세계 MLOps 시장의 예측(-2030년) : 컴포넌트별, 전개별, 기업 유형별, 용도별, 최종 사용자별, 지역별 분석

According to Stratistics MRC, the Global MLOps Market is accounted for $1441.60 million in 2024 and is expected to reach $11571.35 million by 2030 growing at a CAGR of 41.5% during the forecast period. MLOps, or Machine Learning Operations, is a field that streamlines and scales the deployment, monitoring, and management of machine learning models in production environments by fusing data engineering, DevOps, and machine learning techniques. Organizations can more quickly and reliably deploy models at scale owing to MLOps continuous integration, testing, and delivery of models. Moreover, businesses may lower operational friction, improve model accuracy through ongoing learning, and make sure their machine learning (ML) models stay applicable and useful in changing conditions by putting MLOps into practice.

According to the International Data Corporation (IDC), global spending on artificial intelligence systems is expected to reach $97.9 billion in 2023, driven by advancements in machine learning and the growing adoption of AI across various industries.

Market Dynamics:

Driver:

Growing use of AI and machine learning

One of the main factors propelling the MLOps market is the extensive use of AI and machine learning in sectors like manufacturing, finance, healthcare, and retail. Businesses are investing extensively in developing and implementing machine learning models as they realize the potential of AI to generate business insights, optimize processes, and improve customer experiences. Additionally, strong MLOps platforms are becoming more and more necessary due to the difficulty of incorporating AI into current business processes and the requirement to manage massive volumes of data.

Restraint:

Exorbitant implementation expenses

The high cost of implementing MLOps solutions is one of the major factors impeding the growth of the MLOps market. It takes a significant investment in infrastructure, tools, and talent to develop and implement an all-encompassing MLOps framework. To manage machine learning models throughout their entire lifecycle, organizations frequently need to invest in cloud services, high-performance computing resources, and sophisticated software tools. Furthermore, these expenses might be unaffordable for smaller businesses or those with tighter budgets, which would prevent them from fully implementing MLOps solutions.

Opportunity:

Growth of infrastructure-as-a-service (IaaS) and cloud computing

The infrastructure-as-a-service (IaaS) and cloud computing industries are growing quickly, which is opening up new market opportunities for MLOps. Machine learning model development, deployment, and management are supported by scalable and adaptable infrastructure provided by cloud platforms like AWS, Google Cloud, and Microsoft Azure. Moreover, the growing popularity of cloud-based solutions lowers the complexity and expense of managing hardware and software resources while enabling enterprises to take advantage of MLOps advantages, like automated model deployment and continuous monitoring.

Threat:

Growing market saturation and competition

A growing number of well-established tech companies and startups are entering the MLOps market, making it more competitive. Due to market saturation caused by this flood of competitors, it is harder for individual MLOps providers to stand out from the competition and take market share. In order to stay competitive, businesses may feel pressure to provide more sophisticated features or reduce costs, which could have an effect on sustainability and profitability. Additionally, the abundance of different MLOps solutions may confuse prospective clients, making it difficult for them to choose the one that best suits their unique requirements.

Covid-19 Impact:

Machine learning and artificial intelligence (AI) technologies have become increasingly popular in a variety of industries due to the COVID-19 pandemic. This is because businesses needed to optimize their operations and adjust to rapidly changing conditions. The demand for MLOps solutions that could effectively manage and deploy machine learning models at scale increased due to the rise in remote work, increased reliance on digital platforms, and the pressing need for data-driven insights. However, the pandemic also revealed weaknesses in the infrastructure that was already in place and brought attention to issues with scaling and securing MLOps frameworks.

The Platform segment is expected to be the largest during the forecast period

The platform segment has the largest share in the MLOps market. Model development, deployment, and monitoring are all streamlined in the machine learning lifecycle by the full range of tools and services provided by MLOps platforms. These platforms offer crucial features that improve an organization's efficiency and scalability, like version control, collaboration tools, and automated model training. Furthermore, these platforms are essential for companies looking to effectively use AI technology because they facilitate the faster and more dependable deployment of machine learning models by combining different phases of the ML workflow into a single system.

The Cloud segment is expected to have the highest CAGR during the forecast period

The cloud segment of the MLOps market is growing at the highest CAGR. Cloud-based MLOps solutions are very advantageous in terms of cost-effectiveness, scalability, and flexibility. With the help of these solutions, businesses can use cloud infrastructure to manage and deploy machine learning models without having to make significant investments in on-premise hardware. The cloud environment facilitates easy collaboration, dynamic resource allocation, and seamless integration with other cloud-based services, all of which speed up the creation and application of machine learning models. Moreover, the demand for cloud-based MLOps solutions is growing quickly as more companies use cloud technologies to improve their data processing capabilities and automate their AI operations.

Region with largest share:

The North American region is anticipated to hold the largest share of the MLOps market. The region's strong technological infrastructure, concentration of top technology companies, and large investments in machine learning and artificial intelligence projects are the main causes of its dominance. North America's dominant position is a result of its developed ecosystem of MLOps solution providers as well as its strong emphasis on innovation and research. Additionally, major data centers and cloud service providers are also present in the area, which encourages the widespread adoption of MLOps practices and puts North America at the forefront of the industry.

