AWS-MLA.AE1
AWS Certified Machine Learning Engineer Study Guide
Master AWS ML engineering. This guide covers data to deployment, ensuring you build, train, and deploy robust models.
- Practice in 24 Laboratorios prácticos — nothing to install
- 10 Lecciones interactivas y 56 topics mapped to the official exam objectives
- 389 Preguntas del examen de práctica y 2 Pruebas completas
Intermediate A tu propio ritmo · 1 año de acceso
24 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
10Lecciones interactivas
56Topics
24Laboratorio en vivo
389Preguntas del examen de práctica
8Vídeos
200Tarjetas didácticas
100Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
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No se requiere tarjeta de crédito
his isn't a theoretical overview; it's a deep dive into becoming an AWS Certified Machine Learning Engineer. We'll dissect the entire ML lifecycle on AWS, from raw data ingestion using services like S3 and Kinesis to advanced model deployment with SageMaker. Expect to grapple with real-world data transformation challenges, understand why certain feature engineering techniques fail, and learn to optimize model performance under strict budget constraints. We'll cover critical aspects like hyperparameter tuning, model monitoring, and securing your ML pipelines. You'll gain practical skills to avoid common pitfalls, ensuring your models don't just work, but perform reliably and cost-effectively in production. This course prepares you for the exam and the job.
- Architecting and implementing robust data ingestion and storage solutions on AWS for diverse ML workloads, understanding the trade-offs between latency and cost.
- Applying advanced feature engineering and data transformation techniques to raw datasets, recognizing how data quality directly impacts model accuracy and deployment viability.
- Developing, training, and evaluating machine learning models using Amazon SageMaker, including hyperparameter tuning and identifying common overfitting/underfitting failure points.
- Deploying, orchestrating, and monitoring ML models in production environments on AWS, ensuring security, cost-efficiency, and operational resilience against real-world data drift.
Course Highlights
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10 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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24 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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389 Preguntas de práctica Pruebas de evaluación con justificaciones de respuesta detalladas
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
10 Lecciones interactivas · 56 topics01 Introduction 9 topics +
- The AWS Certified Machine Learning Engineer – Associate Exam
- Who Should Buy This Course
- Conventions Used in This Course
- Course Objectives
- AWS Certified Machine Learning Engineer Exam Objectives
- Domain 1: Data Preparation for Machine Learning (ML)
- Domain 2: ML Model Development
- Domain 3: Deployment and Orchestration of ML Workflows
- Domain 4: ML Solution Monitoring, Maintenance, and Security
02 Introduction to Machine Learning 5 topics · 1 Laboratorio en vivo +
- Understanding Artificial Intelligence
- Understanding Machine Learning
- Understanding Deep Learning
- Summary
- Exam Essentials
1 Laboratorio en vivo in this lesson — see the labs panel →
03 Data Ingestion and Storage 4 topics · 3 Laboratorio en vivo +
- Introducing Ingestion and Storage
- Ingesting and Storing Data
- Summary
- Exam Essentials
3 Laboratorio en vivo in this lesson — see the labs panel →
04 Data Transformation and Feature Engineering 9 topics · 1 Laboratorio en vivo +
- Introduction
- Understanding Feature Engineering
- Data Cleaning and Transformation
- Feature Engineering Techniques
- Data Labeling
- Managing Class Imbalance
- Data Splitting
- Summary
- Exam Essentials
1 Laboratorio en vivo in this lesson — see the labs panel →
05 Model Selection 5 topics · 1 Laboratorio en vivo +
- Understanding AWS AI Services
- Developing Models with Amazon SageMaker Built-in Algorithms
- Criteria for Model Selection
- Summary
- Exam Essentials
1 Laboratorio en vivo in this lesson — see the labs panel →
06 Model Training and Evaluation 6 topics · 1 Laboratorio en vivo +
- Training
- Hyperparameter Tuning
- Model Performance Evaluation
- Deep-Dive Model Tuning Example
