AWS Certified Machine Learning Specialty MLS-C01

Specialty MLS-C01

AWS Certified Machine Learning – Specialty validates deep expertise in building, architecting, and running ML and deep learning workloads on AWS. It covers the full ML lifecycle from data engineering through modelling, deployment, and production operations.

Amazon SageMaker Deep Learning TensorFlow PyTorch Feature Engineering Amazon Kinesis AWS Glue Amazon EMR Model Evaluation SageMaker Autopilot

Exam Domains

Four Deep ML Knowledge Domains

MLS-C01 is the highest ML credential on AWS, testing expert-level skills across data engineering, exploratory analysis, modelling, and production operations.

Domain 01

Data Engineering

24%

exam weight

›Create data repositories — S3, EFS, EBS, Redshift, and RDS for ML workloads
›Data ingestion — Kinesis Data Streams, Kinesis Firehose, AWS Glue, Apache Kafka
›Data transformation — ETL with Glue, EMR, Spark, Hive, and MapReduce
›Streaming vs batch ingestion patterns and their ML use cases
›Schedule and orchestrate data pipelines with AWS Step Functions and EventBridge

Domain 02

Exploratory Data Analysis

18%

exam weight

›Statistical analysis — correlation, distributions, p-values, summary statistics
›Feature engineering — encoding, normalizing, augmenting, and scaling features
›Data visualization — scatter plots, histograms, box plots, and time series
›Cluster analysis — hierarchical clustering, elbow plot, and k-means diagnostics
›Handle class imbalance, missing data, outliers, and stop words

Domain 03

Modeling

36%

exam weight

›Frame business problems as ML — classification, regression, clustering, forecasting
›Algorithm selection — XGBoost, linear learner, random forests, CNNs, RNNs, transformers
›SageMaker training — distributed, spot, hyperparameter tuning, and AutoML
›Regularization — L1/L2, dropout, cross-validation, and early stopping
›Evaluate models — AUC-ROC, F1, precision, recall, RMSE, confusion matrices

Domain 04

ML Implementation & Operations

22%

exam weight

›SageMaker endpoints — real-time, batch transform, serverless, multi-model
›Build ML systems for high availability, fault tolerance, and auto scaling
›AWS security for ML — IAM roles, VPC isolation, KMS encryption, audit trails
›Monitor deployed models — SageMaker Model Monitor, CloudWatch, drift detection
›Recommend appropriate ML services — Rekognition, Comprehend, Forecast, Polly

Why MLS-C01

The Gold Standard AWS ML Certification

MLS-C01 is AWS's most advanced ML credential. It validates end-to-end mastery of the machine learning lifecycle — from raw data through production-grade model deployment and monitoring.

Specialty Tier — Expert-Level ML Mastery

MLS-C01 requires 2+ years of ML experience on AWS. It commands premium salaries and is listed as preferred or required in senior data science and AI architecture roles globally.

Deepest ML Coverage on AWS

MLS-C01 tests the entire ML spectrum — data engineering, statistical analysis, neural networks, NLP, computer vision, and production operations on AWS.

Data Engineering Is Half the Battle

Real-world ML success depends on data quality. MLS-C01 tests Kinesis, Glue, EMR, and Spark — ensuring you can build the data pipelines that feed production ML systems.

Security-First ML Architecture

VPC isolation, IAM execution roles, KMS encryption, and audit trails are core exam areas — ensuring ML systems meet enterprise security standards.

Modelling Excellence

From XGBoost to deep learning, MLS-C01 tests algorithm selection, hyperparameter tuning, evaluation metrics, and regularization at expert depth.

Maximum Career Impact

MLS-C01 holders are among the most sought-after professionals in tech — qualifying for Principal Data Scientist, ML Architect, and AI Lead roles at top companies.

Curriculum

15-Topic MLS-C01 Programme

Comprehensive ML specialty curriculum covering data engineering, exploratory analysis, advanced modelling, and production ML operations on AWS.

Identify data sources — content, location, and primary sources such as user data
Determine storage mediums — S3, EFS, EBS, RDS, DynamoDB, and Redshift for ML data
Data lake architecture with AWS Lake Formation and Glue Data Catalog
Choose appropriate storage tiers based on ML access patterns and cost

Identify data job styles — batch load and streaming ingestion pipelines
Orchestrate data pipelines using AWS Glue, EMR, and AWS Batch
Amazon Kinesis Data Streams and Kinesis Firehose for real-time data ingestion
Managed Service for Apache Flink for streaming ML feature computation
Schedule and automate ingestion jobs with EventBridge and Step Functions

