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Industry Landscape
The MLOps industry is rapidly expanding, driven by the increasing need to operationalize AI/ML models at scale. It focuses on automating and streamlining the ML lifecycle, from data preparation to model deployment and monitoring. Key trends include the rise of feature stores for consistent data, enhanced tools for LLMs, and a growing emphasis on real-time capabilities and governance. The sector is characterized by intense competition and continuous innovation to address the complexities of production AI.
Total Assets Under Management (AUM)
MLOps Platform Market Size in United States
~Approx. 2.1 billion USD (2023)
(39.1% CAGR)
- Driven by increased AI/ML adoption.
- Focus on operationalizing models.
- Need for automation and governance.
Total Addressable Market
Approx. 2.1 billion
Market Growth Stage
Pace of Market Growth
Emerging Technologies
Generative AI & LLMs
The rapid advancement of Generative AI and Large Language Models (LLMs) is pushing the boundaries of AI applications, requiring new ways to manage and serve features for these complex models.
Real-time MLOps
The increasing demand for immediate insights and predictions necessitates robust real-time MLOps capabilities, including low-latency feature serving and continuous model retraining.
MLOps Governance & Explainability
Growing regulatory scrutiny and the need for trustworthy AI are driving the development of tools for enhanced MLOps governance, lineage tracking, and model explainability.
Impactful Policy Frameworks
AI Risk Management Framework (AI RMF) 1.0 (2023)
Published by NIST, the AI RMF 1.0 provides voluntary guidance to better manage risks to individuals, organizations, and society associated with artificial intelligence (AI).
This framework encourages MLOps platforms to incorporate better risk assessment, transparency, and explainability features, potentially influencing future product development and compliance requirements for Feast's enterprise clients.
Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (2023)
This U.S. Executive Order directs federal agencies to establish new standards for AI safety and security, protect privacy, and promote innovation and competition.
The order's emphasis on data privacy, security, and responsible AI development will push MLOps providers like Feast to prioritize robust data governance, access controls, and auditing capabilities to help users comply with emerging federal guidelines.
California Consumer Privacy Act (CCPA) and California Privacy Rights Act (CPRA) (2018/2020)
These California laws grant consumers greater control over their personal information and establish strict requirements for businesses regarding data collection, use, and sharing.
As a feature store handling user data, Feast and its users must ensure that feature creation and serving pipelines comply with data minimization, purpose limitation, and individual rights requirements under these privacy regulations, especially for features derived from personal data.
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