MLS-C01.AE1
AWS Certified Machine Learning Specialty Training (MLS-C01)
Learn, prepare and practice for the AWS exam. Gain real-world experience with hands-on Labs and case studies.
- Practice in 26 Praktische Übungen — nothing to install
- 18 Interaktive Lektionen Und 93 topics mapped to the official exam objectives
- 403 Testfragen zur Praxis Und 2 Ausführliche Tests
Intermediate Selbstgesteuert · 1 Jahr Zugang 4.7/5 (48 Rezension)
26 Praktische LiveLabs
Practice real IT tasks in guided environments.
- Reale Umgebungen
- Automatisch bewertet
- Keine Installation
18Interaktive Lektionen
93Topics
26LiveLab
403Testfragen zur Praxis
188Karteikarten
89Glossar der Begriffe
01 / Fähigkeiten, die Sie erwerben
What you will be able to do
Try Free →
Keine Kreditkarte benötigt
AWS Certified Machine Learning Study Guide: Specialty (MLS-C01) is a preparatory course that provides a structured and detailed learning approach to pass the certification exam. The course focuses on foundational ML concepts, foundations of statistics, data analysis, exploration, feature engineering, and common ML algorithms. In addition to this, it equips you with the skills to deploy those solutions on AWS and to be able to architect an end-to-end solution on AWS from data ingestion to model deployment and monitoring using a host of relevant AWS services for a given business use case. This AWS Machine Learning course comes with an exam-focused approach curriculum that is totally aligned with the latest exam objectives to help you prepare and pass the exam easily.
- Expertise in using AWS AI/ML services like Amazon SageMaker, Amazon Rekognition, and more
- Understanding of ML algorithms like linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks, and deep learning models
- Data Science pipelines, the entire ML lifecycle
- Awareness of AWS infrastructure services like Amazon S3, Amazon EC2, Amazon RDS, Amazon VPC, and AWS Lambda
- Design and implement scalable and cost-effective cloud architectures for ML applications
- Skilled with deep learning frameworks like TensorFlow and PyTorch, and their application to tasks like image recognition, natural language processing, and time series analysis
- Understanding of reinforcement learning concepts and algorithms
- Tuning hyperparameter to optimize model performance
- Knowledge of ML deployment models as web services, containerized applications, or serverless functions
- Utilizing MLOps for managing the entire ML lifecycle, including version control, continuous integration/continuous delivery (CI/CD), and monitoring
Target Career Roles
- Entwickler und Data Scientists
02 / Lektionen & Labore
See exactly what you will learn and practice
Unterrichtsplan
18 Interaktive Lektionen · 93 topics01 Introduction 3 topics +
- The AWS Certified Machine Learning Specialty Exam
- Study Guide Features
- AWS Certified Machine Learning Specialty Exam Objectives
02 AWS AI ML Stack 16 topics · 4 LiveLab +
- Amazon Rekognition
- Amazon Textract
- Amazon Transcribe
- Amazon Translate
- Amazon Polly
- Amazon Lex
- Amazon Kendra
- Amazon Personalize
- Amazon Forecast
- Amazon Comprehend
- Amazon CodeGuru
- Amazon Augmented AI
- Amazon SageMaker
- AWS Machine Learning Devices
- Summary
- Exam Essentials
4 LiveLab in this lesson — see the labs panel →
03 Supporting Services from the AWS Stack 7 topics · 2 LiveLab +
- Storage
- Amazon VPC
- AWS Lambda
- AWS Step Functions
- AWS RoboMaker
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
04 Business Understanding 4 topics +
- Phases of ML Workloads
- Business Problem Identification
- Summary
- Exam Essentials
05 Framing a Machine Learning Problem 4 topics +
- ML Problem Framing
- Recommended Practices
- Summary
- Exam Essentials
06 Data Collection 5 topics · 2 LiveLab +
- Basic Data Concepts
- Data Repositories
- Data Migration to AWS
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
07 Data Preparation 3 topics · 2 LiveLab +
- Data Preparation Tools
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
