MLOps is a set of practices for collaboration and communication between data scientists and operations professionals. Applying these practices increases the quality, simplifies the management process, and automates the deployment of machine learning and deep learning models in large-scale production environments. It is easier to align models with business needs, as well as regulatory requirements. MLOps is slowly evolving into an independent approach to ML lifecycle management. It applies to the entire lifecycle - data gathering, model creation, orchestration, deployment, health, diagnostics, governance, and business metrics.
Furthermore, adopting MLOps practices provides faster time-to-market for ML projects. It provides self-service environments with access to curated data sets and lets data engineers and data scientists move faster and waste less time with missing or invalid data. In addition, automating all the steps in the MLDC helps to ensure a repeatable process, including how the model is trained, evaluated, versioned, and deployed. Also, MLOps enforce policies that guard against model bias and track changes to data statistical properties and model quality over time. Such benefits provide lucrative opportunities for market growth during the forecast period.
Furthermore, MLOps helps managers and developers to be more agile and strategic in their decisions. In addition, it serves as the map to guide individuals, small teams, and even businesses to achieve their goals, no matter their constraints, be it sensitive data, fewer resources, or small budget.
The MLOps market is segmented into component, deployment mode, organization size, industry vertical, and region. By component, it is bifurcated into platform and service. By deployment mode, it is divided into on-premise and cloud. By organization size, the market is segregated into large enterprises and small & medium-sized enterprises. By industry vertical, the market is classified into BFSI, manufacturing, IT & telecom, retail & e-commerce, energy & utility, healthcare, media and entertainment and others. Region wise, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
The market players operating in the MLOps market are Akira AI, Alteryx, Amazon Web Services, Inc., Cloudera, Inc., Databricks, Inc., DataRobot, Inc., GAVS Technologies, Google LLC, IBM Corporation and Microsoft Corporation. These major players have adopted various key development strategies such as business expansion, new product launches, and partnerships, which help to drive the growth of the MLOps market globally.
Furthermore, adopting MLOps practices provides faster time-to-market for ML projects. It provides self-service environments with access to curated data sets and lets data engineers and data scientists move faster and waste less time with missing or invalid data. In addition, automating all the steps in the MLDC helps to ensure a repeatable process, including how the model is trained, evaluated, versioned, and deployed. Also, MLOps enforce policies that guard against model bias and track changes to data statistical properties and model quality over time. Such benefits provide lucrative opportunities for market growth during the forecast period.
Furthermore, MLOps helps managers and developers to be more agile and strategic in their decisions. In addition, it serves as the map to guide individuals, small teams, and even businesses to achieve their goals, no matter their constraints, be it sensitive data, fewer resources, or small budget.
The MLOps market is segmented into component, deployment mode, organization size, industry vertical, and region. By component, it is bifurcated into platform and service. By deployment mode, it is divided into on-premise and cloud. By organization size, the market is segregated into large enterprises and small & medium-sized enterprises. By industry vertical, the market is classified into BFSI, manufacturing, IT & telecom, retail & e-commerce, energy & utility, healthcare, media and entertainment and others. Region wise, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
The market players operating in the MLOps market are Akira AI, Alteryx, Amazon Web Services, Inc., Cloudera, Inc., Databricks, Inc., DataRobot, Inc., GAVS Technologies, Google LLC, IBM Corporation and Microsoft Corporation. These major players have adopted various key development strategies such as business expansion, new product launches, and partnerships, which help to drive the growth of the MLOps market globally.
Key Benefits For Stakeholders
- The study provides an in-depth analysis of the MLOps market along with the current trends and future estimations to elucidate the imminent investment pockets.
- Information about key drivers, restrains, and opportunities and their impact analysis on the MLOps market size is provided in the report.
- The Porter’s five forces analysis illustrates the potency of buyers and suppliers operating in the MLOps industry.
- The quantitative analysis of the global sports management market for the period 2022-2032 is provided to determine the MLOps market potential.
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- Investment Opportunities
- Technology Trend Analysis
- Regulatory Guidelines
- Strategic Recommedations
- Expanded list for Company Profiles
- SWOT Analysis
Key Market Segments
By Component
- Platform
- Service
By Deployment Mode
- On-premise
- Cloud
By Organization Size
- Large Enterprises
- Small and Medium-sized Enterprises
By Industry Vertical
- BFSI
- Manufacturing
- IT and Telecom
- Retail and E-commerce
- Energy and Utility
- Healthcare
- Media and Entertainment
- Others
By Region
- North America
- U.S.
- Canada
- Europe
- UK
- Germany
- France
- Italy
- Spain
- Rest of Europe
- Asia-Pacific
- China
- Japan
- India
- Australia
- South Korea
- Rest of Asia-Pacific
- LAMEA
- Latin America
- Middle East
- Africa
- Key Market Players
- DataRobot, Inc.
- Microsoft Corporation
- Amazon Web Services, Inc.
- Alteryx
- Cloudera, Inc.
- GAVS Technologies
- IBM Corporation
- Databricks, Inc.
- Akira AI
- Google LLC
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Table of Contents
CHAPTER 1: INTRODUCTION
CHAPTER 2: EXECUTIVE SUMMARY
CHAPTER 3: MARKET OVERVIEW
CHAPTER 4: MLOPS MARKET, BY COMPONENT
CHAPTER 5: MLOPS MARKET, BY DEPLOYMENT MODE
CHAPTER 6: MLOPS MARKET, BY ORGANIZATION SIZE
CHAPTER 7: MLOPS MARKET, BY INDUSTRY VERTICAL
CHAPTER 8: MLOPS MARKET, BY REGION
CHAPTER 9: COMPETITIVE LANDSCAPE
CHAPTER 10: COMPANY PROFILES
List of Tables
List of Figures
Executive Summary
According to the report, the mlops market was valued at $983.64 million in 2021, and is estimated to reach $23.1 billion by 2031, growing at a CAGR of 37.5% from 2022 to 2031.The MLOps Market is likely to experience a significant growth rate of 37.5% from 2022-2031, owing to increasing market demand from IT and telecom sector.
