The global MLOps Market size is projected to grow from USD 1.1 billion in 2022 to USD 5.9 billion by 2027, at a CAGR of 41.0% during the forecast period, according to research report by MarketsandMarkets™.

The adoption rate of MLOps is increasing due to increased ability to consistently track and get insights into model performance. The enormous flexibility and administration features of MLOps enable supervision, control, management, and monitoring of thousands of models for continuous integration, continuous delivery, and continuous deployment are the key factors driving market growth.

Browse 188 market data Tables and 45 Figures spread through 219 Pages and in-depth TOC on "MLOps Market - Global Forecast to 2027"

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Various globally established players such as IBM (US), Google (US), Microsoft (US), HPE (US), and Alteryx (US) are dominating the MLOps market. These players have adopted various growth strategies, such as partnerships, agreements and collaborations, new product launches and product enhancements, and acquisitions, to expand their footprint in the MLOps market.

IBM offers IBM Watson Studio, an MLOps platform. The platform enables data scientists, developers, and analysts to build, run, and manage AI models. It also offers optimizing choices for data from anywhere on the platform. In June 2021, IBM collaborated with Black & Veatch to jointly market Asset Performance Management solutions. These solutions will include remote monitoring technologies that combine artificial intelligence and near real-time data analytics to support customers in maintaining equipment and assets at peak performance and dependability.

Another important player in the MLOps market is HPE one of the world's leading IT services and consulting company HPE is a worldwide edge-to-cloud corporation that enables an enterprise to connect, protect, analyze, and act on all of the data, from edge to cloud, to help transform, turn insights into outcomes and run digital business. The company is committed to provide an exceptional client experience. In March 2022, HPE announced it joining with BMW, the global leader in manufacturing premium automobiles and motorcycles. This partnership will help BMW to record electric test car data from throughout the world using HPE Ezmeral, providing BMW Group data.

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Key Dynamic Factors For MLOps Market:

Technological Advancements in AI and Machine Learning:

Continuous improvements in AI and machine learning technologies are driving the demand for scalable and effective MLOps solutions for managing, monitoring, and deploying models in production.

Growing Use of Cloud Services:

increasing availability of cloud-based MLOps tools and services, as well as a growing reliance on cloud platforms to host machine learning models and workflows.

Orchestration and Automation:

The ML lifecycle is being streamlined with a focus on automation and orchestration tools to minimise manual involvement and increase the effectiveness of model deployment and maintenance.

Combining DevOps Methods with Integration:

Integration of well-established DevOps techniques with MLOps methods to guarantee a smooth and cooperative workflow between the operations, development, and data science teams.

Flexibility and Scalability:

MLOps platforms' scalability and flexibility to handle the growing number and complexity of machine learning models and data.

Adherence to Regulations:

Growing recognition of the need of regulatory compliance while managing sensitive data has prompted the creation of MLOps solutions compliant with privacy and data protection laws.

Interpretability and Explainability:

Growing demand for MLOps technologies that address issues of bias, justice, and accountability by offering transparency and interpretability in machine learning models.

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Competitive and Segmentation Analysis:

Understanding the industry landscape and identifying important companies and market categories in the MLOps market requires a thorough understanding of competitive and segmentation analysis. Many businesses compete in this fast-paced industry to offer complete MLOps solutions that meet the numerous demands of businesses using machine learning into their operations. Established firms providing end-to-end MLOps systems and up-and-coming startups offering cutting-edge open-source tools define the competitive landscape. Typically, deployment methodologies (on-premises or in the cloud), organisation size, and industry verticals are used to segment the market.

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