The Global Machine Learning Model Operationalization Management (MLOps) Market size is expected to reach $8.5 billion by 2028, rising at a market growth of 38.9% CAGR during the forecast period.
Data scientists and operations specialists can work together and communicate using MLOps, a set of techniques. Implementing Machine Learning and Deep Learning models in large production environments can be automated while improving quality and streamlining the management process. In addition, aligning models with business demands and regulatory standards is simpler.
MLOps is gradually becoming a stand-alone method for managing the ML lifecycle. It covers every lifecycle stage, including data collection, model building (using the software development lifecycle and continuous integration/delivery), deployment, orchestration, health, governance, diagnostics, and business metrics.
Machine learning technology solutions are being aggressively adopted by businesses to improve the customer experience and support maximizing profit. Market participants are implementing advanced data processing and integration strategies to gather insights and get a competitive edge over rivals. The use of MLOps in enterprises is still in its infancy.
As people become more aware of the advantages of doing so, there will likely be lucrative chances for market expansion. The demand for cutting-edge solutions for improved data management is fueled by the expanding usage of data science technologies for improvements in computing power, artificial intelligence, and system learning.
The well-known industry verticals, such as retail, healthcare, education, telecommunication, manufacturing, and financial institutions, have a significant demand for machine learning. Standardized models and workflows are made possible with the assistance of ML Ops. Additionally, it facilitates the simple implementation of machine learning technology anywhere, which is the primary factor in enterprises" high preference for them.
The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation and Google LLC are the forerunners in the Machine Learning Model Operationalization Management (MLOps). Companies such as Amazon Web Services, Inc. (Amazon.com, Inc.), IBM Corporation, Hewlett-Packard enterprise Company are some of the key innovators in Machine Learning Model Operationalization Management (MLOps).
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Microsoft Corporation, Amazon Web Services, Inc. (Amazon.com, Inc.), Google LLC, IBM Corporation, Hewlett-Packard enterprise Company, Alteryx, Inc., Cloudera, Inc., DataRobot, Inc., Domino Data Lab, Inc., and H2O.ai, Inc.
Data scientists and operations specialists can work together and communicate using MLOps, a set of techniques. Implementing Machine Learning and Deep Learning models in large production environments can be automated while improving quality and streamlining the management process. In addition, aligning models with business demands and regulatory standards is simpler.
MLOps is gradually becoming a stand-alone method for managing the ML lifecycle. It covers every lifecycle stage, including data collection, model building (using the software development lifecycle and continuous integration/delivery), deployment, orchestration, health, governance, diagnostics, and business metrics.
Machine learning technology solutions are being aggressively adopted by businesses to improve the customer experience and support maximizing profit. Market participants are implementing advanced data processing and integration strategies to gather insights and get a competitive edge over rivals. The use of MLOps in enterprises is still in its infancy.
As people become more aware of the advantages of doing so, there will likely be lucrative chances for market expansion. The demand for cutting-edge solutions for improved data management is fueled by the expanding usage of data science technologies for improvements in computing power, artificial intelligence, and system learning.
The well-known industry verticals, such as retail, healthcare, education, telecommunication, manufacturing, and financial institutions, have a significant demand for machine learning. Standardized models and workflows are made possible with the assistance of ML Ops. Additionally, it facilitates the simple implementation of machine learning technology anywhere, which is the primary factor in enterprises" high preference for them.