- Summary
- Exam Essentials
1 Laboratorio en vivo in this lesson — see the labs panel →
07 Model Deployment and Orchestration 6 topics · 3 Laboratorio en vivo +
- AWS Model Deployment Services
- Advanced Model Deployment Techniques
- Orchestrating ML Workflows
- Deep-Dive Model Deployment Example
- Summary
- Exam Essentials
3 Laboratorio en vivo in this lesson — see the labs panel →
08 Model Monitoring and Cost Optimization 4 topics · 5 Laboratorio en vivo +
- Monitoring Model Inference
- Monitoring Infrastructure and Cost
- Summary
- Exam Essentials
5 Laboratorio en vivo in this lesson — see the labs panel →
09 Model Security 4 topics · 9 Laboratorio en vivo +
- Security Design Principles
- Securing AWS Services
- Summary
- Exam Essentials
9 Laboratorio en vivo in this lesson — see the labs panel →
10 Appendix A: Mathematics Essentials 4 topics +
- Linear Algebra
- Statistics
- Probability Theory
- Calculus
Laboratorios prácticos Our edge
24 Laboratorio en vivos- Rebuilding Clarity Through Broken AI Decisions and Model Choices
- Creating an Amazon DynamoDB Table
- Creating an Amazon S3 Glacier Storage Using Lifecycle Rules
- Creating ETL Resources Using AWS Glue
- Detecting Objects in an Image Using Amazon Rekognition
- Using Amazon Lex to Build a Chatbot
- Optimizing Models and Tuning Hyperparameters
- Generating AI Responses Using Amazon Bedrock Playground
- Creating an AWS Lambda Function
- Launching an EC2 Instance
- Creating Resources using AWS CloudFormation
- Implementing AWS CloudTrail for Security Monitoring
- Detecting Threats with Amazon GuardDuty
- Analyzing Security Logs in AWS Lambda Using CloudWatch
- Creating a Rule in Amazon EventBridge
- Creating an NACL
- Creating a Security Group
- Creating an AWS WAF Web ACL
- Creating an IAM User
- Creating and Managing IAM Policies
- Restricting Amazon S3 Access via a VPC Endpoint Policy
- Comprehensive Lab: Optimizing and Saving an XGBoost-Based Prediction Model Using Amazon SageMaker
- Comprehensive Lab: Building an End-to-End Machine Learning Pipeline Using Amazon SageMaker
- Comprehensive Lab: Training, Evaluating, and Saving a Classification Model Using Amazon SageMaker
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
Is this AWS Certified Machine Learning Engineer Study Guide suitable for beginners?+
While it covers foundational ML concepts, this guide assumes a basic understanding of AWS services and Python. We'll build from there, but if you're entirely new to cloud or programming, expect a steeper learning curve.
What kind of hands-on experience will I get with this AWS Certified Machine Learning Engineer training? +
You'll engage with 20 hands-on labs and 132 practice exercises. This isn't just theory; you'll be configuring services, writing code, and deploying models, which is crucial for understanding real-world limitations.You'll engage with 20 hands-on labs and 132 practice exercises. This isn't just theory; you'll be configuring services, writing code, and deploying models, which is crucial for understanding real-world limitations.
How does this course prepare me for the AWS Certified Machine Learning Engineer exam?+
Beyond comprehensive content, you get 90 practice quizzes, 101 flashcards, and a full practice exam. We focus on the exam objectives, but more importantly, on the practical knowledge needed to answer scenario-based questions effectively.
What are the common pitfalls when deploying ML models on AWS that this course addresses?+
We explicitly cover issues like model drift, managing inference costs, securing endpoints, and orchestrating complex pipelines. Expect to learn how to monitor for these failures and implement resilient solutions, not just deploy a model once.
Do I need a strong math background for the AWS Certified Machine Learning Engineer certification?+
The course includes a 'Mathematics Essentials' appendix covering linear algebra, statistics, probability, and calculus. While you don't need to be a mathematician, a solid grasp of these fundamentals is critical for truly understanding model behavior and limitations.
Keep going
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Build Production-Ready ML Skills on AWS
Gain hands-on AWS ML skills with real-world labs, SageMaker workflows, deployment training, and exam-focused practice.
- 1 año de acceso completo
- 24 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito