With the rapid expansion of language models over the past 18 months, hundreds of variants are now available. These include large language models (LLMs)…
Overview
The article discusses the integration of large language models (LLMs) into enterprise applications using NVIDIA NIM and Outerbounds, emphasizing the importance of secure deployment, continuous improvement, and CI/CD practices. It outlines best practices for developing, deploying, and managing LLM-powered systems while addressing challenges related to data governance and model management.
What You'll Learn
1
How to deploy LLMs securely in your own cloud environment
2
Why continuous improvement practices are essential for LLM systems
3
How to implement CI/CD pipelines for LLM-powered applications
Prerequisites & Requirements
- Understanding of machine learning infrastructure and deployment practices
- Familiarity with NVIDIA NIM and Outerbounds platforms(optional)
Key Questions Answered
How can enterprises securely deploy LLMs?
Enterprises can securely deploy LLMs by using NVIDIA NIM to self-host GPU-accelerated microservices within their own cloud environments. This approach mitigates security and data governance concerns by avoiding third-party services and allows for compliance with existing data governance rules.
What are the best practices for developing LLM-powered applications?
Best practices for developing LLM-powered applications include establishing productive development environments, ensuring collaboration and continuous improvement, and implementing robust production deployments. These practices help teams iterate on models efficiently while maintaining stability and performance.
What is LLMOps and how does it differ from MLOps?
LLMOps refers to the management of large language model dependencies and operations, focusing specifically on the challenges posed by LLMs. In contrast, MLOps encompasses a broader range of tasks related to overseeing machine learning models across various domains and applications.
Key Statistics & Figures
Input tokens processed
230 million
Outerbounds processed this amount in about 9 hours using a LLama 3 70B model with five concurrent worker tasks.
NVIDIA GPUs used
4 NVIDIA H100 Tensor Core GPUs
These GPUs were utilized to run the LLama 3 model during the processing of input tokens.
Technologies & Tools
Backend
Nvidia Nim
Used for deploying GPU-accelerated microservices for LLMs.
Mlops Platform
Outerbounds
Facilitates the development and deployment of LLM-powered applications.
Data Science Framework
Metaflow
Used for developing, deploying, and operating data-intensive applications involving LLMs.
Key Actionable Insights
1Leverage NVIDIA NIM microservices to create isolated development environments for LLMs.This allows developers to experiment and fine-tune models without risking interference from other projects, ultimately increasing development velocity and efficiency.
2Implement GitOps practices to ensure version control and continuous improvement in LLM systems.By tracking changes in code, data, and models, teams can maintain stability while iterating on their applications, which is crucial for adapting to the rapid evolution of LLM technologies.
3Utilize Parameter-Efficient Fine Tuning (PEFT) techniques to customize LLMs with minimal computational resources.This approach allows developers to fine-tune models effectively without the need for extensive compute resources, making it easier to adapt LLMs to specific use cases.
Common Pitfalls
1
Neglecting to treat LLMs as core dependencies can lead to system instability.
As LLMs evolve rapidly, failing to manage their versions and dependencies can cause applications to break unexpectedly. It's essential to maintain strict version control and monitor changes in LLMs to ensure compatibility.
Related Concepts
Mlops Best Practices
Continuous Integration And Delivery
Data Governance In AI Applications