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“Dell (US), Vertiv (US), Schneider Electric (France), Huawei (China), Eaton (US), Johnson Controls (US), Delta Electronics (Taiwan), ABB (Switzerland), Rittal (Germany), Stulz (Germany) and Hubbell (PCX) (US).”

The Modular Data Center Market is projected to experience a Compound Annual Growth Rate (CAGR) of 17.7% from 2024 to 2030, increasing from USD 29.93 billion to USD 79.49 billion. This market growth is driven by the adaptability and scalability of modular data centers, which can be deployed more quickly than traditional data centers. These pre-engineered solutions allow organizations to establish data centers across various locations efficiently, ensuring optimal performance in each setting. Additionally, modular data centers are cost-effective and energy-efficient, allowing companies to expand seamlessly without major operational disruptions. This flexibility enables businesses to meet evolving technological needs, maintain a competitive edge, and manage complex, distributed infrastructures effectively.

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“As per form factor, skid-mounted is expected to grow at the highest CAGR during the forecast period.”

Skid-mounted systems are another flexible and easy to implement solution in the modular data center market. These systems are permanently mounted on a strong metallic structure that enables them to be easily connected to other machineries and electrical appliances in an organization. Skid-mounted systems are another type of systems that have the benefit of being pre-built in a factory, leaving little to setup on site. This pre-construction approach is not only beneficial in the installation process but also reduces the construction time. When used as pre-fabricated structures, skid-mounted modules can provide a convenient way to grow or create new data centers. They normally occupy less space than conventional enclosures, hence, enabling efficient use of the available space. Furthermore, these systems are normally cheaper and usually developed to fit certain operations of an organization. For organizations that need standby systems or require a solution for permanent installations, skid-mounted systems are a cost-effective option to improve the data center infrastructure.

“As per deployment type, indoor holds the largest share during the forecast period.”

Modular data centers for indoors are specifically planned to be implemented within the premises of buildings, including warehouses or office spaces, and take advantage of the available space for the IT facilities. These units are typically characterized by standard containerized modules that can be easily incorporated into existing structures. One of the major benefits of indoor deployments is that the environment is controlled, which is especially important for the performance and durability of some crucial equipment. Moreover, indoor installations can leverage pre-existing power, cooling, and networking infrastructure, thereby requiring less investment in these areas. However, these solutions can face issues such as space constraints, which can hinder expandability and future growth. Additionally, indoor environments may require enhanced security measures to protect sensitive data and equipment from unauthorized access. Modular data centers designed for indoor environments are preferred by organizations that need to maximize their premises while ensuring that operational capacity is not compromised, thus achieving an optimal balance between flexibility and efficiency in their IT solutions. In general, modular data centers for indoor use meet the requirements of organizations that need to improve their infrastructure without interrupting business processes.

“As per region, Asia Pacific will grow at the highest CAGR during the forecast period.”

The Asia Pacific region, comprising China, Japan, Australia, and New Zealand, has witnessed the fastest growth in the modular data center market over recent years, propelled by increasing demand for AI and cloud computing infrastructure. China’s investment in data center infrastructure has surged, with a staggering $61 billion allocated to support the rising demand for AI chips. Major companies like Alibaba are expanding their capacity, aiming for a 200 MW increase in hyperscale facilities across Beijing and Shanghai.

In Japan, significant investments are transforming the data center landscape. Oracle has committed $8 billion to bolster cloud computing and AI capabilities, while Microsoft is investing $2.9 billion to enhance its data center operations, reflecting the region’s focus on digital transformation. Australia is also actively contributing to this growth, with DXN delivering modular data centers that tackle supply chain challenges while emphasizing sustainability and innovation. The rapid digital transformation across the Asia Pacific has made it the fastest-growing market for modular data centers globally, driven by government initiatives, robust cloud adoption, and escalating data consumption. As companies adapt to this evolving landscape, the region is well-positioned to solidify its dominance in the global data center sector, paving the way for continued advancements and expansion in the years ahead.