COVID-19 Impact Analysis
The COVID-19 pandemic is anticipated to be aided by artificial intelligence technology. Several nations are using population surveillance techniques made possible by machine learning and artificial intelligence to track and trace COVID-19 cases. For instance, researchers in South Korea use geo-location information and surveillance camera footage to monitor coronavirus cases. In addition, data scientists use machine intelligence algorithms to anticipate the location of the next outbreak and notify the appropriate authorities, allowing for real-time illness tracking. This has permitted technologically advanced nations to put a speed breaker on the spread of the virus. Such active endeavors are projected to increase the demand for machine intelligence solutions during the upcoming period.Market Growth Factors
ML should be standardized for efficient teamwork
Manual data collection and reprocessing are inefficient and may yield unacceptable results. MLOps aids in automating the entire workflow of ML models. This comprises data collection, the model creation, testing, retraining, and deployment. MLOps assist businesses in reducing errors and saving time. For the company-wide adoption of ML models, IT and business professionals and data scientists and engineers are involved in cooperation.Use of Machine Leading Expanded in The Financial Sector
Financial institutions possess a vast amount of client information. They may collect information on purchases, spending habits, platform usage, and geo-locational preferences in addition to standard banking information, such as bank account balances, to create a 360-degree image of the consumer. This enables the bank to offer goods and services that are particularly tailored to the customer"s requirements and preferences. Therefore, the growing use of ML in the financial industry will fuel the expansion of the MLOps market.Market Restraining Factor
Lack of Expertise
While more SMBs in the machine learning as a service industry use cloud-based services, the time-consuming machine learning integration process will become significantly less time-consuming. It helps to enhance an organization"s efficiency without recruiting human resources by avoiding repetitive work. Organizations need now utilise MLOps in data management to collect and integrate the enormous volumes of data from several internal and external data sources and unite the data silos.
Components Outlook
Based on components, the Machine Learning Model Operationalization Management (MLOps) Market is categorized into Platform and Services. In 2021, the services segment recorded a sizable revenue share. MLOps solutions are being adopted by businesses worldwide to strengthen their customer interaction, brand recognition, and marketing initiatives. Organizations can effortlessly engage consumers, communicate more effectively, and broaden their reach using MLOps marketing tools.Deployment Mode Outlook
Based on deployment mode, the Machine Learning Model Operationalization Management (MLOps) Market is classified into On-Premises and Cloud. The cloud category had the most revenue share in the market in 2021. To boost employee productivity, the cloud-based system enables worldwide IT task outsourcing. Three other types of cloud computing exist private, public, and hybrid. The public cloud"s rising popularity is primarily due to its numerous organizational advantages, including flexibility and scalability, remote access, simplicity, speedier installation, and many other benefits.Organization Size Outlook
Based on organization size, the Machine Learning Model Operationalization Management (MLOps) Market is categorized into Large Enterprises and SMEs based on Organization Size. In 2021, the small and medium-sized business segment obtained a sizeable revenue share. This is because machine learning adoption enables SMEs to optimize their processes on a limited budget. Shortly, it is anticipated that AI and machine learning will be the key technologies that let SMEs access digital resources and save money on ICT.Vertical Outlook
Based on vertical, the Machine Learning Model Operationalization Management (MLOps) Market is categorized into BFSI, Retail and eCommerce, Government and Defense, Healthcare and Life Sciences, Manufacturing, Telecom, IT and ITeS, Energy, and Utilities, Transportation and Logistics, and Others. The BFSI sector produced the highest revenue share in the market in 2021. However, most banks also experience considerable difficulties managing inert models, particularly in settings where application deployments could be more active and influential. MLOps, which essentially applies DevOps techniques and methods to machine learning, can assist banks in swiftly and effectively addressing some of these issues.Regional Outlook
Based on geography, the Machine Learning Model Operationalization Management (MLOps) Market is classified into North America, Europe, Asia Pacific, and LAMEA. North America is anticipated to hold the most significant market share during the projection period. By market share, North America is one of the top regions for MLOps. MLOps in this region are expanding due to the use of ML technology by nations like the US and Canada in various application fields. The US is regarded as one of the key contributors to North American MLOps market.The Cardinal Matrix - Machine Learning Model Operationalization Management (MLOps) Market Competition Analysis
The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation and Google LLC are the forerunners in the Machine Learning Model Operationalization Management (MLOps). Companies such as Amazon Web Services, Inc. (Amazon.com, Inc.), IBM Corporation, Hewlett-Packard enterprise Company are some of the key innovators in Machine Learning Model Operationalization Management (MLOps).
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Microsoft Corporation, Amazon Web Services, Inc. (Amazon.com, Inc.), Google LLC, IBM Corporation, Hewlett-Packard enterprise Company, Alteryx, Inc., Cloudera, Inc., DataRobot, Inc., Domino Data Lab, Inc., and H2O.ai, Inc.
