AWS Certified Machine Learning Engineer - Associate
Description
Comprehensive learning pathway to master AWS machine learning engineering skills, covering data preparation, model development, deployment, and operations using AWS services like SageMaker, Bedrock, and other AI/ML tools.
Learning Objectives
Learners will develop comprehensive expertise in implementing ML workloads in production and operationalizing them using AWS cloud services. They will master data preparation, feature engineering, model training and tuning, deployment strategies, monitoring, and security practices. Upon completion, learners will be capable of designing, building, deploying, and maintaining machine learning solutions and pipelines in the AWS Cloud, with proficiency in SageMaker ecosystem, MLOps practices, and AI/ML service integration.
Courses in this Bundle (15)
Python Programming for Machine Learning
Python programming skills specifically focused on machine learning applications, data manipulation, and scientific computing.
Machine Learning Fundamentals and Statistics
Foundation concepts in machine learning, statistics, and mathematical principles required for ML engineering.
AWS AI/ML Services Integration
Comprehensive integration of AWS AI/ML services including Comprehend, Rekognition, Transcribe, Textract, and other managed AI services with ML workflo...
Performance Optimization and Cost Management
Comprehensive performance optimization techniques and cost management strategies for efficient and cost-effective ML operations on AWS.
ML Model Development and Training on SageMaker
Advanced model development techniques, training strategies, and optimization methods using SageMaker's comprehensive ML development platform.
MLOps and CI/CD for Machine Learning
Comprehensive MLOps practices, CI/CD pipeline implementation, and automation strategies for machine learning workflows on AWS.
Data Preparation and Feature Engineering
Advanced data preparation techniques, feature engineering, and data preprocessing specifically for machine learning workflows on AWS.
Generative AI and Amazon Bedrock
Comprehensive understanding of generative AI concepts, foundation models, and Amazon Bedrock for building AI applications.
SageMaker Built-in Algorithms and Frameworks
Comprehensive understanding of SageMaker's built-in algorithms, their applications, and framework integrations for various ML use cases.
Security and Compliance for ML Systems
Comprehensive security framework, compliance requirements, and data protection strategies for machine learning systems on AWS.
ML Solution Monitoring and Maintenance
Comprehensive monitoring, maintenance, and operational management of production ML systems using AWS monitoring and management tools.
Model Deployment and Inference Strategies
Comprehensive model deployment strategies, inference optimization, and production deployment patterns using SageMaker's deployment capabilities.
Amazon SageMaker Fundamentals
Comprehensive introduction to Amazon SageMaker platform, its core components, and basic ML workflow implementation.
AWS Core Services and Cloud Fundamentals
Essential AWS services and cloud computing concepts necessary for machine learning engineering on AWS platform.
AWS Certified Machine Learning Engineer Associate Exam Preparation
Comprehensive exam preparation including terminology, exam details, study resources, communities, and practical preparation strategies for the AWS Cer...