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Unique Features in the Modular Data Center Market

Modular data centers are designed for quick deployment, making them ideal for companies that need to expand or establish data centers on a short timeline. Unlike traditional data centers, modular designs can be set up within weeks or even days, allowing for swift scaling in response to increased data demand. This flexibility helps organizations avoid the long lead times and disruptions typically associated with traditional data center builds.

One of the most significant advantages of modular data centers is their cost efficiency. By opting for pre-fabricated, pre-engineered solutions, companies reduce the upfront capital required for construction and infrastructure. Modular data centers also support phased investment, allowing organizations to add capacity as needed, avoiding the over-provisioning common in traditional data center setups.

Many modular data centers incorporate energy-efficient cooling, power, and airflow management systems, reducing power consumption and environmental impact. These solutions often come with advanced cooling technologies, such as liquid or immersion cooling, to maintain ideal temperatures. As a result, modular data centers help organizations achieve their sustainability goals by minimizing energy usage and cutting down on operational costs.

Modular data centers can be deployed in diverse environments, from urban areas to remote or challenging locations, due to their compact and self-contained design. This adaptability makes them suitable for sectors such as telecommunications, government, and military, where reliable data infrastructure may be required in isolated regions.

Since modular data centers are pre-engineered and typically self-contained, they often come with robust security features to protect data integrity and physical assets. Many are designed with advanced physical security measures and include options for redundant power and cooling systems, which ensures high reliability and uptime, critical for business continuity.

Major Highlights of the Modular Data Center Market

A key highlight of the market is the growing demand for data centers that can be deployed quickly and scaled flexibly. Modular data centers offer pre-engineered, ready-to-deploy solutions that reduce setup time drastically, helping organizations respond rapidly to changes in demand.

With businesses continually seeking ways to reduce operational costs, modular data centers are increasingly popular for their cost-saving benefits. These centers provide a cost-effective alternative to traditional data centers, minimizing capital expenditure through their prefabricated design and phased deployment model.

As organizations expand their data networks to support edge computing, modular data centers are emerging as an ideal choice for remote and edge deployments. These data centers can be established in remote, geographically challenging, or underserved areas, allowing businesses to bring data processing closer to end-users.

The modular data center market is evolving to support a broad range of emerging technologies, such as 5G, artificial intelligence (AI), machine learning, and Internet of Things (IoT) applications. Modular data centers provide the infrastructure needed for these technologies, delivering the processing power, low-latency support, and scalability required to accommodate them.

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Top Companies in the Modular Data Center Market

Key players in this industry operate primarily in North America and Europe. Dell, Vertiv, Schneider Electric, Huawei, Eaton, Johnson Controls, and Delta Electronics are among the leading companies in modular data center market. These companies have implemented a variety of tactics to improve their market position and achieve growth. Some examples are product debuts, agreements, partnerships, company consolidations, and extending into new regions through product innovation.

Dell

Dell Technologies is a prominent player in the modular data center market, leveraging its extensive expertise in infrastructure solutions and advanced technology to cater to the evolving demands of businesses worldwide. The company has a complete portfolio of modular data center solutions that are scalable, flexible and energy-efficient. Dell’s systems are most relevant for those organizations that have need to make the best use of the limited space and also require high performance from the systems in use. Through hyper-converged infrastructure, edge computing, and artificial intelligence, Dell provides customers with an opportunity to implement scalable data centers based on business requirements and operational conditions.

Dell’s commitment to innovation and customer-centric solutions has positioned it as a preferred partner for enterprises across various sectors, including healthcare, finance, and telecommunications. The company emphasizes sustainability through its modular designs, which facilitate energy efficiency and reduced carbon footprints. Additionally, Dell collaborates with key technology partners to enhance its offerings, providing customers with comprehensive solutions that include networking, security, and management capabilities. This strategic approach not only helps Dell to build up its market power but also responds to the increasing customers’ requirements for the flexible and robust computing platforms in the modern world of globalization and informatization. Since many businesses are transitioning to using modular data centers, Dell is prepared to provide the best technology and service possible.