Transform data in transit — ETL with AWS Glue, EMR, and Apache Spark
Handle ML-specific data with MapReduce — Hadoop, Spark, Hive
Feature encoding — one-hot, target, binary, and embedding techniques
Dimensionality reduction — PCA, feature selection, and regularization
AWS Glue DataBrew for visual low-code data transformation workflows

Identify and handle missing data, corrupt records, and stop words
Format, normalize, augment, and scale feature data for ML algorithms
Determine data labelling requirements and sufficiency for supervised learning
Mitigation strategies — oversampling, undersampling, and synthetic data generation
SageMaker Ground Truth for human and automated data labelling workflows

Extract features from text, images, audio, and public datasets
Binning, tokenization, and synthetic feature creation techniques
Outlier detection and treatment strategies — IQR, z-scores, isolation forest
One-hot encoding, ordinal encoding, and embedding representations
Correlation analysis and multicollinearity handling in feature sets

Create graphs — scatter plots, time series, histograms, box plots, heatmaps
Interpret descriptive statistics — mean, median, variance, correlation, p-value
Perform cluster analysis — hierarchical, k-means, elbow plot, silhouette score
Amazon QuickSight and SageMaker Studio for ML data visualization
Statistical hypothesis testing for feature significance evaluation

Determine when to use and when not to use ML for business problems
Supervised vs unsupervised vs reinforcement learning — when to apply each
Select model types — classification, regression, forecasting, clustering, recommendations
Define success metrics aligned to business KPIs and model performance indicators
Cost-benefit analysis of building custom vs using AWS AI services

XGBoost, logistic regression, linear regression, and tree-based model selection
K-means, DBSCAN, and hierarchical clustering for unsupervised problems
RNN, LSTM, and transformer architectures for sequential and NLP tasks
CNN architectures for image classification, detection, and segmentation
Transfer learning — fine-tuning pre-trained models for domain-specific tasks

Split data — training, validation, and holdout test set strategies
SageMaker training jobs — instance types, spot training, and checkpointing
Distributed training — data parallelism and model parallelism on SageMaker
Optimization techniques — gradient descent, Adam, momentum, learning rate schedules
Batch vs online (real-time) training update strategies

Regularization — L1 (Lasso), L2 (Ridge), and elastic net regularization
Dropout layers and batch normalization in deep neural networks
SageMaker Automatic Model Tuning — Bayesian, random, and grid search strategies
Neural network architecture choices — layers, nodes, and activation functions
Tree-based HPO — number of trees, max depth, learning rate, and min samples

Avoid overfitting and underfitting — bias-variance trade-off analysis
Evaluate metrics — AUC-ROC, accuracy, precision, recall, F1, RMSE, MAE
Interpret and optimize confusion matrices — threshold tuning
A/B testing and online model evaluation with traffic splitting on endpoints
Compare models by training time, inference latency, and engineering cost

Monitor AWS environments — CloudWatch, CloudTrail, and SageMaker Model Monitor
Build error monitoring and alerting solutions for ML endpoints
Multi-AZ and multi-Region deployment strategies for ML inference
Docker containers, AMIs, and Auto Scaling for ML inference fleets
Right-size compute resources — GPU vs CPU, distributed vs single-node

AWS AI services — Rekognition, Comprehend, Forecast, Personalize, Textract
SageMaker built-in algorithms vs custom model trade-offs
AWS Bedrock for generative AI and foundation model integration
AWS service quotas and limit planning for production ML systems
Spot Instances with SageMaker Managed Spot Training for cost reduction

IAM roles and policies for SageMaker — execution roles and resource policies
S3 bucket policies, KMS encryption, and network isolation for ML data
VPC endpoints for private SageMaker access — no internet traffic
SageMaker Clarify for bias detection, fairness metrics, and explainability
CloudTrail audit logging for ML experiment and training job activities

SageMaker endpoints — real-time, batch transform, serverless, and async inference
Expose and manage inference endpoints with API Gateway and Lambda
Perform A/B testing with SageMaker endpoint traffic splitting
Retrain pipelines — SageMaker Pipelines with scheduled and drift-triggered retraining
Debug and troubleshoot models with SageMaker Debugger and Profiler

Course Snapshot

15 Topics

Full MLS-C01 domains

55+ Hours

Total learning time

Specialty Cert

Expert ML level

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Career Outcomes

MLS-C01 Careers in Advanced ML

MLS-C01 is the most prestigious AWS ML credential. Holders qualify for principal data science, ML architecture, and AI leadership roles commanding premium compensation globally.

Amazon

ML Scientist

Google

AI Research

Microsoft

Data Science

Machine Learning

Specialty MLS-C01

Four Deep ML Knowledge Domains

The Gold Standard AWS ML Certification

15-Topic MLS-C01 Programme

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MLS-C01 Careers in Advanced ML

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