking, and managed services and he brings a strong track record of delivering commercial growth, including leading on commercial acquisitions.
In addition to his role at CyXcel, Church serves as Chair of Xalient, a UK-headquartered converged cyber, identity, and networking managed services company. He is also a Board member and strategic advisor to Redshift, as well as a Board member at beqom. Furthermore, Church serves on the Advisory Board of Glasswall, a UK-based security technology company.
Ed Lewis, CyXcel Co-Founder and Managing Partner commented:
“Simon’s unparalleled expertise and proven track record will be invaluable as we continue to innovate and deliver pioneering solutions to our clients worldwide. With Simon’s strategic leadership, we are poised to achieve new heights in our mission to safeguard enterprises and navigate the complex geopolitical, regulatory and legal implications of ever-evolving cyber threats.”
Simon Church said:
“I’m thrilled to be joining such an experienced and ambitious team and the CyXcel offer is unlike any I’ve ever seen. CyXcel has already established itself as a trusted partner in empowering organisational awareness and responsiveness to the financial, existential, and strategic complexities of global cyber threats. I look forward to delivering rapid growth around the world by developing and executing initiatives that provide unparalleled protection and value to our clients.”
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Artificial Intelligence
IBM Introduces New Microsoft Copilot Capabilities to Fuel AI-Powered Business Transformation
ARMONK, N.Y., May 9, 2024 /PRNewswire/ — IBM (NYSE: IBM) today announced the availability of IBM Copilot Runway, a new offering from IBM Consulting designed to help enterprises create, customize, deploy and manage copilots including Copilot for Microsoft 365. With the new offering, clients will be able to seamlessly integrate copilots’ generative AI into their organizations in order to enhance productivity and drive business success. IBM Consulting has also formed a dedicated practice of consultants with Microsoft copilot skills, credentials and expertise to guide clients on their AI transformation journey.
As part of the new offering, IBM will work with clients to build custom copilots that can be tailored to fit the needs of specific business scenarios and efficiently deployed to help reduce the time and effort often required for implementations. IBM will initially focus on helping clients across priority use cases, including customer and field service, employee experience, and procurement and finance – as well as specific industries, like financial services, retail and CPG, government, and supply chain. These use cases will include:
The Procurement and Finance Contract Copilot which assists specialists in extracting valuable insights from contracts.The Customer Service and Field Service Copilot which gives agents and technicians access to self-service options and a time-saving generative AI search.The Employee Experience Copilot which is designed to enhance employee engagement. IBM and Microsoft already serve clients across a variety of industries with a range of AI solutions and services. In fact, IBM Consulting worked with Virgin Money to develop and launch Redi, a conversational virtual assistant that helps credit card customers in the Virgin Money credit card app. Redi, powered by Virgin Money’s suite of Microsoft Copilots, is a testament to the power of partnership.
“Our customers tell us how much they enjoy interacting with Redi in the Credit Card app,” said Adam Paice, Head of Digital Proposition, Virgin Money. “Our partnership with IBM has helped us to get the most out of Microsoft Copilot to find a balance between innovation and control.”
IBM Consulting is also scaling its Microsoft copilot capabilities and capacity across its network of Global Innovation Centers on each continent. For example, these capabilities are being used to co-create solutions with clients at IBM Consulting’s new IBM-Microsoft Experience Zone in Bangalore, India – a first of its kind in our collaboration.
At the Experience Zone, clients from around the world and across industries are working together with IBM Consulting in various technology stations to co-ideate and co-create generative AI-powered solutions – leveraging Microsoft technologies, including Copilot. Later this year, IBM Consulting plans to open additional IBM-Microsoft Experience Zones in Romania, U.K. and U.S., where clients in these regions can explore the power of Copilot. In addition IBM and Microsoft hosted a second client-focused hackathon with 800 participants, including clients and experts to build solutions leveraging Microsoft copilot – with winners to be announced this quarter.
