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This course is designed to introduce generative artificial intelligence (AI) to software developers interested in using large language models (LLMs) without fine-tuning. The course provides an overview of generative AI, planning a generative AI project, getting started with Amazon Bedrock, the foundations of prompt engineering, and the architecture patterns to build generative AI applications using Amazon Bedrock and LangChain.

Activities

This course includes presentations, demonstrations, and group exercises

Describe generative AI and how it aligns to machine learning
Define the importance of generative AI and explain its potential risks and benefits
Identify business value from generative AI use cases
Discuss the technical foundations and key terminology for generative AI
Explain the steps for planning a generative AI project
Identify some of the risks and mitigations when using generative AI
Understand how Amazon Bedrock works
Familiarize yourself with basic concepts of Amazon Bedrock
Recognize the benefits of Amazon Bedrock
List typical use cases for Amazon Bedrock
Describe the typical architecture associated with an Amazon Bedrock solution
Understand the cost structure of Amazon Bedrock
Implement a demonstration of Amazon Bedrock in the AWS Management Console
Define prompt engineering and apply general best practices when interacting with foundation models (FMs)
Identify the basic types of prompt techniques, including zero-shot and few-shot learning
Apply advanced prompt techniques when necessary for your use case
Identify which prompt techniques are best suited for specific models
Identify potential prompt misuses
Analyze potential bias in FM responses and design prompts that mitigate that bias
Identify the components of a generative AI application and how to customize an FM
Describe Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIs
Identify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications
Describe how to integrate LangChain with LLMs, prompt templates, chains, chat models, text embeddings models, document loaders, retrievers, and Agents for Amazon Bedrock
Describe architecture patterns that you can implement with Amazon Bedrock for building generative AI applications
Apply the concepts to build and test sample use cases that use the various Amazon Bedrock models, LangChain, and the Retrieval Augmented Generation (RAG) approach

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I have attended a training from Bilginc IT Academy before and I was satisfied.

I have attended a training from a different provider and it was not helpful.

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How many employees do you have in your IT department?

0 – 50

50 – 250

250 – 1000

1000+

Upcoming Trainings

Join our public courses in our Australia facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

24 February 2025 (2 Days)
Melbourne, Sydney, Brisbane
Classroom / Virtual Classroom

26 February 2025 (2 Days)
Melbourne, Sydney, Brisbane
Classroom / Virtual Classroom

27 February 2025 (2 Days)
Melbourne, Sydney, Brisbane
Classroom / Virtual Classroom

06 March 2025 (2 Days)
Melbourne, Sydney, Brisbane
Classroom / Virtual Classroom

€2,775 +VAT
Book Now

24 February 2025 (2 Days)
Melbourne, Sydney, Brisbane
Classroom / Virtual Classroom

26 February 2025 (2 Days)
Melbourne, Sydney, Brisbane
Classroom / Virtual Classroom

27 February 2025 (2 Days)
Melbourne, Sydney, Brisbane
Classroom / Virtual Classroom

06 March 2025 (2 Days)
Melbourne, Sydney, Brisbane
Classroom / Virtual Classroom

€2,775 +VAT
Book Now

Developing Generative AI Applications on AWS Training Course in Australia

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We offer a wide range of IT courses, from cybersecurity, data science and software development to business skills and project management, and we can host training at your preferred location in Australia. Let our experienced instructors provide you with hands-on training and practical insights.

Developing Generative AI Applications on AWS Training in Australia

Outline

Upcoming Trainings