08 Feature Engineering 4 topics +
- Feature Engineering Concepts
- Feature Engineering Tools on AWS
- Summary
- Exam Essentials
09 Model Training 9 topics · 4 LiveLab +
- Common ML Algorithms
- Local Training and Testing
- Remote Training
- Distributed Training
- Monitoring Training Jobs
- Debugging Training Jobs
- Hyperparameter Optimization
- Summary
- Exam Essentials
4 LiveLab in this lesson — see the labs panel →
10 Model Evaluation 4 topics +
- Experiment Management
- Metrics and Visualization
- Summary
- Exam Essentials
11 Model Deployment and Inference 5 topics · 1 LiveLab +
- Deployment for AI Services
- Deployment for Amazon SageMaker
- Advanced Deployment Topics
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
12 Application Integration 5 topics · 2 LiveLab +
- Integration with On-Premises Systems
- Integration with Cloud Systems
- Integration with Front-End Systems
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
13 Operational Excellence Pillar for ML 3 topics · 1 LiveLab +
- Operational Excellence on AWS
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
14 Security Pillar 5 topics · 4 LiveLab +
- Security and AWS
- Secure SageMaker Environments
- AI Services Security
- Summary
- Exam Essentials
4 LiveLab in this lesson — see the labs panel →
15 Reliability Pillar 5 topics · 2 LiveLab +
- Reliability on AWS
- Change Management for ML
- Failure Management for ML
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
16 Performance Efficiency Pillar for ML 3 topics · 1 LiveLab +
- Performance Efficiency for ML on AWS
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
17 Cost Optimization Pillar for ML 4 topics +
- Common Design Principles
- Cost Optimization for ML Workloads
- Summary
- Exam Essentials
18 Recent Updates in the AWS AI/ML Stack 4 topics · 1 LiveLab +
- New Services and Features Related to AI Services
- New Features Related to Amazon SageMaker
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
Praktische Übungen Our edge
26 LiveLabs- Detecting Objects in an Image
- Using Amazon Translate
- Using Amazon Transcribe and Polly
- Using Amazon SageMaker
- Creating an AWS Lambda Function
- Using Step Functions
- Creating an Amazon DynamoDB Table
- Creating a Kinesis Firehose Delivery Stream
- Using Amazon Athena
- Using AWS Glue
- Performing the K-Means Clustering
- Creating Amazon EventBridge Rules that React to Events
- Creating a CloudWatch Dashboard and Adding a Metric to it
- Creating CloudTrail
- Deploying an ML Model Using AWS SageMaker
- Creating an AWS Backup
- Creating a Model
- Enabling Versioning in the Amazon S3 Bucket
- Using Amazon EC2
- Configuring a Key
- Using Amazon SageMaker Notebook Instance
- Attaching an AWS IAM Role to an Instance
- Understanding Production Security
- Creating an Auto Scaling Group
- Creating an Amazon EFS
- Creating an Amazon Redshift Cluster
Labore laufen direkt im Browser — nichts zu installieren.
03 / Prüfungsdetails
AWS Certified Machine Learning Specialty Training (MLS-C01) Details
Erwerben Sie die erforderlichen Kenntnisse, um die AWS ML Specialty-Prüfung mit dem AWS Certified Machine Learning Study Guide: Specialty (MLS-C01) Kurs und Lab zu bestehen. Das Lab bietet Ihnen praktische Lernerfahrungen im Bereich Machine Learning in einer sicheren Online-Umgebung. Ziel dieses Kurses ist es, Ihnen die Konzepte und Prinzipien des maschinellen Lernens zu vermitteln, um die AWS Certified Machine Learning Specialty-Prüfung erfolgreich zu bestehen. Dieser Kurs richtet sich an Fachkräfte, die im Bereich Data Science und Machine Learning tätig sind.
Questions
65
(pro Prüfung)
Duration
180 Minuten
(pro Prüfung)
Passing Score
750
(auf einer Skala von 1000)
Exam Fee
USD 300
(pro Prüfung)
Delivered by
PSI or Pearson VUE
(vor Ort oder online)
Question Format
Multiple choice and multiple response
Einzel-/Mehrfachauswahl, leistungsbasiert
Übungstests
403 Übungsfragen
(abgestimmt auf die offiziellen Prüfungsziele)
Zertifizierung
MLS-C01.AE1
Amazon Zertifizierung
Bereit für die Prüfung?
