Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to AWS and its AI/ML services.
Setting Up the AWS Environment.
- Creating and managing an AWS account.
- Introduction to the 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
- A foundational understanding of AI/ML concepts.
- Familiarity with the basics of AWS.
- Proficiency in Python programming.
Target 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
