AWS ML Engineer Bootcamp: Machine Learning, MLOps & Exam Prep
Data Science Academy
AI & ML Engineering Experts
Master AWS ML engineering to build real, production-ready AI systems
Most machine learning courses stop at theory or basic model building. But in the real world, companies don’t just need models — they need end-to-end ML systems that scale, deploy, and deliver impact.
You may understand algorithms, but:
You’re unsure how to move from notebooks to production
AWS services like SageMaker, Glue, and pipelines feel overwhelming
You don’t know how to design scalable, cost-efficient ML architectures
Certification prep feels disconnected from real-world skills
In just 14 days, you’ll go from understanding ML concepts to confidently designing and implementing production-grade ML workflows on AWS.
You’ll learn how to:
Structure the entire ML lifecycle from data ingestion to deployment
Use core AWS services like S3, Glue, Athena, and SageMaker effectively
Train, tune, and deploy models using real engineering workflows
Build ML pipelines, monitoring systems, and retraining strategies
Apply MLOps best practices used in real companies
Optimize for performance, scalability, and cost
Prepare for the AWS Machine Learning Engineer Associate certification
By the end of this course, you won’t just “know ML” —
you’ll understand how to build and operate ML systems like an engineer.
What you’ll learn
Go from basic ML knowledge to building, deploying, and managing real-world ML systems on AWS with confidence.
Map full ML lifecycle from data ingestion to deployment
Choose correct AWS services (S3, Glue, SageMaker)
Apply real-world architecture patterns
Implement ETL pipelines using Glue and Athena
Apply feature engineering and preprocessing techniques
Structure data for scalable ML workflows
Use SageMaker training jobs and built-in algorithms
Apply hyperparameter tuning strategies
Evaluate models using proper validation techniques
Configure real-time and batch inference endpoints
Implement autoscaling and deployment strategies
Manage production-ready ML services
Build ML pipelines with SageMaker Pipelines
Detect model and data drift using monitoring tools
Design retraining and automation workflows
Apply cost optimization and instance selection strategies
Implement IAM, security, and compliance best practices
Solve scenario-based AWS ML certification questions
Learn directly from Data
Data Science Academy
Building real-world AI, ML & MLOps systems used in production
Who this course is for
Learners with basic ML knowledge who want to transition into real-world ML engineering roles on AWS.
Professionals who can build models but want to learn deployment, MLOps, and production systems.
Engineers looking to add AI/ML capabilities and build scalable ML systems using AWS services.
What's included
Live sessions
Learn directly from Data Science Academy in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
13 lessons • 12 projects
Week 1
Day 1 - ML Engineering Foundations
Day 2 - Data Engineering for ML
Day 3 — Feature Engineering & Feature Store
Day 4 — Model Training Fundamentals
Day 5 — Training Models on AWS
Day 6 — Model Deployment
Week 2
Day 7 — ML Pipelines & Automation
Day 8 — MLOps & Model Monitoring
Day 9 — Security & Governance in ML
Day 10 — Advanced ML Architectures
Day 11 — Real-World ML Use Cases
Day 12 — Optimization & Cost Efficiency
Day 13 — Exam Preparation
Day 14 — Full Mock Exam + Review
Schedule
Live sessions
1 hr / week
Projects
4 hrs / week
Async content
7 hrs / week
Frequently asked questions
$50
USD
6 days left to enroll