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Course Outline

Foundational overview of Amazon Web Services (AWS) and its artificial intelligence and machine learning capabilities

Establishing the AWS Infrastructure Environment

  • Account provisioning and lifecycle management
  • Navigating the AWS Management Console interface
  • Configuration of AWS Command Line Interface (CLI) and Software Development Kits (SDKs)

Strategic Landscape of AWS AI/ML Services

  • Examination of Amazon SageMaker, Deep Learning AMIs, and core AI services
  • Deployment of AI/ML solutions in operational contexts
  • Analysis of case studies and sector-specific applications

Amazon SageMaker Platform

  • Detailed introduction to the Amazon SageMaker environment
  • Utilization of SageMaker Studio and managed notebook instances
  • Review of core capabilities and system functionalities
  • Data ingestion and preprocessing workflows within SageMaker
  • Techniques for feature engineering and data hygiene

Model Training and Optimization Processes

  • Structuring and parameterizing training operations
  • Application of native algorithms and custom scripting
  • Implementation of hyperparameter tuning strategies
  • Troubleshooting and performance profiling of training jobs

Model Deployment and Operational Management

  • Configuration and provisioning of inference endpoints
  • Continuous monitoring and lifecycle management of deployed models
  • Advanced deployment methodologies
  • Architecture of multi-model endpoints
  • Execution of A/B testing and blue/green deployment patterns

Application-Specific AWS AI Services

  • Functional review of Amazon Rekognition
  • Computational analysis of image and video data
  • Implementation of voice recognition and synthesis capabilities
  • Integration of Amazon Polly and Transcribe into application stacks

Advanced AI Capabilities on AWS

  • Overview of Amazon Comprehend and Amazon Lex architectures
  • Natural language processing and conversational interface services
  • Development and deployment of chatbot solutions via Lex
  • Integration of Amazon Translate and Forecast services
  • Execution of language translation and time-series predictive analytics
  • Evaluation of practical implementations and operational use cases

Conclusions and Strategic Action Plan

Requirements

  • Foundational knowledge of AI and ML theoretical principles
  • Basic competency with core AWS concepts and terminology
  • Proficiency in Python programming

Target Audience

  • Data scientists engaged in analytical workflows
  • Machine learning engineers focused on model implementation
  • AI practitioners and subject matter experts
  • IT professionals supporting digital infrastructure
 14 Hours

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