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Starts 7 July 2025 05:25

Ends 7 July 2025

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Build Machine Learning Models Using Natural Language with Amazon Q and SageMaker

Discover how Amazon Q Developer and SageMaker simplify machine learning model development through natural language interactions, streamlining the workflow for data scientists.
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Overview

Discover how Amazon Q Developer and SageMaker simplify machine learning model development through natural language interactions, streamlining the workflow for data scientists.

Syllabus

  • Introduction to Amazon Q Developer and SageMaker
  • Overview of Amazon Q Developer
    Overview of Amazon SageMaker
    Benefits of natural language interactions in machine learning
  • Setting Up Your Environment
  • Prerequisites and setup requirements
    Accessing Amazon Q Developer
    Navigating Amazon SageMaker
  • Understanding Natural Language Interfaces
  • Basics of natural language processing in machine learning
    How Amazon Q Developer leverages NLP for model creation
    Example use cases of natural language interfaces in data science
  • Data Preparation and Management
  • Importing and managing data in SageMaker
    Using natural language to query and preprocess datasets
    Data labeling and augmentation techniques
  • Building Machine Learning Models with Natural Language
  • Designing model structure with Amazon Q
    Implementing algorithms through natural language commands
    Customizing model parameters using natural language
  • Training and Evaluating Models
  • Setting up training with SageMaker
    Monitoring training jobs via natural language commands
    Evaluating model performance with built-in SageMaker tools
  • Deployment and Inference
  • Deploying models using SageMaker's tools
    Performing inference through natural language requests
    Scaling and optimizing models for production use
  • Best Practices and Advanced Techniques
  • Improving model accuracy with advanced natural language techniques
    Automated model tuning and hyperparameter optimization
    Securing and managing machine learning workflows
  • Case Studies and Real-world Applications
  • Review of successful implementations of Amazon Q and SageMaker
    Discussion on challenges and solutions in natural language model development
  • Project: Developing a Machine Learning Solution
  • Define a problem statement using natural language
    Build, train, and deploy a machine learning model with SageMaker and Amazon Q
    Present and critique project outcomes
  • Conclusion and Next Steps
  • Recap of key learnings
    Opportunities for further learning and certification paths
    Open discussion on future advancements in natural language AI tools

Subjects

Data Science