What You’ll Learn: AWS for Real AI Work
You’ll move beyond local prototypes and deploy real, scalable AI systems on AWS — the way top companies do it.
SageMaker Training
Use managed notebooks, built-in algorithms, and custom containers to train models at scale.
Model Deployment
Deploy real-time endpoints or batch transforms with auto-scaling and HTTPS.
IAM & Security
Configure roles, policies, and VPCs to keep your models and data secure.
Monitoring & Cost
Track model performance, set alarms, and optimize spend using CloudWatch and billing tools.
Who Is This Course For?
Designed for developers and data scientists ready to take their AI from notebook to production — with confidence in the cloud.
- ML engineers preparing for cloud roles
- Data scientists scaling models beyond local machines
- Developers integrating AI into production apps
Hands-On Projects
SageMaker Training Job
Train a model using SageMaker Estimator and built-in XGBoost on a real dataset.
Real-Time Prediction API
Deploy a model endpoint and call it from a Python client or web frontend.
Capstone
End-to-End Secure Pipeline
Build a secure, monitored pipeline: S3 → SageMaker → Lambda → API Gateway.
4-Week AWS Syllabus
~48 hours total • Lifetime LMS access • 1:1 mentor support • Free Tier guidance
Week 1: AWS Foundations
- AWS account setup & Free Tier
- IAM roles and policies for ML
- S3 for data storage
- Cloud security best practices
Week 2: SageMaker Training
- SageMaker Studio & Notebooks
- Using built-in algorithms
- Custom training scripts
- Hyperparameter tuning
Week 3: Deployment & APIs
- Real-time endpoints
- Batch transform jobs
- Integrating with Lambda & API Gateway
- Testing and versioning models
Week 4: Monitoring & Cost
- Model monitoring with SageMaker Model Monitor
- CloudWatch alarms and logs
- Cost tracking and optimization
- CI/CD for ML (overview)
NSTC‑Accredited Certificate
Share your verified credential on LinkedIn, resumes, and portfolios.
Frequently Asked Questions
Basic familiarity with cloud concepts helps, but we provide a guided AWS account setup and free-tier guidance. Prior ML or Python experience is more important.
Yes! You’ll work directly in the AWS Console with SageMaker, S3, Lambda, and IAM — using the AWS Free Tier where possible.
