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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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