Building MLOps Infrastructure for Modern AI Applications

Introduction: The New Era of AI Operations The AI landscape has evolved dramatically with the rise of large language models (LLMs), retrieval-augmented generation (RAG), and multimodal AI systems. Traditional MLOps frameworks struggle to handle: Billion-parameter LLMs with unique serving requirements Vector databases that power semantic search GPU resource management for cost-effective scaling Prompt engineering workflows that require version control Embedding pipelines that process millions of documents In this article I will be providing a blueprint on different development tools for different components of building an AI/MLOps infrastructure capable which supports the recent advanced AI applications. Core Components of AI-Focused MLOps LLM Lifecycle Management Vector Database & Embedding Infrastructure GPU Resource Management Prompt Engineering Workflows API Services for AI Models 1. LLM Lifecycle Management a) Tooling Stack: Model Hubs: Hugging Face, Replicate Fine-tuning: Axolotl, Unsloth, TRL Serving: vLLM, Text Generation Inference (TGI) Orchestration: LangChain, LlamaIndex b) Key Considerations: Version control for adapter weights (LoRA/QLoRA) A/B testing frameworks for model variants GPU quota management across teams 2. Vector Database & Embedding Infrastructure Database Choice Pinecone Weaviate Milvus PGVector QDrant Embedding Pipeline Best Practices: Chunk documents with overlap (512-1024 tokens) Batch process with SentenceTransformers Monitor embedding drift with Evidently AI 3. GPU Resource Management Deployment Patterns: Approach Use Case Tools Dedicated Hosts Stable workloads NVIDIA DGX Kubernetes Dynamic scaling K8s Device Plugins Serverless Bursty traffic Modal, Banana Spot Instances Cost-sensitive AWS EC2 Spot Optimization Techniques: Quantization (GPTQ, AWQ) Continuous batching (vLLM) FlashAttention for memory efficiency 4. Prompt Engineering Workflows MLOps Integration: Version prompts alongside models (Weights & Biases) Test prompts with Ragas evaluation framework Implement canary deployments for prompt changes 5. API Services for AI Models Production Patterns: Framework Latency Best For FastAPI

Apr 17, 2025 - 04:23

Building MLOps Infrastructure for Modern AI Applications

Introduction: The New Era of AI Operations

The AI landscape has evolved dramatically with the rise of large language models (LLMs), retrieval-augmented generation (RAG), and multimodal AI systems. Traditional MLOps frameworks struggle to handle:

Billion-parameter LLMs with unique serving requirements
Vector databases that power semantic search
GPU resource management for cost-effective scaling
Prompt engineering workflows that require version control
Embedding pipelines that process millions of documents

In this article I will be providing a blueprint on different development tools for different components of building an AI/MLOps infrastructure capable which supports the recent advanced AI applications.

Core Components of AI-Focused MLOps

LLM Lifecycle Management
Vector Database & Embedding Infrastructure
GPU Resource Management
Prompt Engineering Workflows
API Services for AI Models

1. LLM Lifecycle Management

a) Tooling Stack:

Model Hubs: Hugging Face, Replicate
Fine-tuning: Axolotl, Unsloth, TRL
Serving: vLLM, Text Generation Inference (TGI)
Orchestration: LangChain, LlamaIndex

b) Key Considerations:

Version control for adapter weights (LoRA/QLoRA)
A/B testing frameworks for model variants
GPU quota management across teams

2. Vector Database & Embedding Infrastructure

Database Choice

Pinecone
Weaviate
Milvus
PGVector
QDrant

Embedding Pipeline Best Practices:

Chunk documents with overlap (512-1024 tokens)
Batch process with SentenceTransformers
Monitor embedding drift with Evidently AI

3. GPU Resource Management

Deployment Patterns:

Approach	Use Case	Tools
Dedicated Hosts	Stable workloads	NVIDIA DGX
Kubernetes	Dynamic scaling	K8s Device Plugins
Serverless	Bursty traffic	Modal, Banana
Spot Instances	Cost-sensitive	AWS EC2 Spot

Optimization Techniques:

Quantization (GPTQ, AWQ)
Continuous batching (vLLM)
FlashAttention for memory efficiency

4. Prompt Engineering Workflows

MLOps Integration:

Version prompts alongside models (Weights & Biases)
Test prompts with Ragas evaluation framework
Implement canary deployments for prompt changes

5. API Services for AI Models

Production Patterns:

Framework	Latency	Best For
FastAPI	<50ms	Python services
Triton	<10ms	Multi-framework
BentoML	Medium	Model packaging
Ray Serve	Scalable	Distributed workloads

Essential Features:

Automatic scaling
Request batching
Token-based rate limiting

End-to-End Reference Architecture

Below will be the whole infrastructure diagram for an AIOps Infrastructure, feel free to take a pause to go over as it could be overwhelming :)

Final Takeaways

Quick lessons for production,

Separate compute planes for training vs inference
Implement GPU-aware autoscaling
Treat prompts as production artifacts
Monitor both accuracy and infrastructure metrics

This infrastructure approach enables organizations to deploy AI applications that are:

Scalable (handle 100x traffic spikes)
Cost-effective (optimize GPU utilization)
Maintainable (full lifecycle tracking)
Observable (end-to-end monitoring)

Thanks for reading— I hope this guide helps you tackle those late-night MLOps fires with a bit more confidence. If you’ve battled AI infrastructure quirks at your own, I’d love to hear your war your solutions! :)