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Industry Landscape
The MLOps industry is experiencing rapid growth, driven by the increasing adoption of AI/ML in enterprises. It focuses on streamlining the deployment, monitoring, and management of ML models in production, addressing challenges like reproducibility, scalability, and operational efficiency. The industry is evolving with new tools and best practices to bridge the gap between ML development and reliable production systems.
Total Assets Under Management (AUM)
MLOps Platform Market Size in United States
~Cannot find specific value for US market, but global MLOps market size was valued at 1.4 Billion USD in 2022.
(39.4% CAGR)
The MLOps market is expanding rapidly.
- Driven by enterprise AI adoption.
- Focus on operational efficiency and scalability.
- Global market expected to grow significantly.
Total Addressable Market
11.1 billion USD
Market Growth Stage
Pace of Market Growth
Emerging Technologies
Foundation Models/Large Language Models (LLMs) in MLOps
The integration of LLMs and foundation models will automate and streamline various MLOps tasks, from code generation to automated model documentation and intelligent monitoring.
Responsible AI/AI Governance Platforms
These platforms will provide tools for fairness, explainability, privacy, and security in ML models, becoming crucial for compliance and ethical AI deployment.
MLOps on Edge Devices
Deploying and managing ML models directly on edge devices will enable real-time inference and reduce latency for applications in IoT, manufacturing, and autonomous systems.
Impactful Policy Frameworks
AI Bill of Rights (2022)
The White House Office of Science and Technology Policy released the 'Blueprint for an AI Bill of Rights' in October 2022, outlining five principles to guide the design, use, and deployment of automated systems.
This blueprint encourages the development of responsible AI practices within MLOps platforms, impacting features related to fairness, transparency, and accountability.
NIST AI Risk Management Framework (AI RMF 1.0) (2023)
Published in January 2023, this voluntary framework from the National Institute of Standards and Technology provides guidance to manage risks associated with AI systems, focusing on trustworthiness.
The NIST AI RMF will influence MLOps platforms to incorporate robust risk assessment, governance, and auditing features to help users comply with best practices.
California Consumer Privacy Act (CCPA) - as amended by CPRA (2020/2023)
The CCPA (2020), as amended by the California Privacy Rights Act (CPRA, effective 2023), grants consumers more control over their personal information and introduces requirements for businesses handling data, including data used in AI/ML.
This legislation necessitates MLOps platforms to offer features that support data privacy, data anonymization, and robust data governance for model training and deployment within the US market.
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