Recent Strategies Deployed in Machine Learning Model Operationalization Management (MLOps) Market
Partnerships, Collaborations and Agreements:
- Sep-2022: Domino Data Lab has collaborated with Nvidia, a Chipmaker company, and NetApp, a data management and storage system provider. This collaboration is aimed to advance the latest solutions and reference architecture that would help data science and machine learning workloads that are operating in muli cloud and hybrid systems.
- Mar-2022: Amazon collaborated with Virginia Tech, a public land-grant research university, and launched an initiative for ML and AI research. This collaboration would allow doctoral students who have applied for Amazon fellowships and are performing ML and AI research. Additionally, this would help the efforts of faculty members engaged in the field of research.
- Feb-2022: Microsoft entered into a partnership with Tata Consultancy Services, an Indian company focusing on providing information technology services and consulting. Under the partnership, Tata Consultancy Services leveraged its software, TCS Intelligent Urban Exchange (IUX) and TCS Customer Intelligence & Insights (CI&I), to enable businesses in providing hyper-personalized customer experiences. CI&I and IUX are supported by artificial intelligence (AI), and machine learning, and assist in real-time data analytics. The CI&I software empowered retailers, banks, insurers, and other businesses to gather insights, predictions, and recommended actions in real-time to enhance the satisfaction of customers.
- Mar-2021: Amazon partnered with Hugging Face, a company that develops tools for building applications using machine learning. Through this Partnership, the company would ease the use of Machine Learning models for organizations and provide advanced NLP features in comparatively lesser time.
- Feb-2021: Amazon Web Services entered into a partnership with Salesforce, a cloud-based software company. The partnership enabled us to utilize a complete set of Salesforce and AWS capabilities simultaneously to rapidly develop and deploy new business applications that facilitate digital transformation. Salesforce also embedded AWS services for voice, video, artificial intelligence (AI), and machine learning (ML) directly in new applications for sales, service, and industry vertical use cases.
Product Launches & Product Expansions:
- Dec-2022: Alteryx, Inc. launched Alteryx Machine Learning. This newly launched product consists of Time Series enhancements which would broaden the predictive power of the company"s machine learning product. The product also includes a user interface (UI) update with new model evaluation abilities creating the process of model development highly simple and intuitive.
- Apr-2022: Hewlett Packard released Machine Learning Development System (MLDS) and Swarm Learning, their new machine learning solutions. The two solutions are focused on simplifying the burdens of AI development in a development environment that progressively consists of large amounts of protected data and specialized hardware. The MLDS provides a full software and services stack, including a training platform (the HPE Machine Learning Development Environment), container management (Docker), cluster management (HPE Cluster Manager), and Red Hat Enterprise Linux
- May-2022: Hewlett Packard launched HPE Swarm Learning and the new Machine Learning (ML) Development System, two AI and ML-based solutions. These new solutions increase the accuracy of models, solve AI infrastructure burdens, and improve data privacy standards. The company declared the new tool a “breakthrough AI solution” that focuses on fast-tracking insights at the edge, with attributes ranging from identifying card fraud to diagnosing diseases.
- Jan-2022: Domino Data Lab unveiled Domino 5.0, the first Enterprise MLOps solution, an end-to-end software suite optimized to run AI workloads with VMWare. This newly launched platform would help the end-to-end data science lifecycle and offer data scientists in using the tools of their choice.
- May-2021: Google released Vertex AI, a novel managed machine learning platform that enables developers to more easily deploy and maintain their AI models. Engineers can use Vertex AI to manage video, image, text, and tabular datasets, and develop machine learning pipelines to train and analyze models utilizing Google Cloud algorithms or custom training code. After that, the engineers can install models for online or batch use cases all on scalable managed infrastructure.
- Mar-2021: Microsoft released updates to Azure Arc, its service that brought Azure products and management to multiple clouds, edge devices, and data centers with auditing, compliance, and role-based access. Microsoft also made Azure Arc-enabled Kubernetes available. Azure Arc-enabled Machine Learning and Azure Arc-enabled Kubernetes are developed to aid companies to find a balance between enjoying the advantages of the cloud and maintaining apps and maintaining apps and workloads on-premises for regulatory and operational reasons. The new services enable companies to implement Kubernetes clusters and create machine learning models where data lives, as well as handle applications and models from a single dashboard.
