Design an AWS fine-tuning platform for LLMs

Q: Design an AWS fine-tuning platform for LLMs

This question evaluates a candidate's competency in designing cloud-native ML platforms for fine-tuning large language models, covering distributed training, multi-tenant security and isolation, cost controls, reproducibility, observability, checkpointing, and deployment workflows.

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Question

Scenario

You need to build a system that lets customers fine-tune their own large language model (LLM) on AWS.

Task

Design a managed platform where users can:

Upload datasets (text + optional instruction/response pairs).
Choose a base model and fine-tuning method (full FT, LoRA/QLoRA, adapters).
Launch training jobs, monitor progress, and evaluate results.
Deploy the resulting model behind an inference endpoint.

Constraints / Requirements

Multi-tenant isolation and security (VPC, IAM, encryption).
Cost controls and quotas.
Support for distributed training and checkpointing.
Reproducibility (job config, code versioning, dataset versioning).
Observability (metrics, logs, traces) and failure recovery.

Deliverables

Architecture, key APIs, training workflow, data management, and evaluation strategy.

Design an AWS fine-tuning platform for LLMs

Scenario

Task

Constraints / Requirements

Deliverables

Solution

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Design an AWS fine-tuning platform for LLMs

Overview

Scenario

Task

Constraints / Requirements

Deliverables

Solution

Comments (0)