AWS Generative AI and AI Agents with Amazon Bedrock Professional Certificate

AWS Generative AI and AI Agents with Amazon Bedrock Professional Certificate

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Develop Generative AI Solutions on AWS. Use Amazon Bedrock, Amazon Q Developer, and LangChain to build applications using Generative AI

What you’ll learn

  • Build and deploy generative AI applications using Amazon Bedrock, integrating foundation models for text, language, and summarization tasks
  • Develop generative AI agents and knowledge bases to automate complex tasks and improve decision-making processes in enterprise applications
  • Optimize generative AI model performance through fine-tuning, evaluation jobs, and efficient deployment techniques like prompt caching and routing

Skills you’ll gain

  • LLM Application
  • Context Management
  • Retrieval-Augmented Generation
  • Responsible AI
  • Applied Machine Learning
  • Model Evaluation
  • Artificial Intelligence and Machine Learning (AI/ML)
  • Embeddings
  • Large Language Modeling
  • Transfer Learning
  • Generative AI Agents

Tools you’ll learn

  • LangChain
  • Prompt Engineering
  • AI Orchestration
  • AI Workflows
  • Amazon Bedrock
  • Amazon S3
  • Generative AI
  • Model Deployment
  • Amazon Web Service

Start on a journey into the world of generative AI with this AWS Professional Certificate program. Designed for software developers and DevOps engineers, this program will arm you with the skills to use AWS’ generative AI services, particularly Amazon Bedrock, to create generative AI applications.

Throughout this program, you’ll gain hands-on experience with language models and foundation models, learning how to use them for text generation, language understanding, and summarization tasks. You’ll learn Amazon Bedrock, exploring its powerful features including Knowledge Bases, Agents, and Guardrails.

The course outline takes you from fundamental concepts to advanced implementation strategies. You’ll start by understanding the basics of generative AI and how to access foundation models through Amazon Bedrock. As you continue, you’ll learn to create AI-driven agents for task automation, implement knowledge bases for information retrieval, and optimize AI model performance through techniques like fine-tuning and prompt engineering.

A big focus of this program is practical application. You’ll work on projects that simulate enterprise-level challenges, learning to integrate AI capabilities into existing software architectures. You’ll also explore Amazon Q Developer, gaining insights into how generative AI can enhance your development workflow.

By the end of this Professional Certificate, you’ll be ready to design, develop, and deploy generative AI solutions on AWS.

Applied Learning Project

The included projects provide learners with hands-on experience using Amazon Bedrock and Amazon Q Developer to build, deploy, and manage generative AI applications. By applying skills such as prompt engineering, multi-turn conversation handling, tool integration, and content safety enforcement, learners solve authentic development challenges like debugging real code, securing AI outputs, and creating intelligent agents for practical use cases.

Please note: The following hands-on exercises are optional and require access to your own AWS account. Completing these activities may result in minimal usage charges.

Table of Contents

amazon-bedrock-customization-optimization-automation

module-1-improving-foundation-model-results

introduction-to-the-course
1 introduction-to-the-course
2 course-roadmap_instructions
3 tech-talk-langchain
4 langchain_instructions

model-customization
5 fine-tuning-models
6 continued-pre-training
7 model-distillation
8 customizing-a-model-with-amazon-bedrock_custom-models
9 customizing-a-model-with-amazon-bedrock_instructions
10 customizing-a-model-with-amazon-bedrock_model-distillation

module-2-using-foundation-models-for-efficiency

model-assessment-and-efficiency
11 amazon-bedrock-evaluation-jobs
12 prompt-caching
13 exercise-prompt-caching-with-amazon-bedrock_Welcome
14 exercise-prompt-caching-with-amazon-bedrock_instructions
15 exercise-prompt-caching-with-amazon-bedrock_prompt-caching
16 prompt-routing
17 model-assessment-and-efficiency_evaluation
18 model-assessment-and-efficiency_instructions
19 model-assessment-and-efficiency_prompt-caching

automation
20 bedrock-data-automation
21 automation-with-amazon-bedrock_API_Operations_Data_Automation_for_Amazon_Bedrock
22 automation-with-amazon-bedrock_instructions
23 exercise-amazon-bedrock-data-automation_Welcome
24 exercise-amazon-bedrock-data-automation_bda
25 exercise-amazon-bedrock-data-automation_instructions
26 demo-amazon-q-developer-cloudwatch-logs-analysis
27 demo-amazon-q-cli
28 tech-talk-amazon-bedrock-for-generative-ai-wrap-up-part-1
29 tech-talk-amazon-bedrock-for-generative-ai-wrap-up-part-2
30 glossary-course-3_instructions
31 post-course-survey_instructions

