NVIDIA and Outerbounds innovate LLM-based production systems

Language models have expanded rapidly over the past 18 months, with hundreds of variants now available, including large language models (LLMs), small language models (SLMs), and domain-specific models. Many of these models are freely accessible for commercial use, making them cheaper and simpler to fine-tune using custom datasets, according to the NVIDIA Technology Blog.

Building LLM-Based Enterprise Applications with NVIDIA NIM

NVIDIA NIM provides containers that self-host GPU-accelerated microservices for pre-trained and custom AI models. Outerbounds, born at Netflix, is an MLOps and AI platform based on the open source framework Metaflow. This allows LLM and the systems built around it to be managed efficiently and safely.

NVIDIA NIM provides a variety of prepackaged and optimized community-created LLMs that can be deployed in private environments, mitigating security and data governance concerns by avoiding third-party services. Since its launch, Outerbounds has been helping companies develop LLM-based enterprise applications and securely deploy them across cloud and on-premises resources by integrating NIM into the platform.

The term LLMOps emerged to describe a method of managing large-scale language model dependencies and operations, while MLOps covers a wider range of tasks related to supervising machine learning models in a variety of domains.

Step 1: LLM-supported system development

The first step involves setting up a productive development environment for rapid iteration and experimentation. NVIDIA NIM microservices provide optimized LLM that can be deployed in secure, private environments. This phase includes fine-tuning the model, building a workflow, and testing with real data while ensuring data control and maximizing LLM performance.

Outerbounds helps you deploy development environments within your company’s cloud account using your existing data governance rules and boundaries. NIM exposes an OpenAI-compatible API, allowing developers to use off-the-shelf frameworks to reach private endpoints. Metaflow allows developers to create end-to-end workflows that integrate NIM microservices.

Phase 2: Continuous improvement of the LLM system

To ensure consistent and continuous improvement, your development environment requires appropriate version control, tracking, and monitoring. Metaflow’s built-in artifacts and tags help promote collaboration across developer teams by tracking prompts, responses, and models used. Treating the LLM as a core dependency of the system ensures stability as the model evolves.

Deploying NIM microservices in a controlled environment allows you to reliably manage the model lifecycle and associate prompts and assessments with the correct model version. Monitoring tools like Metaflow cards allow you to visualize important metrics to keep an eye on your system and troubleshoot performance issues immediately.

Step 3: CI/CD and production rollout

Incorporating continuous integration and continuous delivery approaches ensures a smooth production rollout of LLM-based systems. Automated pipelines enable continuous improvements and updates while maintaining system stability. Progressive deployment and A/B testing help manage the complexity of LLM systems in real-world environments.

Integrating compute resources while decoupling business logic and models helps maintain reliable and highly available production deployments. Shared compute pools across development and production increase utilization and lower the cost of valuable GPU resources. Metaflow event triggering integrates LLM-based systems with upstream data sources and downstream systems to ensure compatibility and reliability.

conclusion

Systems powered by LLMs should be approached like any other large-scale software system, with a focus on resilience and continuous improvement. NVIDIA NIM provides LLM as a standard container image, enabling reliable and secure production systems without sacrificing speed of innovation. By leveraging software engineering best practices, organizations can build robust LLM-based applications that adapt to changing business needs.

Image source: Shutterstock