Press release
MLOps Market Set to Exceed USD 17.4 Billion by 2030 Driven by Growing Demand for Cloud Based MLOps Solutions
MLOps Market Report
MLOps, an adaptation of DevOps practices for machine learning, streamlines ML development processes. It transitions companies from manual ML model usage to integration throughout the organization's operations. This transformation significantly enhances delivery time, reduces defects, and amplifies data science productivity, paving the way for substantial market growth.
MLOps serves as the pivotal bridge between machine learning, data science, and data engineering. It seamlessly unifies these functions, ensuring consistent deployment of ML solutions, thereby amplifying productivity and efficacy. This technology thrives on an operational framework amalgamated with the best practices and proven architectural principles.
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Key Players:
➤ IBM Corporation
➤ GAVS Technologies
➤ Amazon Web Services
➤ Databricks
➤ DataRobot
➤ Microsoft Corporation
➤ Cloudera
➤ Akira AI
➤ Alteryx
➤ Google LLC
➤ Others
Market Analysis
Standardizing ML processes for effective teamwork is one of the key factors fueling MLOps' growth. Manual data reprocessing and collection prove inefficient and error-prone, whereas MLOps automates the entire ML workflow, enhancing efficiency, reducing error rates, and fostering collaboration among varied stakeholders.
The upsurge in demand for ML/AI-based projects, coupled with the surge in digital and internet penetration worldwide, is driving market growth. Additionally, increased adoption of MLOps across enterprises to augment operations and productivity is a significant contributing factor. The burgeoning investments in the healthcare sector are poised to offer lucrative growth opportunities during the forecast period.
Segment Analysis:
➤ By Component: The platform segment dominates due to its adoption in various enterprises, boosting productivity and reducing costs significantly.
➤ By Deployment: On-premises deployment leads due to its ensured data safety and security, granting enterprises greater control over their ML pipelines.
➤ By Organization Size: Large enterprises spearhead the market, leveraging MLOps to enhance scalability, reliability, and innovation in their operations.
➤ By End-use Vertical: The BFSI sector leads the charge, utilizing MLOps for automation and enhanced client experiences. The government and healthcare sectors also exhibit substantial growth potential.
Market Segmentation and Sub-Segmentation Included Are:
By Deployment
➤ On-premise
➤ Cloud
By Enterprise Size
➤ Large
➤ Small & Medium
By Training Delivery Mode
➤ E-Learning Platforms
➤ Virtual Instructor-Led Training
➤ Blended Learning
By End-Use
➤ BFSI
➤ Automotive
➤ Retail
➤ Healthcare
➤ IT
➤ Telecom
➤ Discrete Manufacturing
➤ Government
➤ Education
➤ Others
Key Regional Development:
North America: Leading the MLOps market share, the US and Canada's adoption of ML technology across multiple sectors fuels regional growth. The presence of major tech providers and emerging startups fortifies this region's outlook.
Asia Pacific: Expected to register the fastest CAGR, APAC benefits from a booming cloud computing market and heavy investments in AI and machine learning, driving substantial demand for MLOps solutions.
Key Takeaways
➤ Enhanced Operational Efficiency: MLOps, integrating DevOps with machine learning, revolutionizes workflows, ensuring swift model deployment and minimizing errors, thereby streamlining operations and boosting productivity across industries.
➤ Market Expansion and Diverse Adoption: The MLOps market exhibits robust growth driven by its adoption across varied sectors like finance, healthcare, and government. Its ability to scale operations and enhance client experiences fuels its widespread implementation.
➤ Regional Dynamics: North America leads the MLOps market share, driven by tech giants and burgeoning startups. Meanwhile, the Asia Pacific region shows exceptional potential with its escalating investments in AI, cloud computing, and machine learning.
➤ Technological Innovation: The market witnesses constant innovation, evident in collaborations and strategic alliances aiming to revolutionize data center infrastructure and advance AI technologies for real-world applications.
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Recent Developments:
In June 2022, DataRobot introduced an advanced AI Cloud with heightened business impact, amplifying its capabilities for comprehensive and impactful data analytics solutions.
GAVS Technologies partnered with NTT Ltd. in May 2022, integrating the ZIF AIOps platform into NTT Ltd.'s Infrastructure Managed Services, fortifying their joint offering with enhanced AI-driven operational efficiency.
HPE and Ayar Labs announced in February 2022 a multi-year strategic collaboration, focusing on data center innovation through silicon photonics solutions based on optical I/O technology, aiming to revolutionize data transmission and processing.
DataRobot collaborated with Foxtons, a prominent London-based estate agency, in September 2021, augmenting their data analytics and modeling capabilities to make more strategic, data-driven business decisions and better serve consumers.
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Table of Contents
1. Introduction
2. Research Methodology
3. Market Dynamics
4. Impact Analysis
4.1. COVID-19 Impact Analysis
4.2. Impact of Ukraine- Russia war
4.3. Impact of Ongoing Recession on Major Economies
5. Value Chain Analysis
6. Porter's 5 Forces Model
7. PEST Analysis
8. MLOps Market Segmentation, by Component
9. MLOps Market Segmentation, by Deployment
10. MLOps Market Segmentation, by Organization Size
11. MLOps Market Segmentation, by End-Use Vertical
12. Regional Analysis
13. Company Profile
14. Competitive Landscape
15. USE Cases and Best Practices
16. Conclusion
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