AWS Certified Machine Learning (Specialty)
Specialize in AWS ML. Cover algo basics to advanced, feature eng, eval, deploy. Ready for specialty cert via labs/cases.
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Course Level
Beginner to Advanced
Certification
Complete All Modules
Career Impact
High Growth Potential
Course Overview
Specialize in AWS ML. Cover algo basics to advanced, feature eng, eval, deploy. Ready for specialty cert via labs/cases.
What You'll Learn
Key concepts/algos AWS
Train/deploy with SageMaker
Frameworks TensorFlow/PyTorch AWS
Predictive models class/reg/cluster
Reinforcement principles/application
NLP/use cases AWS
Optimization/tuning
AutoML processes
Serverless ML with Lambda
Preprocessing with S3/Redshift/RDS
Pipelines ML workflows
Large datasets handling
Monitoring/mgmt
Scale/deploy infra
Security/compliance ML
Complete ML solution hands-on
Life cycles mgmt
Hyperparameter tuning/eval
Algorithms reinforcement
Production integration
Big data analytics ML/AI
Exam prep mocks
Tools/services key
Process/clean training
Real-time apps
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Expert Support
Get expert guidance from our dedicated support team.
Detailed Curriculum
AWS ML/Tools Intro
Ecosystem ML
Services offered ML
SageMaker build/deploy
Lambda serverless ML
Glue/prep ML
Model types
Supervised/unsupervised
Basic workflow
Preprocess/clean training
Marketplace datasets/algos
Engineering importance
Basic predictive SageMaker
Eval techniques/metrics
Tune better accuracy
Notebooks experimentation SageMaker
Deep learning/neural
Batch inference SageMaker
Retraining auto SageMaker
Integration lakes
CI/CD models
Prep/Preprocessing ML Data
Quality/prep importance
Wrangling structured/unstructured
ETL with Glue
Storage solutions ML
Preprocessing S3/Redshift
Engineering improve performance
Cleaning auto Lambda
Formats use ML pipelines
Missing values/imputation
Normalization/scaling convergence
Batch/real-time processing
Querying with Athena
Text for NLP
Time-series predictive
Pipelines auto Glue/Step Functions
Image/video with Rekognition
Validation/cleaning strategies
Distributed with EMR
Pipeline monitoring/error handling
Visual prep with DataBrew
Training/Eval AWS
Algo choice problem
Class/reg/cluster differences
Training built-in algos SageMaker
Tuning with HPO
Auto tuning with HPO SageMaker
Accuracy with cross-validation/holdout
Bias-variance tradeoff
K-fold robustness
Metrics precision/recall/F1/AUC
Endpoints real-time predictions
Batch large datasets
Over/underfitting
Monitoring with Model Monitor
Explainability interpret
Fairness/bias detect
Multi-model deployments
Pipelines continuous
Reinforcement eval
Transfer learning optimization
Performance live feedback
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