AWS Certified Machine Learning Engineer Associate
Length
3 days / 3 weeks
Price
$1,320 USD
Days
Mon - Fri
Learn More
Why Choose This Course
AWS Certified Machine Learning Engineer Associate is designed for professionals who want tovalidatetheir ability to build, deploy, andmaintainmachine learning solutions on AWS. This course focuses on the full ML lifecycle, including data preparation, model development, deployment, monitoring, and optimization using AWS services such as Amazon SageMaker, AWS Glue, and Amazon S3.
The training covers best practices for operationalizing ML workloads, implementing CI/CD pipelines for ML workflows, and securing ML systems. Participants will gain hands-on experience through labs and real-world scenarios, ensuring practical application of concepts like feature engineering, hyperparameter tuning, and model evaluation.
This certification is highly relevant for roles such as machine learning engineers, data engineers, DevOps specialists, and backend developers working with ML workloads. It aligns with industry demand for professionals who can implement scalable, production-grade ML solutions in the cloud. A certificate of course attendance is included.
Prerequisites
- There are no formal prerequisites for this course. Recommended: 1 year of experience with AWS services and basic knowledge of ML algorithms.
Exam
- AWS Certified Machine Learning Engineer – Associate (MLA-C01).
Books
- AWS Certified Machine Learning Engineer Associate
Delivery
- Live virtual online training attend in real-time from anywhere
Skills Gained
- Prepare and transform data for ML modeling using AWS services
- Perform feature engineering and data validation
- Train andoptimizeML models with Amazon SageMaker
- Tune hyperparameters and analyze model performance
- Deploy models using endpoints and configure auto scaling
- Implement CI/CD pipelines for ML workflows
- Monitor models, data, and infrastructure for issues
- Secure ML systems with IAM and encryption best practices
- Manage model versions and lifecycle
- OptimizeML workloads for cost and performance
- Apply troubleshooting techniques for ML pipelines
Audience
- Machine learning engineers andMLOpsspecialists
- Data engineers and data scientists
- Backend developers and DevOps engineers working with ML workloads
Course Schedule & Pricing
Choose the schedule that fits your life — all options include full course materials & certification support
Full-time immersion for rapid certification readiness.
Balance your career while you upgrade your skills.
Maximum flexibility for busy working professionals.
Outline
- Introduction to AWS Machine Learning Services
- Amazon SageMaker overview and capabilities
- Data ingestion and transformation with AWS Glue and S3
- Feature engineering techniques and tools
- Model training and evaluation in SageMaker
- Hyperparameter tuning and optimization
- Model deployment strategies and endpoints
- CI/CD for ML workflows using SageMaker Pipelines
- Monitoring ML models with CloudWatch and SageMaker Model Monitor
- Security and compliance for ML workloads
- Cost optimization strategies for ML solutions
- Troubleshooting ML pipelines and deployments
- Hands-on labs: data preparation, model training, deployment, and monitoring
- Exam preparation tips and practice questions
Terms & Conditions
Frequently Asked Questions (FAQ's)
What is the AWS Certified Machine Learning Engineer Associate certification?
Who should take this course?
Do I need prior ML experience?
No formal prerequisites are required, but familiarity with AWS services and basic ML concepts is recommended.
Our Partnership
Reliable certification testing is vital for validating professional skills in today’s tech-driven world. As a Pearson VUE Authorised Centre, we provide a secure environment for globally recognised IT exams. This partnership ensures convenient access to certifications with the highest standards of integrity and accuracy.
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