Training: AWS AI/ML
Level
IntermediateDuration
16h / 2 daysDate
Individually arrangedPrice
Individually arrangedTraining: AWS AI/ML
The AWS AI/ML training is an intensive two-day course focused on the practical use of AWS services for building, training, and deploying machine learning models. The program is designed so that 80% of the time is dedicated to hands-on workshops and 20% to theory. Participants will learn how to effectively use tools such as Amazon SageMaker, Amazon Bedrock, and other AWS AI/ML services by working on real-world examples and use cases.
Required technical skills
- Basic knowledge of Python programming
- Basic understanding of machine learning
- Experience with cloud services (an advantage, but not mandatory)
What will you learn?
- How to configure the AWS environment and manage access to AI/ML services
- How to perform exploratory data analysis and train ML models with Amazon SageMaker and Autopilot
- How to deploy ML models on SageMaker Endpoint and monitor their performance
- How to integrate ML models with other AWS services such as Lambda, Rekognition, and Comprehend
Who is this training for?
Developers and data engineers who want to expand their skills in machine learning on AWS-
Data scientists and data analysts seeking to train and deploy ML models in production environments
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IT specialists who want to use AWS AI/ML to automate data processing and predictions
Training Program
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Day 1: Introduction to AWS AI/ML and Predictive AI
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Fundamentals of AWS AI/ML
- Introduction to the AWS ecosystem and its AI/ML services
- Configuring the AWS environment and managing access
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Data preparation and exploratory data analysis (EDA)
- Importing and processing data in AWS S3
- Conducting EDA using AWS Glue and AWS Data Wrangler
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Training models in Amazon SageMaker
- Introduction to Amazon SageMaker
- Automating model training with SageMaker Autopilot
- Deploying and monitoring models via SageMaker Endpoint
- Workshop: Training the first model – hands-on exercises with a real dataset
- Model evaluation and results analysis
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Day 2: Advanced Techniques and Practical Applications with Generative AI
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Introduction to Generative AI
- Overview of Amazon Bedrock and foundation models for generative AI
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Advanced GenAI techniques
- Customizing foundation models
- Using RAG (Retrieval-Augmented Generation) to enhance GenAI intelligence
- Integrating GenAI with external systems – GenAI agents in action
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Security in the world of GenAI
- Applying guardrails as protective mechanisms for AI models
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Workshop: Generative AI in Practice
- Hands-on exercises with Amazon Bedrock and Large Language Models (LLMs)