Acquisitions and Mergers:
- Jul-2021: DataRobot took over Algorithmia, a machine learning operations platform. The acquisition of Algorithmia would strengthen DataRobot’s position as the preeminent provider of complete solutions in the MLOps space, focused on offering machine learning models into production.
- Jun-2021: Hewlett Packard completed the acquisition of Determined AI, a San Francisco-based startup that offers a strong and solid software stack to train AI models faster, at any scale, utilizing its open-source machine learning (ML) platform. Hewlett Packard integrated Determined AI’s unique software solution with its world-leading AI and high-performance computing (HPC) products to empower ML engineers to conveniently deploy and train machine learning models to offer faster and more precise analysis from their data in almost every industry.
- May-2021: IBM acquired Waeg, a Salesforce Consulting Partner in Europe. Through this acquisition, IBM would broaden IBM"s suite of Salesforce services and develop IBM"s AI and hybrid cloud strategy. Additionally, this acquisition is based on IBM"s continued investment in Salesforce consulting services to address the growing client requirements for experience-led business transformation and the latest customer engagement strategies supported by machine learning, data, and AI.
Scope of the Study
By Component
- Platform
- Services
By Vertical
- BFSI
- IT & ITeS
- Manufacturing
- Retail & Ecommerce
- Government & Defense
- Healthcare & Life Sciences
- Telecom
- Energy & Utilities
- Travel & Tourism
- Others
By Organization size
- Large Enterprises
- SMEs
By Deployment Mode
- Cloud
- On-premise
By Geography
- North America
- US
- Canada
- Mexico
- Rest of North America
- Europe
- Germany
- UK
- France
- Russia
- Spain
- Italy
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- South Korea
- Singapore
- Malaysia
- Rest of Asia Pacific
- LAMEA
- Brazil
- Argentina
- UAE
- Saudi Arabia
- South Africa
- Nigeria
- Rest of LAMEA
Key Market Players
List of Companies Profiled in the Report:
- Microsoft Corporation
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- Google LLC
- IBM Corporation
- Hewlett-Packard enterprise Company
- Alteryx, Inc.
- Cloudera, Inc.
- DataRobot, Inc.
- Domino Data Lab, Inc.
- H2O.ai, Inc.
Unique Offerings
- Exhaustive coverage
- The highest number of market tables and figures
- Subscription-based model available
- Guaranteed best price
- Assured post sales research support with 10% customization free
Table of Contents
Chapter 1. Market Scope & Methodology1.1 Market Definition
1.2 Objectives
1.3 Market Scope
1.4 Segmentation
1.4.1 Global Machine Learning Model Operationalization Management (MLOps) Market, by Component
1.4.2 Global Machine Learning Model Operationalization Management (MLOps) Market, by Vertical
1.4.3 Global Machine Learning Model Operationalization Management (MLOps) Market, by Organization size
1.4.4 Global Machine Learning Model Operationalization Management (MLOps) Market, by Deployment Mode
1.4.5 Global Machine Learning Model Operationalization Management (MLOps) Market, by Geography
1.5 Methodology for the research
Chapter 2. Market Overview
2.1 Introduction
2.1.1 Overview
2.1.1.1 Market Composition and Scenario
2.2 Key Factors Impacting the Market
2.2.1 Market Drivers
2.2.2 Market Restraints
Chapter 3. Competition Analysis - Global
3.1 Cardinal Matrix
3.2 Recent Industry Wide Strategic Developments
3.2.1 Partnerships, Collaborations and Agreements
3.2.2 Product Launches and Product Expansions
3.2.3 Acquisition and Mergers
3.3 Top Winning Strategies
3.3.1 Key Leading Strategies: Percentage Distribution (2018-2022)
3.3.2 Key Strategic Move: (Product Launches and Product Expansions : 2018, Nov - 2022, Dec) Leading Players
Chapter 4. Global Machine Learning Model Operationalization Management (MLOps) Market by Component
4.1 Global Platform Market by Region
4.2 Global Services Market by Region
Chapter 5. Global Machine Learning Model Operationalization Management (MLOps) Market by Vertical
5.1 Global BFSI Market by Region
5.2 Global IT & ITeS Market by Region
5.3 Global Manufacturing Market by Region
5.4 Global Retail & Ecommerce Market by Region
5.5 Global Government & Defense Market by Region
5.6 Global Healthcare & Life Sciences Market by Region
5.7 Global Telecom Market by Region
5.8 Global Energy & Utilities Market by Region
5.9 Global Travel & Tourism Market by Region
