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# LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices

Struggling to move generative AI from experimental notebooks to scalable production? Discover how NVIDIA NIM and NeMo Microservices unify fine-tuning, evaluation, and inference into an automated LLMOps pipeline.

- **Speakers:** [Anshul Jindal](https://www.wearedevelopers.com/@anshul-jindal)
- **Event:** World Congress 2025
- **Published:** August 20, 2025
- **Duration:** 29:20
- **URL:** https://www.wearedevelopers.com/videos/1582-llmops-driven-fine-tuning-evaluation-and-inference-with-nvidia-nim-nemo-microservices

## Summary

Moving generative AI from experimental Jupyter notebooks to scalable production environments frequently falls victim to the "transparent wall" between AI engineers and DevOps teams. Disconnected stages, disparate tooling, and a lack of standard CI/CD practices for large language models make enterprise deployment unpredictable. To bridge this gap, organizations must adopt an LLMOps-driven approach that treats data processing, model customization, alignment, and network inference as a unified, automated cycle. Implementing robust versioning and iterative feedback loops ensures models seamlessly adapt to changing proprietary data while maintaining expected alignment and behavioral guardrails.

NVIDIA’s containerized ecosystem—composed of NeMo Microservices and NVIDIA NIM—provides the architectural backbone for this orchestration. The NeMo Customizer allows teams to embed domain-specific knowledge into foundation models via streamlined API requests, while the NeMo Evaluator continuously benchmarks those fine-tuned adapters using rapid "LLM as a judge" evaluations. At the deployment tier, NVIDIA Inference Microservice (NIM) containers dynamically pull and provision the most uniquely optimized model variant depending on the underlying GPU hardware. For true enterprise scalability, the NIM Operator acts as a custom Kubernetes controller to autoscale inference endpoints, intelligently caching dynamic, task-specific adapters into GPU memory for parallel query routing.

To confidently operationalize this stack, infrastructure orchestrations must rely on established GitOps methodologies instead of localized manual scripts. By utilizing Argo CD to enforce Git as the single source of truth, DevOps teams can declaratively manage cluster configurations and application states globally. Intersecting with these configurations, Argo Workflows sequences the multi-stage data graphs—orchestrating tasks from pulling dataset inputs and tracking validation metrics in MLflow, to managing human-in-the-loop testing approvals prior to final deployment. This architectural alignment abstracts complex infrastructure barriers, empowering AI engineers to execute end-to-end training and inference via accessible UIs while maintaining rigid deployment governance.

**Keywords:** llmops deployment pipelines, nvidia nim containers, nemo microservices architecture, foundation model fine-tuning, nemo customizer integration, nemo evaluator pipelines, generative ai production deployments, llm as a judge benchmarking, kubernetes nim operator, gitops for ai engineering, argo cd kubernetes deployments, argo workflows multi-stage dags, mlflow model metric tracking, gpu hardware optimization inference, dynamic adapter caching, proprietary data alignment, human-in-the-loop ml loops

## Chapters

1. **The generative AI application lifecycle** (00:00) — How building data curation and model training tasks into loops enables continuous generative AI application updates.
1. **Solving application deployment complexities using LLMOps pipelines** (04:42) — Overcoming disconnected deployment stages involves operationalizing the machine learning pipeline from proof-of-concept to production.
1. **Mapping the developer pipeline with NVIDIA microservices** (08:38) — Integrating specialized microservice containers directly addresses the complexity of application customization and evaluation tasks.
1. **Integrating infrastructure enablers for LLMOps operations ecosystems** (10:26) — Combining data storage and operational workflow tools establishes a resilient automation foundation for language models.
1. **Executing fine-tuning and LLM evaluation API workflows** (12:29) — Pulling foundation models and datasets via API requests enables automated container training and adapter benchmarking.
1. **Scaling customized inference models with NVIDIA NIM** (17:27) — Using specialized Kubernetes operators to deploy optimized containers scales customized adapter loading into hardware cache.
1. **Automating discrete component pipelines using Argo Workflows** (20:37) — Writing deterministic component templates stitches discrete deployment operations into reproducible pre-production software workflows.
1. **Managing cluster platform infrastructure via GitOps principles** (23:30) — Establishing version control as the core infrastructure truth aligns application development with Kubernetes cluster operations.
1. **Demonstrating an integrated LLMOps cluster deployment environment** (26:31) — Orchestrating cluster synchronization automatically triggers pipeline sequences and centralizes model training metric visualization.

## Related Moments

- [Automating model lifecycle management with the NIM operator](https://www.wearedevelopers.com/videos/1170-from-foundation-model-to-hosted-ai-solution-in-minutes) (from "From foundation model to hosted AI solution in minutes")
- [Defining MLOps and its role in production systems](https://www.wearedevelopers.com/videos/825-mlops-on-kubernetes-exploring-argo-workflows) (from "MLOps on Kubernetes: Exploring Argo Workflows")
- [Building and fine-tuning models with the NeMo framework](https://www.wearedevelopers.com/videos/100148-ai-that-fits-your-business-not-the-other-way-around) (from "AI That Fits Your Business, Not the Other Way Around")
- [Introduction to DevOps for AI and MLOps](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) (from "DevOps for AI: running LLMs in production with Kubernetes and KubeFlow")
- [Centralizing LLMOps workflows within Azure AI Foundry](https://www.wearedevelopers.com/videos/1250-from-traction-to-production-maturing-your-llmops-step-by-step) (from "From Traction to Production: Maturing your LLMOps step by step")
- [Optimizing and deploying containerized AI inference workloads](https://www.wearedevelopers.com/videos/920-wwc24-ankit-patel-unlocking-the-future-breakthrough-application-performance-and-capabilities-with-nvidia) (from "WWC24 - Ankit Patel - Unlocking the Future Breakthrough Application Performance and Capabilities with NVIDIA")

## Related Articles

- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
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