Program Overview & Executive Scope
The AWS Certified Machine Learning – Specialty program equips engineers and data scientists to solve real-world business challenges using AWS ML infrastructure. Across 40 hours of applied lab sessions, candidates implement automated feature engineering, deep learning training with PyTorch on EC2 GPU clusters, SageMaker Pipelines, and real-time inference endpoint monitoring.
Key Competencies & What You Will Master
Curriculum & Module Breakdown
Data Engineering for ML
S3 storage formats, AWS Glue ETL, Kinesis streaming ingestion, and Athena query tuning.
Exploratory Data Analysis & Feature Engineering
Data profiling, correlation analysis, dimensionality reduction, and text tokenization.
Modeling & SageMaker Built-in Algorithms
Supervised, unsupervised algorithms, hyperparameter tuning, and distributed training.
Machine Learning Implementation & Operations (MLOps)
Model deployment, endpoint scaling, drift monitoring, and AWS AI services.
Prerequisites
- •1-2 years experience developing, architecting, or running ML/DL workloads in the AWS Cloud
Target Audience
- •Machine Learning Engineers
- •Data Scientists
- •AI Software Engineers
Frequently Asked Questions
Yes, our cloud sandboxes provide dedicated GPU instances for SageMaker training during hands-on exercises at no extra charge.
Includes AWS sandbox lab access and Pearson VUE voucher.
Related Certifications in Applied AI, Data & Analytics
Google Cloud Professional Data Engineer
Exam Code: GCP-PDE • 100% Sandbox Labs
Microsoft Azure Data Scientist Associate (DP-100)
Exam Code: DP-100 • 100% Sandbox Labs
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