Revolutionizing AI Deployment: The Shift from MLOps to FMOps

Machine learning operations (MLOps) have transformed the way organisations deploy machine learning models. It streamlines the entire machine learning development lifecycle, fostering collaboration and efficiency. However, as foundation models (FMs) become increasingly vital for unlocking new AI capabilities, a new discipline is emerging: foundation model ops (FMOps)

What are foundation models?

Foundation models (FMs) are AI models pre-trained on vast datasets to learn general world knowledge. This allows them to be adaptable across modalities like language, speech, and vision to perform various downstream tasks through further training.

Unlike traditional ML models built for narrow capabilities, FMs are versatile building blocks for many AI applications today and in the future due to their size and breadth of knowledge.

Key Differences Between MLOps and FMOps

While MLOps focuses on deploying individual ML models, FMOps deals with orchestrating entire AI systems utilizing one or more foundation models.

• Data: FMOps require significantly more data and compute to train and run FMs, whereas MLOps handle relatively smaller datasets.
• Generalization: MLOps trains models for specialized tasks, while FMOps leverages general knowledge models.
• Orchestration: MLOps manages single model deployment, while FMOps coordinates multiple FMs and components.
• Monitoring: FMOps require more advanced monitoring for fairness, toxicity, etc. due to the open-ended nature of FMs.
• Adaptability: Rapid FM innovation requires more experimentation, evaluation, and integration of new model versions in FMOps.

Core FMOps Capabilities

As FMs proliferate, FMOs seek to provide:

• End-to-end orchestration of workflows using diverse FMs
• Tools to support rapid FM development, testing, and deployment
• Advanced monitoring for transparency, fairness, and robustness
• Efficient inference delivery to optimize cost and latency
• Governance for responsible AI across expanding applications

The Responsibilities of FMOs

FMOps aims to:

• Enable builders to efficiently harness FMs
• Provide guardrails for accountability
• Balance innovation with governance as FMs integrate into business products, services, and workflows

Why FMOs are Critical for AI Success

The rise of FMs like Claude, DALL-E, etc. can revolutionize AI leverage. However, their scale and complexity require FMOs to fulfill their potential by:

• Coordinating entire AI systems and tapping into diverse FMs
• Enabling enterprise-grade performance and governance
• Bringing DevOps-style agility and rapid integration of new FM versions

In summary, FMOps promises to be the command center for empowering builders while providing ethical guardrails as generative AI grows. It balances exponential capabilities with oversight for real-world AI impact.

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