Jim leads the technical innovation and design of the ModelOp Center platform. He is also integral to advising ModelOp customer CIOs and CTOs on requirements to better support their IT operations as they execute on digital business strategies that often strain technology infrastructure.
Businesses are always looking for ways to upgrade their operations, and Agentic AI is poised to help. Autonomous agents that collaborate to achieve complex goals demand specialized AI models. Despite their recent popularity (e.g., ChatGPT reached 100 million users in two months when it launched in November 2022), Large Language Models (LLMs) have inherent limitations that are becoming increasingly apparent for highly regulated industries like healthcare and finance. Small Language Models (SLMs) and model distillation are emerging as safer, more cost-effective alternatives.
What is Agentic AI?
Agentic AI is artificial intelligence that acts autonomously, using agents to perform tasks, make decisions, and achieve specific goals. Agentic AI can be characterized by:
- Goal-oriented behavior with minimal human intervention
- Autonomous decision-making to analyze data, plan actions, and adapt to changing conditions
- Simulated human-like reasoning that helps solve complex problems and tasks
- Expert model usage to utilize expert models within agents to achieve their tasks
The Challenges of LLMs in Agentic AI
Even though LLMs have vast knowledge, their application in Agentic AI presents some challenges:
- Resource-intensive: LLMs require substantial computational resources, making them expensive to run and self-host.
- Data privacy concerns: Training and deploying LLMs often involve sending sensitive data to external vendors.
- Reinforcement Learning complexities: Refining LLMs for specific domains through reinforcement learning is time-consuming and costly.
- Generalization vs. Specialization: Agentic AI requires experts in specific domains, and LLMs are general and require costly fine-tuning.
The Case for SLMs and Model Distillation
Small Language Models are designed with fewer but more specific parameters, which offers a compelling alternative to LLMs. SLMs are more computationally efficient, requiring less memory, storage, and processing power. Plus, they're tailored to particular domains, making them ideal for Agentic AI agents. Most interestingly, they are more cost-effective because they can run locally with off-the-shelf hardware or a desktop GPU.
Model Distillation: Creating Specialized SLMs
Model distillation is a powerful technique that leverages the knowledge of a large, pre-trained LLM to teach an SLM. It's a great way to condense the information from a large model to avoid bringing unnecessary data into the smaller model. Instead, it brings only the areas of expertise you need for the use case into the distilled, smaller model. It should be noted that the larger model must already contain the knowledge you seek to distill for the SLM, but model distillation is still a great technique that comes at a lower cost due to less GPU time needed to train the model. This automated form of supervised learning involves:
- Teacher-student paradigm: The LLM (teacher) provides "soft targets" to the SLM (student).
- Loss model optimization: The SLM learns to predict these targets, minimizing deviations using a loss model.
- Knowledge condensation: Specialized knowledge is efficiently transferred and compressed into the SLM.
Use Cases for Model Distillation
Model distillation enables the creation of highly specialized SLMs for diverse applications:
- Healthcare: Distilled SLMs can analyze medical images, predict patient outcomes, or personalize treatment plans, running efficiently on edge devices in hospitals that avoid sending sensitive patient data to large cloud-based LLMs.
- Financial Services: SLMs can detect fraudulent transactions, assess credit risk, or provide personalized financial advice, all while adhering to stringent regulatory requirements.
- Automated Customer Support: Agentic AI architectures can employ multiple distilled SLMs, each specialized in a specific area (e.g., product information, troubleshooting), to provide comprehensive and efficient customer support.
- Industrial Automation: SLMs can monitor equipment performance, predict maintenance needs, or optimize production processes, enabling real-time decision-making on the factory floor.
Risk Mitigation: Why ModelOp Is Your Partner in Agentic AI Governance
Leveraging SLMs in Agentic AI can help with risk mitigation by reducing data privacy concerns since local deployment minimizes the need to send sensitive data to external vendors. Specialized SLMs are also more straightforward to monitor and govern, making it easier to identify all your use cases and dynamically catalog model information.
As Agentic AI proliferates, scalable AI governance is crucial. ModelOp provides comprehensive solutions to:
- Inventory: Understand where your AI and SLMs are deployed
- Controls: Establish approval workflows, maintain a history of distillation, and ensure regulatory compliance
- Reporting: Gain insights into all your AI initiatives through intuitive dashboards
- Integrations: Get more out of your existing tech investments
Are you ready for the Future of Model Governance?
Learn more about how enterprises are adopting modern governance strategies to manage AI models, ensure visibility across autonomous AI systems, and scale responsibly—without compromising speed-to-market.
Click here to request a demo of ModelOp and discover how we can help you unlock the full potential of Agentic AI.
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