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Keep shoppingAWS Certified Machine Learning Specialty - Hands-On + Exams
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AWS Certified Machine Learning Specialty - Hands-On + Exams
Theory | Hands-On Labs | Practice Questions | Downloadable PDF Slides | Pass the certification exam | Latest Syllabus
Created byDeepak Dubey
Last updated 5/2026
English
English [Auto],
What you'll learn
- Design and implement scalable ML data pipelines using AWS services like Kinesis, Glue, EMR, and Firehose for batch and streaming workloads
- Build, train, and optimize ML models using SageMaker with proper hyperparameter tuning, cross-validation, and evaluation metrics
- Deploy production ML solutions with AWS security best practices including IAM policies, VPC configuration, and data encryption
- Operationalize ML systems with monitoring, A/B testing, automated retraining pipelines, and performance optimization on AWS
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Course content
12 sections • 78 lectures • 21h 57m total length
Requirements
- Basic understanding of machine learning concepts and Python programming is helpful, with AWS Machine Learning Associate certification providing a strong foundation but not required. You'll need an AWS free tier account for hands-on labs - all AWS services are explained from scratch with step-by-step demonstrations.
Description
Master AWS Machine Learning Services and Pass the MLS-C01 Certification Exam
This comprehensive course prepares you for the AWS Certified Machine Learning - Specialty (MLS-C01) certification exam through extensive content covering all essential AWS services. Aligned with the official AWS exam guide, the curriculum addresses all four certification domains: Data Engineering (20%), Exploratory Data Analysis (24%), Modeling (36%), and Machine Learning Implementation & Operations (20%).
What You'll Learn:
Through structured lectures combining theory and hands-on demonstrations, you'll master the complete ML lifecycle on AWS. Build data ingestion pipelines using Kinesis, Glue, and EMR. Design feature engineering solutions with proper data preprocessing and transformation techniques. Train and optimize models using SageMaker's built-in algorithms and custom implementations. Deploy production-ready ML solutions with comprehensive security, monitoring, and operational best practices.
Course Structure:
Each AWS service receives dedicated coverage with theoretical explanation followed by practical demonstration. Starting with foundational analytics services (Athena, Redshift, QuickSight), you'll progress through compute options (EC2, Lambda, Batch), containerization (ECS, EKS, Fargate), and dive deep into ML-specific services. The centerpiece SageMaker section covers end-to-end model development, while AI services like Comprehend, Rekognition, Transcribe, and Textract demonstrate pre-built ML capabilities.
Hands-On Learning:
A significant portion of the course consists of hands-on labs where you'll implement real solutions in the AWS console. Configure VPCs for secure ML environments, set up IAM policies for least-privilege access, implement CloudWatch monitoring for model performance, and build complete ML pipelines from data ingestion to model deployment. Every demonstration uses free-tier eligible services, ensuring you can follow along without significant costs.
Exam Preparation:
Beyond service knowledge, you'll understand key ML concepts required for certification: hyperparameter optimization, cross-validation, bias-variance tradeoffs, evaluation metrics (AUC-ROC, F1, precision/recall), and model selection criteria. Learn when to use built-in algorithms versus custom models, how to right-size infrastructure for cost optimization, and best practices for MLOps including A/B testing and automated retraining pipelines.
Additional Resources:
The course includes downloadable PDF slides for offline review, practice questions aligned with exam format, and reference materials for continued learning. Each section builds upon previous knowledge, creating a structured learning path from fundamentals to advanced implementations.
Who Should Enroll:
Perfect for data scientists, ML engineers, cloud architects, and developers pursuing AWS ML specialty certification. Whether advancing from AWS ML Associate certification or building on existing ML experience, this course provides both theoretical knowledge and practical skills needed for exam success and real-world implementation.
Start your journey to becoming an AWS Certified Machine Learning Specialist today.
Who this course is for:
- Data scientists, ML engineers, and cloud professionals preparing for the AWS Certified Machine Learning - Specialty (MLS-C01) exam, or anyone with AWS ML Associate certification ready to advance. Also suitable for developers and IT professionals transitioning to MLOps roles who need hands-on experience with AWS's complete ML ecosystem.