शुरू करने से पहले आपको क्या जानना चाहिए
आप शुरू करें

शुरू होता है 4 June 2026 02:17

समाप्त होता है 4 June 2026

00 दिन
00 घंटे
00 मिनट
00 सेकंड
course image

AWS Certified Machine Learning Engineer - Associate (MLA-C01): ML Model Development

Explore AWS machine learning services, from purpose-built AI solutions to SageMaker algorithms, and learn to monitor and adjust models during training for the MLA-C01 certification.
via Pluralsight

659 कोर्स


2 hours 44 minutes

वैकल्पिक अपग्रेड उपलब्ध है

Not Specified

अपनी गति से आगे बढ़ें

Free Trial Available

वैकल्पिक अपग्रेड उपलब्ध है

अवलोकन

AWS has a broad range of machine learning services to help businesses approach complex problems. In this course, AWS Certified Machine Learning Engineer - Associate (MLA-C01):

ML Model Development, you’ll learn to navigate these services and select the approach that is most appropriate for your machine learning (ML) solutions.

First, you’ll explore AWS managed ML and AI services that are purpose-built for specific use cases. Next, you’ll discover built-in algorithms to tackle more complex ML challenges using Amazon SageMaker.

Finally, you’ll learn how to monitor your models and make adjustments throughout training. When you’re finished with this course, you’ll have the skills and knowledge of machine learning engineering on AWS needed to pass this domain of the exam.

पाठ्यक्रम

  • Introduction to AWS Machine Learning Services
  • Overview of AWS Machine Learning Offerings
    Identifying Suitable AWS Services for ML Solutions
  • AWS Managed ML and AI Services
  • Amazon Rekognition
    Amazon Comprehend
    Amazon Polly
    Amazon Lex
    Use Cases and Examples
  • Amazon SageMaker
  • Setting Up Amazon SageMaker Environment
    SageMaker Studio and Notebooks
    Built-in Algorithms
    Linear Learner
    XGBoost
    K-Means Clustering
    Hyperparameter Optimization
  • Building and Training ML Models
  • Data Preparation and Processing
    Feature Engineering on AWS
    Training Models in SageMaker
    Distributed Training with SageMaker
  • Model Evaluation and Deployment
  • Evaluating Model Performance
    A/B Testing and Model Validation
    Deploying Models with Amazon SageMaker Endpoints
  • Monitoring and Maintenance
  • Model Monitoring with Amazon SageMaker Model Monitor
    Automating Retraining and Deployment with Pipelines
    Handling Model Drift and Retraining
  • Exam Preparation
  • Review of Key Concepts
    Exam Strategies and Practice Questions
    Additional Resources for Study
  • Conclusion and Next Steps
  • Summary of AWS ML Services
    Advanced Learning Paths in AWS Machine Learning

द्वारा पढ़ाया गया

David Blocher


विषय

Programming