Course Objective & Scope
When teams need to train custom tabular or vision models, fine-tune open-weight LLMs on proprietary data or run high-throughput, cost-optimized inference beyond managed APIs, they need full platform control. This programme teaches how to manage the complete model lifecycle using Amazon SageMaker AI and companion AWS AI services.
SageMaker Studio / Unified Studio workspaces, scalable data prep with Processing jobs, distributed training and PEFT/LoRA fine-tuning, deploying open-weight models via SageMaker JumpStart, MLOps automation with SageMaker Pipelines and Model Registry, and endpoint optimization are covered hands-on. Participants build a reusable SageMaker pipeline for one of their organisation's ML or LLM use cases.
Who Should Attend?
- Data scientists and ML engineers training, fine-tuning and deploying models on AWS
- Platform and cloud engineers building enterprise MLOps and LLMOps infrastructure
- Solution architects deciding when to use SageMaker AI versus Amazon Bedrock
- Prerequisite: Python programming, core machine learning knowledge and AWS fundamentals
Key Takeaways
- Select the right architecture across Amazon Bedrock, SageMaker JumpStart and custom SageMaker training
- Run scalable data processing, model training and PEFT/LoRA fine-tuning jobs with SageMaker Training
- Automate training, evaluation and approval workflows using SageMaker Pipelines and Model Registry
- Optimize GPU cost and latency using real-time, asynchronous, batch and multi-adapter inference endpoints
- Detect data drift, model quality decay and bias in production with SageMaker Model Monitor and Clarify
Curriculum
The AWS AI Stack and SageMaker AI Architecture
- Positioning managed AI services (Textract, Transcribe), Amazon Bedrock and SageMaker AI
- SageMaker Unified Studio, IAM execution roles, VPC isolation and the SageMaker Python SDK
Data Preparation, Feature Store and Model Training
- SageMaker Processing jobs, Feature Store (online/offline) and built-in algorithms
- Script mode, custom containers, Managed Spot Training and experiment tracking
SageMaker JumpStart and Fine-Tuning Open-Weight LLMs
- Deploying Llama, Qwen and Mistral models via JumpStart and Hugging Face containers
- PEFT/QLoRA fine-tuning jobs and high-resiliency cluster concepts (HyperPod)
Inference Deployment and GPU Cost Optimization
- Real-time, Serverless, Asynchronous and Batch Transform endpoints
- LMI/vLLM/TGI containers, Inference Components (multi-model/multi-adapter packing) and autoscaling
SageMaker Pipelines, Model Registry and Production Monitoring
- Building MLOps DAGs, governing releases in Model Registry and wiring CI/CD triggers
- Endpoint data capture, Model Monitor drift alerts and explainability with Clarify
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