Vertiv

Vertiv is a global specialist in the critical infrastructure that supports the modern digital economy and is committed to protecting the availability, reliability, and resilience of vital applications. Vertiv is very much conscious of its commitment to innovation and productivity and, thus, applies technological innovations and principles of mass production to its product offerings, which include modular data center solutions fitting the dynamic needs of businesses. The company’s approach is to provide easy to deploy and scalable modular solutions, for example, the Vertiv SmartMod, that does not sacrifice performance and safety, tackles the urgency of high density computing and reduces wasted capital. Vertiv has adopted the following strategies in order to be competitive in the modular data center market.  They emphasize pre-integration and prefabrication, significantly reducing build times and allowing customers to scale their operations quickly. Additionally, Vertiv’s commitment to energy efficiency helps clients lower their Power Usage Effectiveness (PUE) and operating costs while minimizing environmental impact. With over 1,500 modules shipped and more than 800 sites worldwide, Vertiv’s expertise in delivering flexible and reliable solutions positions them as a trusted partner for enterprises needing additional capacity close to existing facilities. This strategic approach not only enhances agility but also supports stakeholders in managing risks and budgets effectively, ultimately driving greater profitability and efficiency.

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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), Squark (US), Aible (US).”

The Automated Machine Learning (AutoML) market is projected to grow from USD 1.0 billion in 2023 to USD 6.4 billion by 2028, at an impressive CAGR of 44.6% over the forecast period. AutoML, a subset of artificial intelligence (AI), empowers users to develop machine learning applications without needing in-depth expertise in statistics or machine learning. This technology streamlines the creation of high-performance machine learning models, a process that traditionally required the skills of specialized data scientists and domain experts. Recent advancements in data science and AI have driven significant progress in AutoML, enhancing its capabilities and accessibility.

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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 Market

Automated Machine Learning (AutoML) platforms are designed to make machine learning accessible to users without requiring deep expertise in data science. By automating complex processes like data preprocessing, model selection, and hyperparameter tuning, AutoML enables non-experts to create effective machine learning models with minimal manual intervention.

AutoML significantly reduces the time and resources required for developing machine learning models. Traditionally, creating high-performance models involved lengthy processes managed by data scientists and engineers. AutoML streamlines these steps, accelerating model development and lowering costs.

AutoML platforms are highly scalable, allowing companies to adjust their machine learning workflows based on the volume of data and complexity of tasks. This scalability makes AutoML suitable for a range of industries, from healthcare to finance and retail, where companies can adapt these tools to suit their specific needs.

AutoML employs advanced optimization techniques, including hyperparameter tuning, feature engineering, and model ensembling, to enhance model performance. These techniques help users achieve high accuracy and efficiency without needing to manually configure these settings, producing models that rival those crafted by experienced data scientists.

A defining feature of the AutoML market is its role in democratizing access to AI and data science. By lowering the skill barrier, AutoML enables more people within organizations to work with machine learning, leading to increased innovation and collaboration.

Major Highlights of the Automated Machine Learning Market

AutoML significantly lowers the barriers to entry for machine learning by enabling users without extensive data science expertise to build robust models. This democratization allows organizations across industries to leverage AI insights, empowering departments such as marketing, finance, and operations to innovate and make data-driven decisions without depending solely on data science teams.

AutoML offers considerable time and cost savings by automating the traditionally labor-intensive steps involved in machine learning, such as data preprocessing, feature selection, and hyperparameter tuning.

The AutoML market is seeing widespread adoption across diverse sectors, including healthcare, finance, retail, and manufacturing. This broad applicability highlights AutoML’s versatility, with organizations using it for applications ranging from predictive maintenance in manufacturing to personalized recommendations in retail.

AutoML platforms are increasingly integrated with major cloud services and existing data pipelines, allowing businesses to seamlessly incorporate AutoML into their data infrastructures. With compatibility across platforms such as AWS, Google Cloud, and Microsoft Azure, AutoML enhances data accessibility and enables companies to deploy models efficiently in cloud or hybrid environments.

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Top Companies in the Automated Machine Learning Market

Major vendors in the global Automated Machine Learning market 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).

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.

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