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“IBM (US), Oracle (US), Microsoft (US), ServiceNow (US), Google (US), Baidu (China), AWS (US), Alteryx (US), Salesforce (US), Altair (US), Teradata (US), H2O.ai (US), DataRobot (US), BigML (US), Databricks (US), Dataiku (France), Alibaba Cloud (China), Appier (Taiwan)”

The market for Automated Machine Learning is estimated to grow from USD 1.0 billion in 2023 to USD 6.4 billion by 2028, at a CAGR of 44.6% during the forecast period. Automated machine learning (AutoML) is a subset of artificial intelligence (AI) that enables users to create machine learning applications without requiring extensive knowledge of statistics and machine learning. It simplifies the process of building high-performance machine learning applications, which traditionally required specialized data scientists and domain experts. Due to advancements in data science and AI, AutoML has seen significant progress in recent years.

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Healthcare & Lifesciences to account for higher CAGR during the forecast period

The AutoML market for healthcare is categorized into various applications, such as anomaly detection, disease diagnosis, drug discovery, chatbot and virtual assistance and others (clinical trial analysis and electronic health record (EHR) analysis). In the healthcare and life sciences industry, AutoML can help automate various tasks such as disease diagnosis, drug discovery, and patient care. AutoML can be used to analyze large volumes of medical data, such as electronic health records, medical images, and genomic data, to identify patterns and make predictions. This can help healthcare professionals make more accurate diagnoses, identify potential treatments, and improve patient outcomes. AutoML can also be used in drug discovery to identify potential drug candidates and optimize drug development processes. By analyzing molecular structures, genetic data, and other factors, AutoML can help identify potential drug targets and optimize drug efficacy and safety. AutoML can also be used to monitor patient progress and adjust treatment plans as needed. The implementation of AutoML in healthcare and life sciences should be done with caution and consideration for ethical and regulatory concerns.

Services Segment to account for higher CAGR during the forecast period

The market for Automated Machine Learning is bifurcated based on offering into solution and services. The CAGR of services is estimated to be highest during the forecast period. AutoML services allow users to automate various tasks involved in building and deploying machine learning models, such as feature engineering, hyperparameter tuning, model selection, and deployment. These services are designed to make it easier for businesses and individuals to leverage the power of machine learning without requiring extensive knowledge or expertise in the field.

Asia Pacific to exhibit the highest CAGR during the forecast period

The CAGR of Asia Pacific is estimated to be highest during the forecast period. Automated machine learning is rapidly growing in Asia Pacific, which includes China, India, Japan, South Korea, ASEAN, and ANZ (Australia and New Zealand). In recent years, there has been significant growth in the adoption of both AutoML and machine learning across various industries in Asia Pacific, driven by the region’s large and diverse datasets, as well as the need for faster and more efficient decision-making. Many companies in the region are also investing in the development of AutoML platforms and tools to help accelerate the adoption of AI and machine learning. To support the adoption of AutoML and machine learning, governments and organizations in the Asia Pacific region are investing in infrastructure and programs to promote innovation, education, and collaboration.

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Unique Features in the Automated Machine Learning (AutoML) Market

Even with automation, it’s important to understand how the sausage is made, especially in regulated industries. XAI helps users understand the inner workings of the models and make better decisions based on the results.

Traditionally, a lot of data science work goes into cleaning and prepping data for use in machine learning models. AutoML can automate some of these tasks, freeing up data scientists for more strategic work.

Some platforms are starting to specialize in particular domains, such as healthcare or finance. These platforms can offer pre-built templates and industry-specific algorithms that can give users a head start on building models for their specific needs.

Machine learning models can degrade over time as data changes.  AutoML platforms with monitoring features can help users keep their models up-to-date and performing well.

AutoML is all about making machine learning more accessible to a wider range of users. The best platforms will offer a no-code or low-code interface that allows users to build models without having to write a lot of code.

Major Highlights of the Automated Machine Learning Market

Market size is expected to reach over $6 billion by 2028, with a Compound Annual Growth Rate (CAGR) exceeding 40%. This surge is fueled by the increasing demand for AI solutions and the accessibility AutoML offers.

AutoML removes the barrier to entry for businesses by automating complex tasks in machine learning model development. This allows even those without extensive data science expertise to leverage the power of AI.

Cloud-based AutoML solutions are gaining traction due to their scalability, flexibility, and cost-effectiveness. Businesses can access these solutions without significant upfront investment in infrastructure .

AutoML streamlines the machine learning pipeline by automating tasks like model selection, hyperparameter tuning, and feature engineering. This translates to faster development times and improved efficiency.

