Machine Learning Model Operationalization Management Global Market Report 2024

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 the increasing adoption rate of machine learning, growing adoption of machine learning (ML), rising complexity of models, increased data volumes, and the 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 forecasted growth in the upcoming period can be attributed to several factors, including the increasing demand for automation, a focus on model governance and compliance, integration with DevOps practices, a heightened focus on cost optimization, and increased investment in AI infrastructure. Major trends expected during this period include automated model deployment, technological advancements, and advancements in Automated Machine Learning (AutoML) practices.

The increasing demand for decision-making is expected to drive the growth of the machine learning model operationalization management market in the future. Decision-making involves making informed decisions quickly by utilizing up-to-date data and analytics. Machine learning model operationalization management (MLOps) facilitates real-time decision-making by enabling efficient deployment, monitoring, and management of machine learning models in production environments. For example, in September 2022, a report by CIO, a US-based technology and IT magazine, indicated that approximately 88% of IT decision-makers acknowledged the potential of data collection and analysis, and 84% of organizations have either deployed or planned data-driven projects. Hence, the demand for decision-making is fueling the growth of the machine learning model operationalization management market.

Major companies in the machine learning model operationalization management market are focusing on developing innovative products, including AutoML tools, to make machine learning more accessible, efficient, and scalable for organizations. AutoML (Automated Machine Learning) tools automate the process of building and deploying machine learning models. For example, in May 2021, Google Cloud launched Vertex AI, a managed machine learning (ML) platform that enables 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 automates many processes involved in building and fine-tuning machine learning models. It provides tools for data preparation, model training, and deployment.

In January 2023, McKinsey & Company, a US-based management consulting firm, acquired Iguazio for $50 million. This acquisition was intended to accelerate and scale enterprise AI deployments by leveraging Iguazio's expertise in MLOps, a set of tools and practices for managing and scaling machine learning models in production. Iguazio is an Israel-based company providing machine learning model operationalization management.

Major companies operating in the machine learning model operationalization management market report are 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.

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. The countries covered in the machine learning model operationalization management market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

Machine learning model operationalization management involves preparing a machine learning model for deployment and integration into business applications, analytical platforms, or other operational environments. It aims to streamline the analytics development life cycle and enhance model stability by automating repetitive workflow steps.

The primary components of machine learning model operationalization management are platforms and services. A platform for managing the operationalization of machine learning models is a software platform that enables organizations to efficiently deploy, manage, and monitor machine learning models in production environments. These platforms can be deployed in cloud-based or on-premise modes and cater to organizations of various sizes, including large enterprises and small and medium enterprises (SMEs). They find application in industries such as banking, financial services and insurance (BFSI), manufacturing, information technology (IT) and telecom, healthcare, and media and entertainment.

The machine learning model operationalization management market research report is one of a series of new reports that provides machine learning model operationalization management market statistics, including machine learning model operationalization management industry global market size, regional shares, competitors with a machine learning model operationalization management market share, detailed machine learning model operationalization management market segments, market trends and opportunities, and any further data you may need to thrive in the machine learning model operationalization management industry. This machine learning model operationalization management market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

The machine learning model operationalization management market includes revenues earned by entities by providing services such as model deployment, model monitoring, version control and rollback, integration with data pipelines, automated model retraining, and scalability and elasticity. The market value includes the value of related goods sold by the service provider or included within the service offering. The machine learning model operationalization management market also includes sales of central processing units (CPUs), graphics processing units (GPUs), and field-programmable gate arrays (FPGAs). Values in this market are ‘factory gate’ values, that is the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD, unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.


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