5.1 Global Other Vertical Market by Region
Chapter 6. Global Machine Learning Model Operationalization Management (MLOps) Market by Organization size
6.1 Global Large Enterprises Market by Region
6.2 Global SMEs Market by Region
Chapter 7. Global Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
7.1 Global Cloud Market by Region
7.2 Global On-premise Market by Region
Chapter 8. Global Machine Learning Model Operationalization Management (MLOps) Market by Region
8.1 North America Machine Learning Model Operationalization Management (MLOps) Market
8.1.1 North America Machine Learning Model Operationalization Management (MLOps) Market by Component
8.1.1.1 North America Platform Market by Country
8.1.1.2 North America Services Market by Country
8.1.2 North America Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.1.2.1 North America BFSI Market by Country
8.1.2.2 North America IT & ITeS Market by Country
8.1.2.3 North America Manufacturing Market by Country
8.1.2.4 North America Retail & Ecommerce Market by Country
8.1.2.5 North America Government & Defense Market by Country
8.1.2.6 North America Healthcare & Life Sciences Market by Country
8.1.2.7 North America Telecom Market by Country
8.1.2.8 North America Energy & Utilities Market by Country
8.1.2.9 North America Travel & Tourism Market by Country
8.1.2.10 North America Other Vertical Market by Country
8.1.3 North America Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.1.3.1 North America Large Enterprises Market by Country
8.1.3.2 North America SMEs Market by Country
8.1.4 North America Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.1.4.1 North America Cloud Market by Country
8.1.4.2 North America On-premise Market by Country
8.1.5 North America Machine Learning Model Operationalization Management (MLOps) Market by Country
8.1.5.1 US Machine Learning Model Operationalization Management (MLOps) Market
8.1.5.1.1 US Machine Learning Model Operationalization Management (MLOps) Market by Component
8.1.5.1.2 US Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.1.5.1.3 US Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.1.5.1.4 US Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.1.5.2 Canada Machine Learning Model Operationalization Management (MLOps) Market
8.1.5.2.1 Canada Machine Learning Model Operationalization Management (MLOps) Market by Component
8.1.5.2.2 Canada Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.1.5.2.3 Canada Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.1.5.2.4 Canada Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.1.5.3 Mexico Machine Learning Model Operationalization Management (MLOps) Market
8.1.5.3.1 Mexico Machine Learning Model Operationalization Management (MLOps) Market by Component
8.1.5.3.2 Mexico Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.1.5.3.3 Mexico Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.1.5.3.4 Mexico Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.1.5.4 Rest of North America Machine Learning Model Operationalization Management (MLOps) Market
8.1.5.4.1 Rest of North America Machine Learning Model Operationalization Management (MLOps) Market by Component
8.1.5.4.2 Rest of North America Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.1.5.4.3 Rest of North America Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.1.5.4.4 Rest of North America Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.2 Europe Machine Learning Model Operationalization Management (MLOps) Market
8.2.1 Europe Machine Learning Model Operationalization Management (MLOps) Market by Component
8.2.1.1 Europe Platform Market by Country
8.2.1.2 Europe Services Market by Country
8.2.2 Europe Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.2.2.1 Europe BFSI Market by Country
8.2.2.2 Europe IT & ITeS Market by Country
8.2.2.3 Europe Manufacturing Market by Country
8.2.2.4 Europe Retail & Ecommerce Market by Country
8.2.2.5 Europe Government & Defense Market by Country
8.2.2.6 Europe Healthcare & Life Sciences Market by Country
8.2.2.7 Europe Telecom Market by Country
8.2.2.8 Europe Energy & Utilities Market by Country
8.2.2.9 Europe Travel & Tourism Market by Country
8.2.2.10 Europe Other Vertical Market by Country
8.2.3 Europe Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.2.3.1 Europe Large Enterprises Market by Country
8.2.3.2 Europe SMEs Market by Country
8.2.4 Europe Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.2.4.1 Europe Cloud Market by Country