Fügen Sie Ihren offiziellen MLS-C01.AE1 Prüfungsgutschein zu Ihrer Bestellung hinzu.
Offizieller Gutschein · Schnelle Lieferung · Wiederholungspaket verfügbar
Der Prüfungsgutschein wird separat verkauft und ist im Standardkurs nicht enthalten.
04 / FAQS
Fragen, bevor Sie beginnen
What is the AWS Machine Learning certification? +
MLS-C01 is a certification exam conducted by AWS. It validates your skills for designing, building, training, and deploying machine learning (ML) models on the AWS platform. It is suitable for all those with some ML experience wanting to demonstrate their expertise in using AWS services.
What are the prerequisites for this course? +
There are no formal prerequisites for the AWS Certified Machine Learning Specialty certification. However it’s an advanced-level course that requires a strong foundation in ML Learning concepts. To boost your understanding and speed up the learning process, you can do some base-level courses like AWS Certified Cloud Practitioner & AWS Certified Developer - Associate.
Does this course cover advanced ML concepts? +
Yes, it covers the latest updates in ML learning concepts and provides a comprehensive coverage on several advanced topics. Some of the advanced ML topics include Deep Learning, Reinforcement Learning, Generative Models, Transfer Learning, Time Series Analysis, and Computer Vision.
What is the format of the AWS MLS-C01 exam? +
The exam has 50 questions in total. It has multiple choice and multiple response questions.
How much does the AWS Certified Machine Learning – Specialty exam cost? +
The certification exam price is $300.
What are the professional benefits of earning the AWS certification? +
This AWS certification offers many benefits like career advancement, technical proficiency, credibility, and a competitive advantage, making it a valuable asset for you. If you want to advance your careers in machine learning and cloud computing this can be the ideal starting point for you.
What is the salary range of an AWS certified ML professional? +
The expected annual salary range of an AWS certified professional is anywhere between 136,500 USD to 198,500 USD.
Welche Voraussetzungen müssen für diese Prüfung erfüllt sein?+
Bevor Sie diese Prüfung ablegen, wird Folgendes empfohlen:
- Mindestens zwei Jahre praktische Erfahrung in der Entwicklung, Architektur und dem Betrieb von ML- oder Deep-Learning-Workloads in der AWS Cloud
- Die Fähigkeit, die Intuition hinter grundlegenden ML-Algorithmen auszudrücken
- Erfahrung in der Durchführung grundlegender Hyperparameteroptimierung
- Erfahrung mit ML- und Deep-Learning-Frameworks
- Fähigkeit, bewährte Verfahren für Modellschulung, -bereitstellung und -betrieb zu befolgen
Wie hoch ist die Prüfungsgebühr?+
300 USD
Wo kann ich die Prüfung ablegen?+
PSI oder Pearson VUE
Wie ist die Prüfung aufgebaut?+
Multiple-Choice- und Mehrfachantwort-System
Wie viele Fragen umfasst die Prüfung?+
Die Prüfung umfasst 65 Fragen.
Wie lange dauert die Prüfung?+
180 Minuten
Welche Punktzahl ist zum Bestehen erforderlich?+
750
(auf einer Skala von 100-1000)
Wie lauten die Regelungen zur Wiederholung der Prüfung?+
Falls Sie eine AWS-Zertifizierungsprüfung nicht bestehen, können Sie die Prüfung unter folgenden Bedingungen wiederholen:
- Sie müssen 14 Tage ab dem Tag, an dem Sie die Prüfung nicht bestanden haben, warten, bevor Sie sie erneut ablegen können.
- Die Kandidaten müssen die Prüfungsgebühr jedes Mal bezahlen, wenn sie die Prüfung ablegen.
Welche Gültigkeit hat die Zertifizierung?+
Normalerweise drei Jahre
Wo finde ich weitere Informationen zu dieser Prüfung?+
Keep going
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Invest In Your Future. Invest In This AWS Course.
Upscale your professional journey to the next level with this MLS-C01 prep course.
- 1 Jahr voller Zugang
- 26 LiveLab enthalten
- Abschlusszertifikat
Keine Kreditkarte benötigt