MLOps is the combination of machine learning and operations. It encompasses both data science and software operations. At the same time, operations encompasses all the activities involved in running applications, such as setting up servers, implementing infrastructure, and managing application performance. The goal of MLOps is to provide companies with a means to rapidly deploy machine learning models into their production environments while ensuring that can continuously improve these models over time.
On the contrary, the machine learning models in decision-making becomes more prevalent, it is increasingly important to ensure they are deployed responsibly. There are a lot of causes of algorithmic bias that need to be avoided when decisions are automated through AI systems. Failure to successfully minimize bias can lead to punishment through government regulation or loss of demand and profits for a brand. To responsibly deploy AI systems, models must be trained on compliant and unbiased data sources, have interpretable and explainable results, and accountability throughout the organization where it is easy to find where in the development pipeline something potentially went wrong to fix it effectively. To deploy AI models responsibly, organizations require a streamlined ML model life cycle. Deploying more and more models requires MLOps to be successful. It is necessary to have a streamlined process to keep track of versioning, to understand model performance versus retrained model performance, and to ensure that a model continues to perform well over time. Without these MLOps, it is very difficult to successfully add value and business impact with these models.
Factors such as increase in investments in AI/ML based systems and rise of data-centric approach to MLOps, drives the growth of the market. In addition, increase in the number of libraries and packages for MLOps tasks, strengthening the growth of the market for future. However, inaccessible data and data security, rigid business models, lack of talent and time-consuming implementation, hamper the growth of the market. Furthermore, rapid changes in business model software due to geographical expansion of the businesses is expected to provide the lucrative growth opportunities for the market during the forecast period.
The MLOps market is segmented into component, deployment mode, organization size, industry vertical, and region. By component, it is bifurcated into platform and service. By deployment mode, it is divided into on-premise and cloud. By organizational size, the market is segregated into small & medium-sized enterprises and large enterprises. By industry vertical, the market is classified into BFSI, manufacturing, IT & telecom, retail & e-commerce, energy & utility, healthcare, media and entertainment and others. Region wise, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
The market players operating in the MLOps market are Akira AI, Alteryx, Amazon Web Services, Inc., Cloudera, Inc., Databricks, Inc., DataRobot, Inc., GAVS Technologies, Google LLC, IBM Corporation and Microsoft Corporation. The players in the market have been actively engaged in the adoption various strategies such as partnership, product launch, collaboration, and acquisition to remain competitive and gain advantage over the competitors in the market. For instance, in June 2019, DataRobot, the leader in enterprise AI, acquired ParallelM, a Santa Clara, CA-based company that created the machine learning operations (MLOps) category, which helps organizations scale the deployment, management, and governance of machine learning in production using any ML platform on any cloud or on-premise environment.
Key Market Insights
By component, the platform segment was the highest revenue contributor to the market in 2021, and is estimated to reach $14.66 billion by 2031, with a CAGR of 36%. However, the services segment is estimated to be the fastest growing segment with the CAGR of 39.6% during the forecast period.By deployment mode, the on-premise segment dominated the global market share in 2021, and is estimated to reach $11.43 billion by 2031, with a CAGR of 36.1%, However, the cloud segment is expected to be the fastest growing segment of market during the forecast period.
Based on organization size, the large enterprises segment was the highest revenue contributor to the market, with $6,38.1million in 2021, and is estimated to reach $13.69 billion by 2031, with a CAGR of 36.2%.
Based on industry vertical, the IT and telecom sector was the highest revenue contributor to the market, with $229.41 million in 2021, and is estimated to reach $3.72 billion by 2031, with a CAGR of 32.4%.
Region wise, North America was the highest revenue contributor, accounting for $3,57.16 million in 2021, and is estimated to reach $7.30 billion by 2031, with a CAGR of 35.6%.
Companies Mentioned
- DataRobot, Inc.
- Microsoft Corporation
- Amazon Web Services, Inc.
- Alteryx
- Cloudera, Inc.
- GAVS Technologies
- IBM Corporation
- Databricks, Inc.
- Akira AI
- Google LLC
Methodology
The analyst offers exhaustive research and analysis based on a wide variety of factual inputs, which largely include interviews with industry participants, reliable statistics, and regional intelligence. The in-house industry experts play an instrumental role in designing analytic tools and models, tailored to the requirements of a particular industry segment. The primary research efforts include reaching out participants through mail, tele-conversations, referrals, professional networks, and face-to-face interactions.
They are also in professional corporate relations with various companies that allow them greater flexibility for reaching out to industry participants and commentators for interviews and discussions.
They also refer to a broad array of industry sources for their secondary research, which typically include; however, not limited to:
- Company SEC filings, annual reports, company websites, broker & financial reports, and investor presentations for competitive scenario and shape of the industry
- Scientific and technical writings for product information and related preemptions
- Regional government and statistical databases for macro analysis
- Authentic news articles and other related releases for market evaluation
- Internal and external proprietary databases, key market indicators, and relevant press releases for market estimates and forecast
Furthermore, the accuracy of the data will be analyzed and validated by conducting additional primaries with various industry experts and KOLs. They also provide robust post-sales support to clients.
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