8.2.4.2 Europe On-premise Market by Country
8.2.5 Europe Machine Learning Model Operationalization Management (MLOps) Market by Country
8.2.5.1 Germany Machine Learning Model Operationalization Management (MLOps) Market
8.2.5.1.1 Germany Machine Learning Model Operationalization Management (MLOps) Market by Component
8.2.5.1.2 Germany Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.2.5.1.3 Germany Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.2.5.1.4 Germany Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.2.5.2 UK Machine Learning Model Operationalization Management (MLOps) Market
8.2.5.2.1 UK Machine Learning Model Operationalization Management (MLOps) Market by Component
8.2.5.2.2 UK Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.2.5.2.3 UK Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.2.5.2.4 UK Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.2.5.3 France Machine Learning Model Operationalization Management (MLOps) Market
8.2.5.3.1 France Machine Learning Model Operationalization Management (MLOps) Market by Component
8.2.5.3.2 France Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.2.5.3.3 France Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.2.5.3.4 France Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.2.5.4 Russia Machine Learning Model Operationalization Management (MLOps) Market
8.2.5.4.1 Russia Machine Learning Model Operationalization Management (MLOps) Market by Component
8.2.5.4.2 Russia Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.2.5.4.3 Russia Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.2.5.4.4 Russia Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.2.5.5 Spain Machine Learning Model Operationalization Management (MLOps) Market
8.2.5.5.1 Spain Machine Learning Model Operationalization Management (MLOps) Market by Component
8.2.5.5.2 Spain Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.2.5.5.3 Spain Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.2.5.5.4 Spain Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.2.5.6 Italy Machine Learning Model Operationalization Management (MLOps) Market
8.2.5.6.1 Italy Machine Learning Model Operationalization Management (MLOps) Market by Component
8.2.5.6.2 Italy Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.2.5.6.3 Italy Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.2.5.6.4 Italy Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.2.5.7 Rest of Europe Machine Learning Model Operationalization Management (MLOps) Market
8.2.5.7.1 Rest of Europe Machine Learning Model Operationalization Management (MLOps) Market by Component
8.2.5.7.2 Rest of Europe Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.2.5.7.3 Rest of Europe Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.2.5.7.4 Rest of Europe Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.3 Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market
8.3.1 Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Component
8.3.1.1 Asia Pacific Platform Market by Country
8.3.1.2 Asia Pacific Services Market by Country
8.3.2 Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.3.2.1 Asia Pacific BFSI Market by Country
8.3.2.2 Asia Pacific IT & ITeS Market by Country
8.3.2.3 Asia Pacific Manufacturing Market by Country
8.3.2.4 Asia Pacific Retail & Ecommerce Market by Country
8.3.2.5 Asia Pacific Government & Defense Market by Country
8.3.2.6 Asia Pacific Healthcare & Life Sciences Market by Country
8.3.2.7 Asia Pacific Telecom Market by Country
8.3.2.8 Asia Pacific Energy & Utilities Market by Country
8.3.2.9 Asia Pacific Travel & Tourism Market by Country
8.3.2.10 Asia Pacific Other Vertical Market by Country
8.3.3 Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.3.3.1 Asia Pacific Large Enterprises Market by Country
8.3.3.2 Asia Pacific SMEs Market by Country
8.3.4 Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.3.4.1 Asia Pacific Cloud Market by Country
8.3.4.2 Asia Pacific On-premise Market by Country
8.3.5 Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Country
8.3.5.1 China Machine Learning Model Operationalization Management (MLOps) Market
8.3.5.1.1 China Machine Learning Model Operationalization Management (MLOps) Market by Component
8.3.5.1.2 China Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.3.5.1.3 China Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.3.5.1.4 China Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.3.5.2 Japan Machine Learning Model Operationalization Management (MLOps) Market
