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 Praktische Übungen — nothing to install
- 10 Interaktive Lektionen Und 56 topics mapped to the official exam objectives
- 389 Testfragen zur Praxis Und 2 Ausführliche Tests
Intermediate Selbstgesteuert · 1 Jahr Zugang
24 Praktische LiveLabs
Practice real IT tasks in guided environments.
- Reale Umgebungen
- Automatisch bewertet
- Keine Installation
10Interaktive Lektionen
56Topics
24LiveLab
389Testfragen zur Praxis
8Videos
200Karteikarten
100Glossar der Begriffe
01 / Fähigkeiten, die Sie erwerben
What you will be able to do
Try Free →
Keine Kreditkarte benötigt
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 Strukturierte Lektionen Umfassende Abdeckung der zentralen Kursziele
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24 Praktische LiveLabs Interaktive, geführte Szenarien mit sofortiger Auswertung
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389 Übungsfragen Bewertungstests mit ausführlichen Antwortbegründungen
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1 Jahr voller Zugang Selbstgesteuertes Lernen, jederzeit auf allen Geräten zugänglich
02 / Lektionen & Labore
See exactly what you will learn and practice
Unterrichtsplan
10 Interaktive Lektionen · 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 LiveLab +
- Understanding Artificial Intelligence
- Understanding Machine Learning
- Understanding Deep Learning
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
03 Data Ingestion and Storage 4 topics · 3 LiveLab +
- Introducing Ingestion and Storage
- Ingesting and Storing Data
- Summary
- Exam Essentials
3 LiveLab in this lesson — see the labs panel →
04 Data Transformation and Feature Engineering 9 topics · 1 LiveLab +
- Introduction
- Understanding Feature Engineering
- Data Cleaning and Transformation
- Feature Engineering Techniques
- Data Labeling
- Managing Class Imbalance
- Data Splitting
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
05 Model Selection 5 topics · 1 LiveLab +
- Understanding AWS AI Services
- Developing Models with Amazon SageMaker Built-in Algorithms
- Criteria for Model Selection
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
06 Model Training and Evaluation 6 topics · 1 LiveLab +
- Training
- Hyperparameter Tuning
- Model Performance Evaluation
- Deep-Dive Model Tuning Example
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
07 Model Deployment and Orchestration 6 topics · 3 LiveLab +
- AWS Model Deployment Services
- Advanced Model Deployment Techniques
- Orchestrating ML Workflows
- Deep-Dive Model Deployment Example
- Summary
- Exam Essentials
3 LiveLab in this lesson — see the labs panel →
08 Model Monitoring and Cost Optimization 4 topics · 5 LiveLab +
- Monitoring Model Inference
- Monitoring Infrastructure and Cost
- Summary
- Exam Essentials
5 LiveLab in this lesson — see the labs panel →
09 Model Security 4 topics · 9 LiveLab +
- Security Design Principles
- Securing AWS Services
- Summary
- Exam Essentials
9 LiveLab in this lesson — see the labs panel →
10 Appendix A: Mathematics Essentials 4 topics +
- Linear Algebra
- Statistics
- Probability Theory
- Calculus
Praktische Übungen Our edge
24 LiveLabs- 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
Labore laufen direkt im Browser — nichts zu installieren.
03 / FAQS
Fragen, bevor Sie beginnen
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.
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Gain hands-on AWS ML skills with real-world labs, SageMaker workflows, deployment training, and exam-focused practice.
- 1 Jahr voller Zugang
- 24 LiveLab enthalten
- Abschlusszertifikat
Keine Kreditkarte benötigt