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Duration 14 hours
Course Outline
Introduction to AWS and its AI/ML service offerings
Establishing the AWS Environment
- Account creation and management
- Navigating the AWS Management Console
- Configuration of AWS CLI and SDKs
Survey of AWS AI/ML Services
- Examination of Amazon SageMaker, AWS Deep Learning AMIs, and core AWS AI Services
- Real-world implementations of AI/ML on AWS
- Analysis of case studies and industry examples
Deep Dive into Amazon SageMaker
- Overview of Amazon SageMaker
- Utilizing SageMaker Studio and notebook instances
- Exploration of key features and functionalities
- Data import and processing workflows in SageMaker
- Techniques for feature engineering and data cleaning
Training and Tuning Models
- Establishment and configuration of training jobs
- Application of built-in algorithms and custom scripts
- Implementation of hyperparameter tuning
- Debugging and profiling of training processes
Deploying and Managing Models
- Creation and setup of endpoints
- Strategies for model monitoring and administration
- Advanced deployment methodologies
- Configuration of multi-model endpoints
- Execution of A/B testing and blue/green deployments
AWS AI Services for Specific Applications
- Introduction to Amazon Rekognition
- Analysis of images and video content
- Utilization of text-to-speech and speech-to-text services
- Integration of Polly and Transcribe into applications
Advanced AWS AI Services
- Overview of Amazon Comprehend and Lex
- Natural language processing and chatbot capabilities
- Construction and deployment of chatbots using Lex
- Application of Amazon Translate and Forecast
- Language translation and time-series forecasting techniques
- Practical applications and use-case demonstrations
Recap and Future Directions
Requirements
- A foundational grasp of AI/ML principles
- Basic familiarity with AWS core concepts
- Programming proficiency in Python
Target Audience
- Data scientists
- Machine learning engineers
- AI enthusiasts
- IT professionals
Testimonials (1)
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