8.3.5.2.1 Japan Machine Learning Model Operationalization Management (MLOps) Market by Component
8.3.5.2.2 Japan Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.3.5.2.3 Japan Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.3.5.2.4 Japan Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.3.5.3 India Machine Learning Model Operationalization Management (MLOps) Market
8.3.5.3.1 India Machine Learning Model Operationalization Management (MLOps) Market by Component
8.3.5.3.2 India Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.3.5.3.3 India Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.3.5.3.4 India Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.3.5.4 South Korea Machine Learning Model Operationalization Management (MLOps) Market
8.3.5.4.1 South Korea Machine Learning Model Operationalization Management (MLOps) Market by Component
8.3.5.4.2 South Korea Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.3.5.4.3 South Korea Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.3.5.4.4 South Korea Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.3.5.5 Singapore Machine Learning Model Operationalization Management (MLOps) Market
8.3.5.5.1 Singapore Machine Learning Model Operationalization Management (MLOps) Market by Component
8.3.5.5.2 Singapore Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.3.5.5.3 Singapore Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.3.5.5.4 Singapore Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.3.5.6 Malaysia Machine Learning Model Operationalization Management (MLOps) Market
8.3.5.6.1 Malaysia Machine Learning Model Operationalization Management (MLOps) Market by Component
8.3.5.6.2 Malaysia Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.3.5.6.3 Malaysia Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.3.5.6.4 Malaysia Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.3.5.7 Rest of Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market
8.3.5.7.1 Rest of Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Component
8.3.5.7.2 Rest of Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.3.5.7.3 Rest of Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.3.5.7.4 Rest of Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.4 LAMEA Machine Learning Model Operationalization Management (MLOps) Market
8.4.1 LAMEA Machine Learning Model Operationalization Management (MLOps) Market by Component
8.4.1.1 LAMEA Platform Market by Country
8.4.1.2 LAMEA Services Market by Country
8.4.2 LAMEA Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.4.2.1 LAMEA BFSI Market by Country
8.4.2.2 LAMEA IT & ITeS Market by Country
8.4.2.3 LAMEA Manufacturing Market by Country
8.4.2.4 LAMEA Retail & Ecommerce Market by Country
8.4.2.5 LAMEA Government & Defense Market by Country
8.4.2.6 LAMEA Healthcare & Life Sciences Market by Country
8.4.2.7 LAMEA Telecom Market by Country
8.4.2.8 LAMEA Energy & Utilities Market by Country
8.4.2.9 LAMEA Travel & Tourism Market by Country
8.4.2.10 LAMEA Other Vertical Market by Country
8.4.3 LAMEA Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.4.3.1 LAMEA Large Enterprises Market by Country
8.4.3.2 LAMEA SMEs Market by Country
8.4.4 LAMEA Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.4.4.1 LAMEA Cloud Market by Country
8.4.4.2 LAMEA On-premise Market by Country
8.4.5 LAMEA Machine Learning Model Operationalization Management (MLOps) Market by Country
8.4.5.1 Brazil Machine Learning Model Operationalization Management (MLOps) Market
8.4.5.1.1 Brazil Machine Learning Model Operationalization Management (MLOps) Market by Component
8.4.5.1.2 Brazil Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.4.5.1.3 Brazil Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.4.5.1.4 Brazil Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.4.5.2 Argentina Machine Learning Model Operationalization Management (MLOps) Market
8.4.5.2.1 Argentina Machine Learning Model Operationalization Management (MLOps) Market by Component
8.4.5.2.2 Argentina Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.4.5.2.3 Argentina Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.4.5.2.4 Argentina Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.4.5.3 UAE Machine Learning Model Operationalization Management (MLOps) Market
