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Course Outline
Introduction to AWS and its AI/ML services
Setting Up AWS Environment
- Creating and managing an AWS account
- Introduction to AWS Management Console
- Setting up AWS CLI and SDKs
Overview of AWS AI/ML Services
- Amazon SageMaker, AWS Deep Learning AMIs, and AWS AI Services
- Real-world applications of AI/ML on AWS
- Case studies and industry examples
Amazon SageMaker
- Introduction to Amazon SageMaker
- SageMaker Studio and notebook instances
- Key features and functionalities
- Importing and processing data in SageMaker
- Feature engineering and data cleaning
Model Training and Tuning
- Creating and configuring training jobs
- Using built-in algorithms and custom scripts
- Hyperparameter tuning
- Debugging and profiling training jobs
Model Deployment and Management
- Endpoint creation and configuration
- Model monitoring and management
- Advanced Deployment Techniques
- Multi-model endpoints
- A/B testing and blue/green deployments
AWS AI Services for Specific Use Cases
- Amazon Rekognition
- Image and video analysis
- Text-to-speech and speech-to-text services
- Integrating Polly and Transcribe into applications
Advanced AI Services on AWS
- Overview of Amazon Comprehend and Lex
- Natural language processing and chatbot services
- Building and deploying chatbots with Lex
- Amazon translate and forecast
- Language translation and time-series forecasting
- Practical applications and use cases
Summary and Next Steps
Requirements
- Basic understanding of AI/ML concepts
- Familiarity with AWS basics
- Programming knowledge in Python
Audience
- Data scientists
- Machine learning engineers
- AI enthusiasts
- IT professionals
14 Hours
Testimonials (1)
I've find out new interesting things about Lambda and Serverless