As regulations and user trust become a bigger concern, XAI capabilities are becoming a crucial feature in AutoML platforms. XAI helps users understand how models arrive at their predictions, leading to better decision-making.

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Top Companies in the Automated Machine Learning Market

Key players operating in the Automated Machine Learning market across the globe are IBM (US), Oracle  (US), Microsoft  (US), ServiceNow  (US), Google  (US), Baidu  (China), AWS  (US), Alteryx  (US), Salesforce  (US), Altair  (US), Teradata  (US), H2O.ai  (US), DataRobot  (US), BigML  (US), Databricks  (US), Dataiku  (France), Alibaba Cloud  (China), Appier  (Taiwan), Squark  (US), Aible  (US), Datafold  (US), Boost.ai  (Norway), Tazi.ai  (US), Akkio  (US), Valohai  (Finland), dotData  (US), Qlik  (US), Mathworks  (US), HPE  (US), and SparkCognition  (US). These Automated Machine Learning vendors have adopted various organic and inorganic strategies to sustain their positions and increase their market shares in the global market.

ServiceNow Inc. is known for providing enterprise cloud computing solutions. It delivers digital workflows on a single enterprise cloud platform called the Now Platform. The product portfolio of the firm is mainly focused on providing information technology and employee and customer workflows. ServiceNow offers solutions for IT operations management that covers service mapping, delivery, and assurance solutions; and business management such as financial management, project portfolio suite, vendor performance management, and performance analytics, including governance, risk, and compliance; and application development services. The company operates in North America, Europe, the Middle East, Africa, the Asia Pacific, and others. In recent years, ServiceNow has also made significant investments in the field of automated machine learning (AutoML). The company’s AutoML platform, called Now Intelligence, is designed to help businesses build and deploy machine learning models more efficiently. Now Intelligence offers a range of features, including data ingestion, data preparation, and model training and deployment. The platform is built on top of ServiceNow’s core platform, which means that customers can leverage their existing ServiceNow data and workflows to build machine learning models without having to learn new tools or languages. With the increasing demand for AI and machine learning solutions in various industries, ServiceNow’s Now Intelligence platform is positioned to be a significant player in the AutoML market.

Baidu is a leading Chinese technology company which was founded in 2000 and is headquartered in Beijing, China. It offers a range of internet-related services, including search engines, online advertising, cloud storage, and artificial intelligence (AI) solutions. It is one of the largest AI and internet companies, with a focus on developing cutting-edge technologies to improve people’s lives. It is operating through segments ranging from transaction services, iQIYI, and search services, the company has an array of vertical search-based products for end users and online marketing services for multinational companies, large domestic businesses, and SMEs. Baidu App, Baidu Search, Baidu Feed, Haokan, Baidu Post Bar, Baidu Knows, Baidu Encyclopedia, Baidu Maps, Baidu IME, popIn, Simeji, and Facemoji are the range of products offered for end users, while Pay for Placement (P4P) and non-P4P are online marketing services offered to customers. Baidu’s services cover a wide range of verticals, including healthcare, education, finance, transportation, and autonomous driving, among others. The company has a significant presence in China, with headquarters in Beijing and offices across the country, as well as international offices in the US, Japan, and other regions. In autoML, Baidu offers a platform called EZDL that allows users to create and train their own deep learning models without requiring extensive programming knowledge. EZDL uses a drag-and-drop interface and provides pre-built templates for various tasks, including image classification and object detection. It also offers automatic model tuning and optimization to improve model accuracy. Baidu’s autoML platform is designed to be accessible to a wide range of users, including small and medium-sized businesses.

Salesforce is an American provider of cloud-based software, which offers CRM software and apps with a focus on sales, customer service, marketing automation, analytics, and application development. Salesforce has a community of over 10 million innovators, disruptors, and community shapers known as Trailblazers. The company offers a wide range of products and services across segments, including sales, services, marketing, application, analytics, employee experience, trailblazers and reskilling, and enablement and collaboration, most of which operate on a single trusted cloud platform. Its service offerings can be easily deployed through mobile devices and internet browsers and can be integrated with other platforms and enterprise applications. Salesforce has a cross-cloud technology named Salesforce 360, which helps its clients obtain a single integrated, holistic customer profile for various departments. The company caters to various industries, including BFSI, healthcare and life sciences, government, communications, retail, consumer goods, media, manufacturing, transportation, and hospitality, automotive, and education. It has a geographic presence in North America, Europe, APAC, and MEA. In the AutoML market, Salesforce offers Einstein AI platform that provides ML, NLP, and computer vision solutions. Einstein also provides various tools, including Einstein Discovery, Einstein Prediction Builder, Einstein Next Best Action, Einstein Language, Einstein Bots, and Einstein Vision among others. In AutoML, Salesforce has integrated AI and machine learning into its products to help businesses automate and improve their data analysis and decision-making processes. The company’s AI platform, Einstein, offers various features including natural language processing, predictive analytics, and automated data preparation. Additionally, Salesforce has formed partnerships and made acquisitions in the AutoML space, such as its collaboration with Google Cloud to integrate automl tools with its customer 360 platform, and its acquisition of Tableau, which has built-in automl capabilities.