Region with highest CAGR:

The MLOps market is growing at the highest CAGR in the Asia-Pacific region. The region's growing digital infrastructure, rising use of AI technologies, and a spike in investments in machine learning and data analytics from the public and private sectors are all contributing to its rapid growth. Leading the way in technological innovation and advancement are nations like China, India, and Japan. Furthermore, the demand for MLOps solutions is being driven by the region's burgeoning tech startups and growing emphasis on digital transformation across various industries.

Key players in the market

Some of the key players in MLOps market include Google LLC, Allegro AI., Domino Data Lab, Inc., Cognizant, GAVS Technologies, Amazon Web Services Inc., Databricks, Inc., IBM Corporation, Cloudera, Inc, Microsoft Corporation, Hewlett Packard Enterprise Development LP, Alteryx, Valohai, DataRobot, Inc. and Neptune Labs Inc.

Key Developments:

In August 2024, Amazon has reached an agreement to acquire chip maker and AI model compression company Perceive, a San Jose, Calif.-based subsidiary of publicly traded technology company Xperi, for $80 million in cash. The deal was disclosed Friday afternoon in a filing by Xperi with the Securities and Exchange Commission.

In May 2024, Google LLC has entered into power purchase agreements (PPAs) with two Japanese energy providers securing 60 MW of solar capacity dedicated to providing electricity to the company's data centres in Japan. The tech giant said the PPAs, the first of their kind for Google in the country, were signed with Clean Energy Connect Inc, a partner of Itochu Corp (TYO:8001), and Shizen Energy.

In August 2023, Allegro MicroSystems announced it has signed a definitive agreement to acquire Crocus Technology, a developer of magnetic sensors based on tunnel-magnetoresistance (TMR) technology. The transaction amounts to $420 million and will be paid in cash. Crocus was spun off from Grenoble, France-based research laboratory in spintronics Spintec in 2006.

Components Covered:

• Platform
• Service

Deployments Covered:

• Cloud
• On-premise
• Hybrid

Enterprise Types Covered:

• SMEs
• Large Enterprises

Applications Covered:

• Data Management
• Model Infrastructure
• Other Applications

End Users Covered:

• IT & Telecom
• Healthcare and Life Sciences
• Banking, Financial Services, and Insurance
• Manufacturing
• Retail
• Government & Public Sector
• Advertising
• Transportation and Logistics
• Energy and Utilities
• Other End Users

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2022, 2023, 2024, 2026, and 2030
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Application Analysis
• 3.7 End User Analysis
• 3.8 Emerging Markets
• 3.9 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global MLOps Market, By Component

• 5.1 Introduction
• 5.2 Platform
• 5.3 Service

6 Global MLOps Market, By Deployment

• 6.1 Introduction
• 6.2 Cloud
• 6.3 On-premise
• 6.4 Hybrid

7 Global MLOps Market, By Enterprise Type

• 7.1 Introduction
• 7.2 SMEs
• 7.3 Large Enterprises

8 Global MLOps Market, By Application

• 8.1 Introduction
• 8.2 Data Management
• 8.3 Model Infrastructure
• 8.4 Other Applications

9 Global MLOps Market, By End User

• 9.1 Introduction
• 9.2 IT & Telecom
• 9.3 Healthcare and Life Sciences
• 9.4 Banking, Financial Services, and Insurance
• 9.5 Manufacturing
• 9.6 Retail
• 9.7 Government & Public Sector
• 9.8 Advertising
• 9.9 Transportation and Logistics
• 9.10 Energy and Utilities
• 9.11 Other End Users

10 Global MLOps Market, By Geography

• 10.1 Introduction
• 10.2 North America
  • 10.2.1 US
  • 10.2.2 Canada
  • 10.2.3 Mexico
• 10.3 Europe
  • 10.3.1 Germany
  • 10.3.2 UK
  • 10.3.3 Italy
  • 10.3.4 France
  • 10.3.5 Spain
  • 10.3.6 Rest of Europe
• 10.4 Asia Pacific
  • 10.4.1 Japan
  • 10.4.2 China
  • 10.4.3 India
  • 10.4.4 Australia
  • 10.4.5 New Zealand
  • 10.4.6 South Korea
  • 10.4.7 Rest of Asia Pacific
• 10.5 South America
  • 10.5.1 Argentina
  • 10.5.2 Brazil
  • 10.5.3 Chile
  • 10.5.4 Rest of South America
• 10.6 Middle East & Africa
  • 10.6.1 Saudi Arabia
  • 10.6.2 UAE
  • 10.6.3 Qatar
  • 10.6.4 South Africa
  • 10.6.5 Rest of Middle East & Africa

11 Key Developments

• 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 11.2 Acquisitions & Mergers
• 11.3 New Product Launch
• 11.4 Expansions
• 11.5 Other Key Strategies

12 Company Profiling

• 12.1 Google LLC
• 12.2 Allegro AI.
• 12.3 Domino Data Lab, Inc.
• 12.4 Cognizant
• 12.5 GAVS Technologies
• 12.6 Amazon Web Services Inc.
• 12.7 Databricks, Inc.
• 12.8 IBM Corporation
• 12.9 Cloudera, Inc
• 12.10 Microsoft Corporation
• 12.11 Hewlett Packard Enterprise Development LP
• 12.12 Alteryx
• 12.13 Valohai
• 12.14 DataRobot, Inc.
• 12.15 Neptune Labs Inc.