End-to-End MLOps Platform Practice Using AWS SageMaker and vLLM

This post introduces an open-source end-to-end MLOps platform practice project—thilakakula13/mlops-sagemaker-vllm-platform. The project combines AWS SageMaker Pipelines (model lifecycle orchestration) and vLLM (high-performance inference service) to address core MLOps challenges in the era of large models, achieving two key outcomes: a 60% reduction in MLOps cycle time and P99 inference latency below 200ms. The following floors will elaborate on dimensions such as background, architecture, optimization, and applications.

With the widespread adoption of Large Language Models (LLMs) in enterprises, MLOps faces three core challenges: 1. Complex deployment due to large model sizes; 2. Strict requirements for inference latency; 3. Need to redesign version management and rollback strategies. Traditional MLOps toolchains are mostly designed for small models and struggle to adapt to the special needs of LLMs. This project, based on the AWS cloud-native environment, provides a complete LLM MLOps pipeline solution.

Core Architecture Components

1. AWS SageMaker Pipelines:

   • Pipeline Orchestration: Defines a complete chain of steps for data preprocessing, training, evaluation, and deployment, supporting conditional branches (e.g., deployment only if metrics meet standards);
   • Experiment Tracking: Integrates with SageMaker Experiments to automatically record hyperparameters, metrics, and artifacts, forming a traceable model lineage;
   • Model Registration: Trained models are automatically registered to the Model Registry, supporting version management and approval workflows;
   • Event-Driven: Uses EventBridge to implement automatic notifications for model state changes and downstream triggers.

2. vLLM Inference Engine:

   • PagedAttention Optimization: KV cache paging management to improve GPU memory utilization and concurrent throughput;
   • Continuous Batching: Dynamic batching of requests to reduce tail latency;
   • Quantization Support: Compatible with schemes like AWQ and GPTQ to balance model quality and speed;
   • OpenAI-Compatible API: Facilitates migration of existing applications.

Project Structure

The code is divided into two main directories: pipeline/ (pipeline definitions: data processing, training, evaluation, deployment rules) and serving/ (inference service configuration: container images, endpoint settings, auto-scaling), implementing the best practice of independent evolution of training and inference.

Key Optimization Measures

1. Training Phase: Distributed training (data/model parallelism), intelligent checkpoint strategy (to avoid progress loss), hyperparameter tuning (integrated with SageMaker Hyperparameter Tuner);
2. Deployment Phase: Blue-green deployment (zero-downtime switch), vLLM inference optimizations (PagedAttention, continuous batching, CUDA graphs), auto-scaling (based on GPU utilization and request queue depth);
3. Monitoring: CloudWatch metrics (latency, throughput, error rate), model drift detection, cost tracking (statistics of training/inference costs by version).

Outcome Verification

• 60% reduction in MLOps cycle time: Significantly reduced time from training to deployment;
• P99 inference latency below 200ms: Meets response speed requirements for production environments.

Application Scenarios

• Internal Enterprise LLM Services: Provide unified hosting and inference services for multiple business lines;
• Model-as-a-Service (MaaS): Offer external APIs with pay-as-you-go billing and quota management;
• Multi-Tenant Environments: Achieve resource isolation and cost sharing via Multi-Model Endpoints;
• Rapid Experiment Iteration: Data scientists focus on model development, while the platform automatically handles deployment and scaling.

Solution Comparison

Feature	This Project	Self-built K8s + vLLM	Pure SageMaker
Orchestration Capability	Strong (SageMaker Pipelines)	Requires self-building (Kubeflow, etc.)	Medium
Inference Performance	High (vLLM optimized)	High	Medium
Operational Complexity	Low (managed service)	High	Low
Cost Control	Flexible (hybrid use)	Flexible	Relatively high
Vendor Lock-in	Partial (AWS)	None	Full

This solution balances performance, ease of use, and flexibility. It leverages AWS managed services to reduce operational burden while gaining cutting-edge optimizations through vLLM.

Deployment Steps

1. Environment Preparation: Configure AWS CLI and SageMaker permissions;
2. Pipeline Deployment: Run the pipeline/ script to create a SageMaker Pipeline;
3. Model Training: Trigger the pipeline to execute training jobs;
4. Inference Service Deployment: Use the serving/ configuration to create a SageMaker endpoint;
5. Client Integration: Call the inference service via HTTP/REST API.

Summary & Outlook

This project demonstrates a practical implementation path for LLM MLOps: combining mature cloud-native tools (SageMaker) with high-performance open-source components (vLLM) to solve real-world problems. For enterprise teams, it provides referenceable code structures, optimization strategies, and implementation paths. In the future, the project can be further enhanced through community contributions: integrating TensorRT-LLM/DeepSpeed Inference, supporting multimodal models, improving security governance capabilities, etc.