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“Schneider Electric (France), HPE(US), Dell(US), Vertiv(US), Huawei(China), Eaton(Ireland), IBM(US), Rittal (Germany), Panduit(US), and Stulz(Germany)”

The micro mobile data center market is expected to grow at a Compound Annual Growth Rate (CAGR) of 13.3% during the forecast period, from USD 5.2 billion in 2024 to USD 9.6 billion by 2029. The adoption of micro mobile data centers is expected to be driven by the demand for edge computing solutions and the need for faster data processing and reduced latency.

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By application, the edge computing & IoT deployment segment will hold the largest market size during the forecast period.

With the rise of edge computing, processing is moved closer to the data source, reducing latency and improving efficiency. Edge computing enables real-time data analysis and decision-making, crucial for applications like IoT, where immediate responses are often necessary. Micro mobile data centers provide the infrastructure needed to support IoT deployments by offering computing power and storage capacity close to IoT devices.

Businesses are increasingly adopting edge computing solutions to meet the demands of real-time applications and reduce latency. This demand drives the need for micro mobile data centers that can be quickly deployed at the edge. The IoT market continues to expand across various industries, including manufacturing, healthcare, transportation, and smart cities.

By region, the Asia Pacific will grow at the highest growth rates during the forecast period.

The Asia Pacific region is experiencing a significant surge in the adoption of micro mobile data centers, driven by several key growth factors. These compact and versatile data centers offer unique advantages that align with the region’s dynamic technological landscape and burgeoning digital infrastructure needs. The Asia Pacific region is home to some of the fastest-growing cities globally. Countries like China, India, and Japan heavily invest in smart city projects, which require robust, low-latency data processing capabilities. The deployment of 5G technology is accelerating across the Asia Pacific region. Nations such as South Korea, China, and Japan are at the forefront of 5G implementation. Micromobile data centers support the 5G infrastructure by providing localized data processing, which is essential for minimizing latency and enhancing network performance.

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Unique Features in the Micro Mobile Data Center Market

MMDCs are designed to be deployed close to the data sources, reducing latency and improving the speed of data processing. They enable local data processing, which is crucial for applications that require real-time analytics and decision-making, such as autonomous vehicles, industrial automation, and smart cities.

MMDCs are typically small, modular units that can be easily deployed in various environments, including remote locations, urban areas, and even within existing infrastructure. Their modular nature allows for easy scalability. Additional units can be added as data needs grow, providing flexibility and future-proofing investments.

MMDCs are designed for rapid deployment, often arriving pre-configured and requiring minimal setup time. This is crucial for disaster recovery, temporary events, or rapidly changing business needs. Many MMDCs are built to be mobile, allowing them to be relocated as needed. This is particularly useful for industries like mining, military, and oil and gas.

MMDCs often come with robust physical security features, including reinforced enclosures, surveillance systems, and controlled access points to protect against unauthorized access and physical tampering. Integrated cybersecurity measures, such as encryption, intrusion detection systems, and secure access protocols, are commonly included to protect the data being processed and stored.

MMDCs are built to withstand harsh environmental conditions, including extreme temperatures, humidity, dust, and vibration. This makes them suitable for deployment in challenging environments like deserts, offshore platforms, and industrial sites. Many MMDCs are designed with energy-efficient cooling and power systems to minimize their environmental footprint and reduce operational costs.

Major Highlights of the Micro Mobile Data Center Market

There is a growing demand for MMDCs across various industries such as IT and telecom, healthcare, retail, manufacturing, and energy due to their ability to provide localized data processing and storage. The surge in edge computing applications, which require low latency and high-speed data processing, is a major driver for the growth of MMDCs.

Innovations in cooling technologies, such as liquid cooling and free-air cooling, are enhancing the efficiency and reliability of MMDCs. Integration of artificial intelligence (AI) and automation for better management, monitoring, and optimization of data center operations is becoming more prevalent.

