Hands-On Labs¶
Practical labs to reinforce concepts for the MLA-C01 exam.
Lab Index¶
| Lab | Topic | Domain | Difficulty |
|---|---|---|---|
| Lab 01 | SageMaker Data Wrangler | Domain 1 | Easy |
| Lab 02 | SageMaker Training Job | Domain 2 | Medium |
| Lab 03 | Hyperparameter Tuning | Domain 2 | Medium |
| Lab 04 | Endpoint Deployment | Domain 3 | Medium |
| Lab 05 | SageMaker Pipelines | Domain 3 | Hard |
| Lab 06 | Model Monitoring | Domain 4 | Medium |
Prerequisites¶
Before starting the labs, ensure you have:
- AWS Account with appropriate permissions
- AWS CLI configured
- Python 3.9+ with boto3 installed
- SageMaker Studio or Notebook instance (optional but recommended)
Cost Warning¶
AWS Costs
These labs will incur AWS costs. To minimize expenses:
- Use the smallest instance types possible
- Clean up resources after each lab
- Use SageMaker Savings Plans if doing multiple labs
- Stop notebook instances when not in use
Lab Structure¶
Each lab follows this structure:
- Objective - What you will learn
- Prerequisites - Required setup
- Steps - Detailed instructions
- Verification - How to verify success
- Cleanup - Resource cleanup instructions
- Key Takeaways - Exam-relevant points
Getting Started¶
# Clone the repository
git clone https://github.com/yourusername/aws-mla-study-notes-and-hands-on-labs.git
# Navigate to labs
cd aws-mla-study-notes-and-hands-on-labs
# Set up Python environment
python -m venv .venv
source .venv/bin/activate # Linux/macOS
# .venv\Scripts\activate # Windows
# Install dependencies
pip install boto3 sagemaker pandas