Master AWS machine learning services including SageMaker, ML algorithms, and AI services. Build, train, deploy, and monitor ML models at scale on AWS.
AWS Machine Learning Specialists design, build, train, and deploy machine learning models on AWS. You'll architect end-to-end ML pipelines, select appropriate algorithms, implement MLOps workflows, optimize model performance, and leverage AWS AI/ML services to solve business problems at scale.
Solutions Architect Associate
Recommended FoundationMachine Learning Specialty
After Phase 5The AWS Certified Machine Learning - Specialty (MLS-C01) exam tests your knowledge across four key domains:
If you're new to AWS Machine Learning:
→Begin with Phase 1: ML Fundamentals Review (expand below)
→Complete AWS Solutions Architect Associate first if needed
→Focus 80% of study time on SageMaker - it's the heart of this exam
→Complete one phase at a time with hands-on practice
Already have ML experience?
→Jump to Phase 2 (SageMaker Mastery) and focus on AWS-specific services
Supervised/unsupervised learning, bias-variance tradeoff, overfitting prevention, cross-validation, evaluation metrics
Missing value handling, outlier detection, feature scaling, categorical encoding, data augmentation, imbalanced datasets
Linear/logistic regression, decision trees, random forests, gradient boosting, SVMs, neural networks, clustering, dimensionality reduction
Feature selection, feature extraction, polynomial features, interaction terms, domain knowledge application
Studio, Processing, Training, Hyperparameter Tuning, Endpoints, Batch Transform, Model Registry, Pipelines
XGBoost, Linear Learner, Image Classification, Object Detection, BlazingText, DeepAR, K-Means, PCA, Random Cut Forest
SageMaker Processing, Data Wrangler, Feature Store, Ground Truth, Pipe mode for large datasets
Model Monitor (data drift, model quality), Clarify (bias, explainability), Debugger, Experiments, distributed training
Rekognition (object detection, facial analysis, custom labels), Textract (OCR, table extraction)
Comprehend (NER, sentiment), Translate, Transcribe (speech-to-text), Polly (text-to-speech)
Forecast (time-series), Personalize (recommendations), Lex (chatbots), Kendra (intelligent search), Fraud Detector
When to use managed AI services vs custom SageMaker models, cost-benefit analysis
Quantization, pruning, knowledge distillation, SageMaker Neo compilation, Elastic Inference
Blue/Green deployment, canary rollout, A/B testing, shadow mode, multi-model endpoints, serverless inference
Data drift detection, model quality monitoring, concept drift, retraining automation, SageMaker Pipelines for MLOps
Spot instances (70-90% savings), right-sizing, Batch Transform vs endpoints, multi-model endpoints, Savings Plans
Complete 3-4 full practice exams, achieve 85%+ consistently, review all incorrect answers thoroughly
SageMaker documentation, built-in algorithm details, AI services FAQs, ML Lens whitepaper
Fraud detection, recommendation systems, image classification, NLP, time-series forecasting, multi-region ML
Keyword recognition (cost-effective, least overhead, real-time), time management (2.8 min/question), elimination
MLS-C01: AWS Certified Machine Learning - Specialty
This certification validates your expertise in building, training, tuning, and deploying ML models on AWS. Exam details: 180 minutes, 65 questions, $300 USD, passing score 750/1000. Focus 80% of study on SageMaker!
Practice MLS-C01 QuestionsDesign and implement complete ML workflows from data ingestion to model deployment using SageMaker
Choose right algorithms, built-in vs custom models, managed AI services vs SageMaker for specific use cases
Implement deployment strategies, configure monitoring, detect drift, automate retraining pipelines
Use Spot instances, optimize inference endpoints, implement model compression, right-size resources
Validate your ML expertise with AWS's official Machine Learning Specialty credential
Implement CI/CD for ML, version models, ensure security, maintain compliance, follow Well-Architected ML Lens