MMDCs can be quickly deployed and scaled, providing flexibility for temporary or permanent installations across diverse locations, including remote and urban areas. Designed for easy relocation, MMDCs can be moved to different sites as needed, making them suitable for dynamic and changing environments.

Enhanced security features, including robust physical protection and advanced cybersecurity measures, are critical components of MMDCs, addressing growing concerns over data security. MMDCs are built to be resilient against natural disasters and harsh environmental conditions, ensuring continuous operation and data protection.

Focus on energy-efficient designs and sustainable operations to reduce carbon footprint and operational costs. Increasing trend towards the development of green MMDCs that utilize renewable energy sources and environmentally friendly materials.

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Top Companies in the Micro Mobile Data Center Market

Some of the key players operating in the micro mobile data center market are – Schneider Electric (France), HPE(US), Dell(US), Vertiv(US), Huawei(China), Eaton(Ireland), IBM(US), Rittal (Germany), Panduit(US), and Stulz(Germany).

Schneider Electric (France)

Schneider Electric is an eminent French multinational company providing energy and automation solutions for efficiency and sustainability. It offers its services to homes, buildings, data centers, infrastructure, and industries by utilizing energy technologies, automation, software, and services. It operates through two business segments: Energy Management and Industrial Automation. The company provides modular data centers through its prefabricated data center modules. The all-in-one module has prefabricated IT Infrastructure, i.e., UPS, cooling, racks, and management software that are factory assembled in one purpose-built, secure, weatherproof structure. It has air cooling, chilled water, and pumped refrigerant cooling options. The all-in-one containers address the challenges of deploying data centers in remote sites, industrial environments, and other locations where a dedicated building or space does not exist. It manufactures various products and services, including power management, Uninterrupted Power Supply (UPS) systems, surge suppressors, precision cooling equipment, power conditioning equipment, software, and professional and consulting services. It has manufacturing facilities in the US, Switzerland, Denmark, Ireland, the Philippines, China, India, and Brazil. As a trusted leader in energy management and digital transformation, Schneider Electric drives innovation in the micro mobile data center market, helping businesses worldwide stay agile and competitive in an increasingly interconnected world.

HPE(US)

Hewlett Packard Enterprise (HPE), an American multinational IT corporation, specializes in providing business-focused solutions in servers, storage, networking, containerization software, and consulting services. Established on November 1, 2015, in Palo Alto, California, as part of the split from the Hewlett-Packard Company, HPE emerged as a separate entity. HP Inc. was formed to handle other aspects of the business. HPE is simplifying data center operations and enhancing automation, integration, and security through its distributed services architecture design. With a focus on delivering high performance and Zero Trust Security, HPE Aruba Networking offers a unified and automated approach to data center networking, ensuring efficiency and reliability for its clients.

Rittal (Germany)

Rittal, based in Germany, is a global leader in providing comprehensive solutions for industrial enclosures, power distribution, climate control, and IT infrastructure. Established in 1961, the company has grown to become a prominent player in the data center market, offering a wide range of products including micro data centers. Rittal’s micro data center solutions are known for their modularity, scalability, and customization capabilities. They integrate power distribution, cooling, and monitoring systems into compact, efficient enclosures, enabling rapid deployment and high performance in various environments. Rittal’s commitment to innovation and quality has made it a trusted name in ensuring reliable and efficient IT infrastructure.

Stulz (Germany)

Stulz, headquartered in Hamburg, Germany, is a renowned global provider of climate control solutions for mission-critical applications. Founded in 1947, the company specializes in precision cooling systems designed for data centers and IT environments. Stulz’s product range includes air conditioning units, chillers, and micro data center cooling solutions. Their expertise in precision cooling ensures optimal thermal management, energy efficiency, and reliability. Stulz’s solutions are tailored to meet the specific needs of diverse environments, from large-scale data centers to compact micro data centers. With a strong focus on innovation and sustainability, Stulz has established itself as a key player in the data center cooling industry.

Panduit (US)

Panduit, an American company founded in 1955, is a leading provider of physical, electrical, and network infrastructure solutions. Headquartered in Tinley Park, Illinois, Panduit offers a comprehensive portfolio that includes solutions for data centers, enterprise networks, and industrial automation. Their micro data center solutions are designed to provide efficient, scalable, and reliable infrastructure in compact form factors. Panduit’s products are known for their quality, innovation, and ease of deployment, supporting rapid installation and maintenance. By integrating connectivity, power management, and environmental monitoring, Panduit ensures robust and resilient micro data center environments. Their commitment to high standards and customer satisfaction has made Panduit a trusted name in the industry.

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