The machine learning model operationalization management global market report 2024 from The Business Research Company provides comprehensive market statistics, including global market size, regional shares, competitor market share, detailed segments, trends, and opportunities. This report offers an in-depth analysis of current and future industry scenarios, delivering a complete perspective for thriving in the industrial automation software market.
Machine Learning Model Operationalization Management Market, 2024 report by The Business Research Company offers comprehensive insights into the current state of the market and highlights future growth opportunities.
Market Size -
The
machine learning model operationalization management market size has
grown exponentially in recent years. It will grow from $1.31 billion in
2023 to $1.92 billion in 2024 at a compound annual growth rate (CAGR) of
46.7%. The growth in the historic period can be attributed to
increasing adoption rate of machine learning, growing adoption of
machine learning (ML), rising complexity of models, increased data
volumes, and rise of edge computing.
The machine learning model operationalization management market size is expected to see exponential growth in the next few years. It will grow to $7.85 billion in 2028 at a compound annual growth rate (CAGR) of 42.1%. The growth in the forecast period can be attributed to demand for automation, focus on model governance and compliance, integration with DevOps practices, focus on cost optimization, and increased investment in AI infrastructure. Major trends in the forecast period include automated model deployment, technological advancements and AutoML advancements.
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Scope Of Machine Learning Model Operationalization Management Market
The Business Research Company's reports encompass a wide range of information, including:
1. Market Size (Historic and Forecast): Analysis of the market's historical performance and projections for future growth.
2. Drivers: Examination of the key factors propelling market growth.
3. Trends: Identification of emerging trends and patterns shaping the market landscape.
4. Key Segments: Breakdown of the market into its primary segments and their respective performance.
5. Focus Regions and Geographies: Insight into the most critical regions and geographical areas influencing the market.
6. Macro Economic Factors: Assessment of broader economic elements impacting the market.
Machine Learning Model Operationalization Management Market Overview
Market Drivers -
The
increasing demand for decision-making is expected to propel the growth
of the machine learning model operationalization management market going
forward. Decision-making refers to the ability to make informed
decisions quickly by utilizing up-to-the-minute data and analytics.
Machine learning model operationalization management (MLOps) helps in
real-time decision-making by enabling the efficient deployment,
monitoring, and management of machine learning models in production
environments. For instance, in September 2022, according to a report by
CIO, a US-based magazine related to technology and IT, around 88% of IT
decision-makers agreed that data collection and analysis have the
potential, and 84% of organizations have already deployed or have
data-driven projects on their roadmaps. Therefore, demand for
decision-making is driving the growth of the machine learning model
operationalization management market.
Market Trends -
Major
companies operating in the machine learning model operationalization
management market are developing innovative products, such as AutoML
tools, to make machine learning more accessible, efficient, and scalable
for organizations. AutoML (Automated Machine Learning) tools refer to
software platforms or frameworks that automate the process of building
and deploying machine learning models. For instance, in May 2021, Google
Cloud, a US-based provider of cloud computing services, launched Vertex
AI, a managed machine learning (ML) platform that allows companies to
accelerate the deployment and maintenance of artificial intelligence
(AI) models. Vertex AI's AutoML eliminates the need for extensive
machine learning expertise, and it automates many of the processes
involved in building and fine-tuning machine learning models. It
provides tools for data preparation, model training, and deployment.
The machine learning model operationalization management market covered in this report is segmented –
1) By Component: Platform, Services
2) By Deployment Mode: Cloud, On-premise
3) By Organization Size: Large Enterprises, Small And Medium Enterprises (SMEs)
4)
By End User: Banking, Financial Services And Insurance (BFSI),
Manufacturing, Information Technology (IT) And Telecom, Healthcare,
Media And Entertainment
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Regional Insights -
North
America was the largest region in the machine learning model
operationalization management market in 2023. Asia-Pacific is expected
to be the fastest-growing region in the forecast period. The regions
covered in the machine learning model operationalization management
market report are Asia-Pacific, Western Europe, Eastern Europe, North
America, South America, Middle East, Africa.
Key Companies -
Amazon.com
Inc., Google LLC, Microsoft Corporation, Dataiku, Azure Machine, IBM
Corporation, Hewlett-Packard enterprise Company, Databricks Inc.,
Alteryx Inc., Aporia, Cloudera Inc., DataRobot Inc., Fractal Analytics
Inc., Domino Data Lab Inc., Seldon Technologies Limited, Iguazio,
NeptuneLabs GmbH, Saturn Cloud Inc., H2O.ai Inc., ModelOp, Algorithmia,
SAS Model Manager, SAS Viya
Table of Contents
1. Executive Summary
2. Machine Learning Model Operationalization Management Market Report Structure
3. Machine Learning Model Operationalization Management Market Trends And Strategies
4. Machine Learning Model Operationalization Management Market – Macro Economic Scenario
5. Machine Learning Model Operationalization Management Market Size And Growth
…..
27. Machine Learning Model Operationalization Management Market Competitor Landscape And Company Profiles
28. Key Mergers And Acquisitions
29. Future Outlook and Potential Analysis
30. Appendix
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