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Course Outline
Introduction to AWS and its AI/ML Service Portfolio
Establishing the AWS Environment
- Creation and administration of an AWS account.
- Navigating the AWS Management Console.
- Configuration of AWS CLI and SDKs.
Surveying AWS AI/ML Services
- Examination of Amazon SageMaker, AWS Deep Learning AMIs, and broader AWS AI Services.
- Exploration of real-world AI/ML applications on AWS.
- Analysis of case studies and industry-specific examples.
Deep Dive into Amazon SageMaker
- Fundamental overview of Amazon SageMaker.
- Utilization of SageMaker Studio and notebook instances.
- Key capabilities and operational features.
- Data importation and processing workflows in SageMaker.
- Techniques for feature engineering and data cleansing.
Training and Optimizing Models
- Setup and configuration of training jobs.
- Application of built-in algorithms and custom scripts.
- Strategies for hyperparameter tuning.
- Debugging methods and performance profiling of training jobs.
Deploying and Managing Models
- Creation and setup of endpoints.
- Ongoing model monitoring and lifecycle management.
- Advanced deployment methodologies.
- Implementation of multi-model endpoints.
- Execution of A/B testing and blue/green deployment strategies.
AWS AI Services for Targeted Applications
- Overview of Amazon Rekognition.
- Analysis of image and video data.
- Services for text-to-speech conversion and speech-to-text transcription.
- Integration of Polly and Transcribe into application architectures.
Advanced AI Services within the AWS Ecosystem
- Introduction to Amazon Comprehend and Amazon Lex.
- Services focused on natural language processing and chatbot development.
- Construction and deployment of chatbots using Lex.
- Utilization of Amazon Translate and Amazon Forecast.
- Language translation tools and time-series forecasting capabilities.
- Practical applications and scenario-based use cases.
Summary and Future Directions
Requirements
- A foundational grasp of AI/ML concepts.
- Familiarity with core AWS principles.
- Programming proficiency in Python.
Target Audience
- Data scientists.
- Machine learning engineers.
- Enthusiasts of AI technologies.
- IT professionals.
14 Hours
Testimonials (1)
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