“As IBM and Microsoft strengthen our partnership, we’re poised to empower more clients with Microsoft Copilot, supercharging productivity and boosting creativity through the power of generative AI,” said John Granger, Senior Vice President, IBM Consulting. “Our dedicated IBM Consulting Microsoft practice, along with Copilot-focused Experience Zones around the world, help us meet clients where they are and bring them the right generative-AI-enabled solutions for their businesses.”
“Clients need the right partners and technology to scale AI responsibly across the enterprise,” said Dinis Couto, General Manager of Global Partner Solutions for Microsoft. “With IBM’s dedicated group of Microsoft Copilot experts, we’re confident we can help more clients unlock the full potential of generative AI for their businesses.”
IBM purchased Copilot for Microsoft 365 for its practitioners. To continue building on the partnership, IBM has also invested in growing its team of experts and capabilities through acquisitions, like Neudesic and Bluetab.
IBM Consulting practitioners work with a range of leading AI software technologies and multiple models from both IBM and its strategic partners like Microsoft. IBM data and AI consultants typically employ multiple models, each applied to a specific use case. Different models can be optimized for specific tasks, enhancing performance and efficiency.
Microsoft is a trademark of Microsoft Corporation in the United States, other countries, or both.
About IBMIBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. More than 4,000 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM’s long-standing commitment to trust, transparency, responsibility, inclusivity and service.
Visit www.ibm.com for more information.
Media Contact:IBMCarolyn [email protected]
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Artificial Intelligence
Inceptio-Powered Autonomous Trucks Surpass 100 Million Kilometers in Safe Commercial Operations
Major Milestone Advances Autonomous Heavy-Duty Truck Commercialization
SHANGHAI, May 9, 2024 /PRNewswire/ — Inceptio Technology (“Inceptio,” or the “Company”), an industry leading developer of autonomous driving technologies for heavy-duty trucks, today announced that heavy-duty trucks powered by the Inceptio Autonomous Driving System and its Truck Navigate-on-Autopilot (T-NOA) capabilities have surpassed the significant milestone of 100 million kilometers in safe commercial operations, reinforcing Inceptio’s global leadership in the commercialization of autonomous trucks.
This achievement underscores how L3 and L2+ autonomous heavy-duty trucks have been successfully deployed across the line-haul logistics sector, including express delivery, less-than-truckload (LTL) transportation, as well as contract logistics. This also reflects the significant value that autonomous trucks offer logistics operators.
Accelerating the Adoption of Autonomous Trucks Across the Line-Haul Logistics Sector
Inceptio-powered trucks surpassed 50 million kilometers of safe commercial operations in August 2023. Building on this success, Inceptio rapidly expanded the number of compatible truck models and surpassed the 100-million-kilometer mark by the end of April 2024.
Inceptio’s Autonomous Driving System covers 83% of China’s national highways connecting 7 major economic zones. Over the course of the 100 million kilometers, a total of 1,864 drivers safely used Inceptio-powered L3 and L2+ trucks in their daily operations.
Current customers include all the top logistics companies in China such as ZTO Express (NYSE: ZTO and HKEX: 2057), YTO Express (HKEX: 6123), STO Express (SZSE: 002468), JD Logistics (HKEX: 2618), and SF Express (SHE: 002352). Inceptio has also established an extensive footprint across the contract logistics segment including cold chain, automotive, beverages, and fast-moving consumer goods among many others, serving global brands like Budweiser and Nestlé. Inceptio’s autonomous driving technology caters to a diverse user base—from big logistics companies to small fleets and individual operators.
Inceptio has partnered with several leading Chinese truck manufacturers to pre-load mass produced trucks with the Inceptio Autonomous Driving System. These partnerships have expanded the number of trucks Inceptio powers and include popular models from Dongfeng, Sinotruk, Foton and Liuqi that are available in both 4×2 and 6×4 axle configurations to meet the diverse needs of the line-haul logistics sector.
Paving the Way for Greater Commercialization with Safer, More Efficient, and Profitable Operations
Over the course of 100 million kilometers, Inceptio has demonstrated how its autonomous driving technology and its T-NOA capabilities are paving the way for greater commercial deployment across the line-haul logistics with safer, more efficient, and profitable operations.