8.4.5.3.1 UAE Machine Learning Model Operationalization Management (MLOps) Market by Component
8.4.5.3.2 UAE Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.4.5.3.3 UAE Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.4.5.3.4 UAE Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.4.5.4 Saudi Arabia Machine Learning Model Operationalization Management (MLOps) Market
8.4.5.4.1 Saudi Arabia Machine Learning Model Operationalization Management (MLOps) Market by Component
8.4.5.4.2 Saudi Arabia Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.4.5.4.3 Saudi Arabia Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.4.5.4.4 Saudi Arabia Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.4.5.5 South Africa Machine Learning Model Operationalization Management (MLOps) Market
8.4.5.5.1 South Africa Machine Learning Model Operationalization Management (MLOps) Market by Component
8.4.5.5.2 South Africa Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.4.5.5.3 South Africa Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.4.5.5.4 South Africa Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.4.5.6 Nigeria Machine Learning Model Operationalization Management (MLOps) Market
8.4.5.6.1 Nigeria Machine Learning Model Operationalization Management (MLOps) Market by Component
8.4.5.6.2 Nigeria Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.4.5.6.3 Nigeria Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.4.5.6.4 Nigeria Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.4.5.7 Rest of LAMEA Machine Learning Model Operationalization Management (MLOps) Market
8.4.5.7.1 Rest of LAMEA Machine Learning Model Operationalization Management (MLOps) Market by Component
8.4.5.7.2 Rest of LAMEA Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.4.5.7.3 Rest of LAMEA Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.4.5.7.4 Rest of LAMEA Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
Chapter 9. Company Profiles
9.1 Microsoft Corporation
9.1.1 Company Overview
9.1.2 Financial Analysis
9.1.4 Research & Development Expenses
9.1.5 Recent strategies and developments:
9.1.5.1 Partnerships, Collaborations, and Agreements:
9.1.5.2 Product Launches and Product Expansions:
9.1.6 SWOT Analysis
9.2 Amazon Web Services, Inc. (Amazon.com, Inc.)
9.2.1 Company Overview
9.2.2 Financial Analysis
9.2.3 Segmental Analysis
9.2.4 Recent strategies and developments:
9.2.4.1 Partnerships, Collaborations, and Agreements:
9.2.4.2 Product Launches and Product Expansions:
9.2.5 SWOT Analysis
9.3 Google LLC
9.3.1 Company Overview
9.3.2 Financial Analysis
9.3.3 Segmental and Regional Analysis
9.3.4 Research & Development Expense
9.3.5 Recent strategies and developments:
9.3.5.1 Product Launches and Product Expansions:
9.3.6 SWOT Analysis
9.4 IBM Corporation
9.4.1 Company Overview
9.4.2 Financial Analysis
9.4.3 Regional & Segmental Analysis
9.4.4 Research & Development Expenses
9.4.5 Recent strategies and developments:
9.4.5.1 Acquisition and Mergers:
9.4.1 SWOT Analysis
9.5 Hewlett Packard Enterprise Company
9.5.1 Company Overview
9.5.2 Financial Analysis
9.5.3 Segmental and Regional Analysis
9.5.4 Research & Development Expense
9.5.5 Recent strategies and developments:
9.5.5.1 Product Launches and Product Expansions:
9.5.5.2 Acquisition and Mergers:
9.6 Alteryx, Inc.
9.6.1 Company Overview
9.6.2 Financial Analysis
9.6.3 Regional Analysis
9.6.4 Research & Development Expense
9.6.5 Recent strategies and developments:
9.6.5.1 Product Launches and Product Expansions:
9.7 Cloudera, Inc.
9.7.1 Company Overview
9.7.2 Financial Analysis
9.7.3 Segmental Analysis
9.7.4 Research & Development Expense
9.8 DataRobot, Inc.
9.8.1 Company Overview
9.8.2 Recent strategies and developments:
9.8.2.1 Acquisition and Mergers:
9.9 Domino Data Lab, Inc.
9.9.1 Company Overview
9.9.2 Recent strategies and developments:
9.9.2.1 Partnerships, Collaborations, and Agreements:
9.9.2.2 Product Launches and Product Expansions:
9.10. H2O.ai, Inc.
9.10.1 Company Overview
9.10.2 Recent strategies and developments:
9.10.2.1 Partnerships, Collaborations, and Agreements:
9.10.2.2 Product Launches and Product Expansions:
Companies Mentioned
- Microsoft Corporation
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- Google LLC
- IBM Corporation
- Hewlett-Packard enterprise Company
- Alteryx, Inc.
- Cloudera, Inc.
- DataRobot, Inc.
- Domino Data Lab, Inc.
- H2O.ai, Inc.
Methodology
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