Spécialisation Exam Prep MLA-C01: AWS Machine Learning Engineer Assocaite

Apply data cleaning, transformation, and feature engineering techniques to prepare datasets for machine learning.

Recognize methods to detect and reduce bias in data preparation and securely manage PII using AWS tools like DataBrew.

Implement ETL workflows using AWS Glue, Glue Crawlers, and DataBrew for data preparation.

Process large-scale datasets using Apache Spark on Amazon EMR for machine learning workloads.

Explore built-in algorithms in Amazon SageMaker such as Linear Learner, XGBoost, LightGBM, and k-NN for ML model development.

Configure key training parameters like epochs, batch size, and steps to train and evaluate ML models effectively.

Compare real-time and batch inference approaches to determine the best strategy for model deployment.

Compare AWS storage options and select the appropriate solution for ML data management.

Explore the end-to-end capabilities of Amazon SageMaker for building and managing ML workflows.

Secure sensitive data using AWS KMS and Secrets Manager for encryption and credential management.

Implement intelligent search and document extraction with Amazon Kendra and Textract.

Create personalized experiences and human review workflows using Personalize, A2I, and Mechanical Turk.

Leverage AWS AI services like Comprehend, Translate, Transcribe, and Polly for language and speech processing tasks.

Apply Amazon Rekognition and Amazon Lex to build intelligent image analysis and conversational AI solutions.