The majority of the routes large express delivery companies in China use exceed 500 kilometers in length. Two drivers are commonly assigned to each traditional truck on these routes and take shifts driving in order to minimize fatigue and ensure safety when meeting tight shipping schedules. Inceptio’s solution makes driving much less physically and mentally exhausting as it handles more than 90% of the journey. Express delivery companies have been able to significantly reduce the number of drivers per truck and labor costs on these same routes as a result. On routes ranging from 500 to 1,200 kilometers, Inceptio has realized a direct shift from two drivers per truck to one, resulting in a significant 40% to 50% reduction in labor costs. On routes that exceed 1,200 kilometers where an autonomous truck relay model has been deployed, a traditional assignment of 6-8 drivers per three trucks has been reduced to 5. Likewise, a traditional assignment of 8-10 drivers per 4 trucks has been reduced to 6, resulting in a substantial decrease in labor costs and improved driver satisfaction.
The benefits are equally strong for contract logistic companies, both large and small. Huatai Logistics for example, a contract logistics company specializing in automotive parts transport on routes that average 1,500 kilometers, has seen its driver-to-truck ratio decrease from two to one by using Inceptio-powered trucks. Combined with a reduction of 3-5 liters in fuel consumption per 100 kilometers, total cost of ownership per kilometer decreased by 7-15%. The stellar safety record and enhanced driving comfort offered by autonomous trucks improved fleet-attendance rates significantly and increased monthly kilometers per truck by as much as 10%.
Some individual operators have also seen increases of 10-20% in monthly kilometers per truck and 2,500-5,500 RMB in monthly net income due to the fundamental improvement of safety and driving comfort offered by Inceptio-powered autonomous trucks. The fuel-saving benefits of autonomous trucks are particularly attractive for individual operators.
Leveraging Data Assets to Enhance Inceptio’s Autonomous Driving Technology
Inceptio leverages its powerful, data-driven R&D system to rapidly iterate and enhance its autonomous driving technology. This system, which incorporates accurate and efficient data capturing, automated cloud processing, advanced scenario mining, and automatic annotation, allows Inceptio to continuously refine its industry-leading T-NOA algorithm in real-time. This focus on real-world data is a key driver of Inceptio’s competitive edge in the autonomous driving technology landscape.
Julian Ma, founder and CEO of Inceptio Technology, commented, “Inceptio’s autonomous driving technology and its T-NOA capabilities are making significant progress in their commercialization, allowing us to rapidly surpass the 100-million-kilometer milestone after hitting 50 million kilometers only eight months ago. The impact our technology is having on the logistics industry is profound. The commercial deployment of Inceptio-powered autonomous trucks across the line-haul logistics sector is exciting, but what’s truly inspiring is the creativity and innovation our customers bring to the table. This user-driven approach is pushing the boundaries of how these autonomous trucks are used, opening up new ways to deploy our technology. The more data we gather, the faster we will be able to enhance our algorithms and improve our full-stack solution. We will continue working closely with our truck OEM partners to offer even greater safety, efficiency, and profitability to logistics customers.”
About Inceptio Technology
Inceptio Technology is an industry leading developer of autonomous driving technologies for heavy-duty trucks. Its flagship technology is the Inceptio Autonomous Driving System, a proprietary full-stack solution. Inceptio partnered with leading OEMs to roll out the industry’s first mass-produced L3 autonomous trucks in late 2021. These trucks are operated nationwide in China by customers across the line-haul logistics sector including express delivery, less-than-truckload (LTL) transportation, and contract logistics. Inceptio is at the cutting edge of developing fully driverless trucks. In 2022 it became the first company in China to receive a public road-testing permit for driverless autonomous heavy-duty trucks.
For more information on Inceptio Technology, visit https://en.inceptio.ai/
View original content:https://www.prnewswire.co.uk/news-releases/inceptio-powered-autonomous-trucks-surpass-100-million-kilometers-in-safe-commercial-operations-302141157.html
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