AWS Certified Machine Learning - Specialty

The AWS Certified Machine Learning – Specialty (MLS-C01) validates deep expertise in designing, building, training, tuning, and deploying machine learning solutions on AWS. It covers the complete ML lifecycle — from framing a business problem and preparing data, to selecting and training models, evaluating performance, and operationalizing production ML systems. This specialty is ideal for data scientists, ML engineers, and AI practitioners who implement ML workloads on AWS at scale.

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What is the AWS Certified Machine Learning - Specialty (MLS-C01)?

Who Should Take This Course?

Data scientists designing and training ML models on AWS
ML engineers building model training and deployment pipelines
Data engineers who want to extend their skills into ML workflows
Software engineers implementing AI/ML features in production systems
AI practitioners working with AWS SageMaker and AI services
Analytics professionals moving toward predictive and prescriptive analytics

What You Will Learn in the MLS-C01 Course

A comprehensive curriculum covering all exam objectives with hands-on labs and real-world practice.

Data Engineering for ML

Prepare, store, and transform data specifically for machine learning workflows.

Amazon S3 data lake design for ML training datasets
AWS Glue and Amazon EMR for large-scale data preparation
Amazon SageMaker Data Wrangler for visual data transformation
Feature engineering best practices and feature stores (SageMaker Feature Store)
Handling imbalanced datasets, missing values, and outliers

Exploratory Data Analysis

Apply statistical analysis and visualization techniques to understand data before modeling.

Statistical concepts: distributions, correlations, and hypothesis testing
Amazon SageMaker Studio notebooks for exploratory analysis
Amazon QuickSight for data visualization and insight discovery
Identifying feature importance and collinearity
Data labeling with Amazon SageMaker Ground Truth

Model Training and Tuning

Select, train, and optimize machine learning models using Amazon SageMaker.

Amazon SageMaker built-in algorithms (XGBoost, Linear Learner, DeepAR, etc.)
Custom training with TensorFlow, PyTorch, and MXNet on SageMaker
Hyperparameter tuning with SageMaker Automatic Model Tuning
Distributed training strategies with SageMaker Training Jobs
Bias detection and explainability with SageMaker Clarify

Model Evaluation and Improvement

Evaluate model quality and apply techniques to reduce error and improve generalization.

Classification metrics: accuracy, precision, recall, F1, AUC-ROC
Regression metrics: RMSE, MAE, R-squared
Cross-validation, overfitting detection, and regularization
A/B testing and shadow deployment for model comparison
Model versioning and lineage tracking in SageMaker

ML Model Deployment and Operations

Deploy, monitor, and maintain ML models in production on AWS.

SageMaker real-time endpoints, batch transform, and async inference
Multi-model endpoints and serverless inference for cost optimization
SageMaker Pipelines for CI/CD of ML workflows
Model monitoring with SageMaker Model Monitor (data drift, bias drift)
AWS Lambda and API Gateway for lightweight ML inference APIs

AWS AI Services

Leverage pre-trained AWS AI services to add intelligence to applications.

Amazon Rekognition for image and video analysis
Amazon Comprehend for NLP and sentiment analysis
Amazon Forecast for time-series predictions
Amazon Personalize for real-time recommendation systems
Amazon Textract for automated document processing

Course Prerequisites

Pre-requisites training is free when you purchase the course from ProSupport

1–2 years of hands-on experience developing, architecting, or running ML workloads on AWS
Proficiency in at least one ML framework (TensorFlow, PyTorch, Scikit-learn)
Working knowledge of Python and data manipulation libraries (Pandas, NumPy)
Familiarity with statistics and linear algebra fundamentals
AWS Solutions Architect Associate or AWS Data Engineer Associate (recommended)

Exam Information

Everything you need to know about the MLS-C01 certification exam.

Exam Component	Details
Exam Name	AWS Certified Machine Learning - Specialty
Exam Code	MLS-C01
Exam Type	Multiple Choice and Multiple Response
Total Questions	65
Passing Score	750 (out of 1000)
Exam Duration	180 minutes
Language	English, Japanese, Korean, Simplified Chinese
Exam Provider	AWS / Pearson VUE
Exam Focus	End-to-end ML lifecycle on AWS — data prep, modeling, evaluation, and production deployment
Exam Registration	Register via aws.amazon.com/certification or Pearson VUE testing centers globally
Retake Policy	14 days wait after first failure; 90 days after second and subsequent failures
Certification Validity	3 years (renewable via recertification exam)