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AWS Machine Learning Engineer Associate Exam Prep

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AWS Machine Learning Engineer Associate Exam Prep

Dozenten: Neal Davis

Bei enthalten

Mehr erfahren

12 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

3 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

12 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

3 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Kompetenzen, die Sie erwerben

Kategorie: Feature Engineering
Kategorie: MLOps (Machine Learning Operations)
Kategorie: Exploratory Data Analysis
Kategorie: Cloud Engineering
Kategorie: Generative Model Architectures
Kategorie: Applied Machine Learning
Kategorie: Machine Learning
Kategorie: Data Storage
Kategorie: Data Preprocessing
Kategorie: Data Storage Technologies
Kategorie: Model Evaluation
Kategorie: Cloud Deployment
Kategorie: Model Training

Werkzeuge, die Sie lernen werden

Kategorie: Generative AI
Kategorie: Amazon S3
Kategorie: Amazon Bedrock
Kategorie: Amazon Redshift
Kategorie: Amazon Web Services
Kategorie: AWS SageMaker
Kategorie: Model Deployment

Wichtige Details

Zertifikat zur Vorlage

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Kürzlich aktualisiert!

April 2026

Bewertungen

11 Aufgaben

Unterrichtet in Englisch

91% of learners achieved a positive career outcome

Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.

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In diesem Kurs gibt es 12 Module

This course prepares learners for the AWS Certified Machine Learning Engineer – Associate certification while building practical skills in implementing and operationalizing machine learning workloads on AWS. Through a combination of clear theory, architectural diagrams, and hands-on demonstrations, the course explains how machine learning solutions are designed, deployed, and managed in real-world cloud environments.

Learners will explore the key concepts required for building production-ready ML systems, including data preparation, model training, deployment strategies, and monitoring ML workloads in AWS. The course also introduces the AWS services and tools commonly used to develop scalable and reliable machine learning solutions. By the end of the course, learners will understand how to implement and manage ML workflows on AWS and will be well prepared to take the AWS Certified Machine Learning Engineer – Associate exam.