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Course Outline
Introduction to AWS and its AI/ML service offerings
Configuring the AWS Environment
- Creating and managing an AWS account
- Overview of the AWS Management Console
- Setting up AWS CLI and SDKs
Landscape of AWS AI/ML Services
- Amazon SageMaker, AWS Deep Learning AMIs, and other AWS AI Services
- Real-world implementations of AI/ML on AWS
- Case studies and industry-specific examples
Working with Amazon SageMaker
- Introduction to the Amazon SageMaker platform
- Utilizing SageMaker Studio and notebook instances
- Key features and core functionalities
- Data import and processing workflows in SageMaker
- Feature engineering and data cleansing techniques
Model Training and Optimization
- Creating and configuring training jobs
- Implementing built-in algorithms and custom scripts
- Hyperparameter tuning strategies
- Debugging and profiling training processes
Model Deployment and Lifecycle Management
- Endpoint creation and configuration
- Ongoing model monitoring and management
- Advanced deployment methodologies
- Managing multi-model endpoints
- A/B testing and blue/green deployment strategies
AWS AI Services for Specific Applications
- Amazon Rekognition
- Image and video analysis capabilities
- Text-to-speech and speech-to-text services
- Integrating Polly and Transcribe into applications
Advanced AI Services on the AWS Platform
- Overview of Amazon Comprehend and Amazon Lex
- Natural language processing and chatbot services
- Building and deploying chatbots using Lex
- Amazon Translate and Amazon Forecast
- Language translation and time-series forecasting techniques
- Practical applications and real-world use cases
Course Summary and Future Directions
Requirements
- Foundational understanding of AI/ML concepts
- Basic familiarity with AWS services
- Programming proficiency in Python
Target Audience
- Data scientists
- Machine learning engineers
- AI enthusiasts
- IT professionals
14 Hours
Testimonials (1)
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