generative-ai-applications-amazon-bedrock

module-1-knowledge-bases-and-workflows

introduction-to-the-course
32 introduction-to-the-course
33 course-roadmap_instructions

amazon-bedrock-knowledge-bases
34 introduction-to-amazon-bedrock-knowledge-bases
35 demonstration-of-amazon-bedrock-knowledge-bases
36 amazon-bedrock-knowledge-bases_instructions
37 amazon-bedrock-knowledge-bases_knowledge-base

amazon-bedrock-prompt-management-and-flows
38 introduction-to-amazon-bedrock-prompt-management
39 prompt-management_API_runtime_Converse
40 prompt-management_instructions
41 prompt-management_welcome
42 exercise-prompt-management-with-amazon-bedrock_Welcome
43 exercise-prompt-management-with-amazon-bedrock_converse
44 exercise-prompt-management-with-amazon-bedrock_index
45 exercise-prompt-management-with-amazon-bedrock_instructions
46 exercise-prompt-management-with-amazon-bedrock_overview
47 exercise-prompt-management-with-amazon-bedrock_prompt-engineering
48 exercise-prompt-management-with-amazon-bedrock_prompt-management-deploy
49 exercise-prompt-management-with-amazon-bedrock_prompt-management-optimize
50 converse-api-and-tool-use-part-1
51 converse-api-and-tool-use-part-2
52 exercise-working-with-the-amazon-bedrock-api_Welcome
53 exercise-working-with-the-amazon-bedrock-api_instructions
54 exercise-working-with-the-amazon-bedrock-api_tool-use
55 converse-apis_API_runtime_Converse
56 converse-apis_instructions
57 converse-apis_welcome
58 introduction-to-amazon-bedrock-flows
59 amazon-bedrock-flows_flows
60 amazon-bedrock-flows_instructions

module-2-ai-driven-agents-for-task-automation

agents
61 amazon-bedrock-agents
62 demonstration-of-amazon-bedrock-agents
63 amazon-bedrock-agents_API_Operations_Agents_for_Amazon_Bedrock
64 amazon-bedrock-agents_agents
65 amazon-bedrock-agents_instructions
66 amazon-bedrock-agents_service_code_examples_bedrock-agent
67 exercise-creating-an-hr-assistant-agent-using-amazon-bedrock-agents_agents
68 exercise-creating-an-hr-assistant-agent-using-amazon-bedrock-agents_bedrock
69 exercise-creating-an-hr-assistant-agent-using-amazon-bedrock-agents_create_agent_action_group
70 exercise-creating-an-hr-assistant-agent-using-amazon-bedrock-agents_instructions

amazon-q-developer
71 amazon-q-developer-security-scanning
72 demo-amazon-q-developer-transform-agent
73 tech-talk-using-agents-knowledge-bases-and-converse-api

course-wrap-up
74 glossary-course-2_instructions
75 post-course-survey_instructions

getting-started-aws-generative-ai-developers

module-1-what-is-generative-ai

introduction-to-the-course
76 introduction-to-the-course
77 course-roadmap_instructions

generative-ai-on-aws
78 amazon-bedrock-for-generative-ai
79 invoking-an-amazon-bedrock-foundation-model
80 amazon-bedrock_bedrock
81 amazon-bedrock_instructions
82 amazon-bedrock_welcome
83 amazon-bedrock_what-is-bedrock
84 exercise-invoking-an-amazon-bedrock-foundation-model_Welcome
85 exercise-invoking-an-amazon-bedrock-foundation-model_index
86 exercise-invoking-an-amazon-bedrock-foundation-model_instructions
87 amazon-q-developer
88 demo-amazon-q-developer-cli-vibe-coding
89 demo-amazon-q-developer-on-github
90 amazon-q-developer_instructions
91 amazon-q-developer_what-is
92 exercise-debug-and-generate-code-with-amazon-q-developer_amazon-q-for-github
93 exercise-debug-and-generate-code-with-amazon-q-developer_command-line
94 exercise-debug-and-generate-code-with-amazon-q-developer_create-aws_builder_id
95 exercise-debug-and-generate-code-with-amazon-q-developer_instructions
96 exercise-debug-and-generate-code-with-amazon-q-developer_what-is

module-2-accessing-amazon-bedrock-foundation-models

application-integration
97 accessing-amazon-bedrock-runtime-apis
98 asynchronous-and-batch-inference
99 demo-amazon-bedrock-samples-repository
100 amazon-q-developer-bedrock-apis_API_Operations_Amazon_Bedrock_Runtime
101 amazon-q-developer-bedrock-apis_instructions
102 amazon-q-developer-bedrock-apis_quotas
103 amazon-q-developer-bedrock-apis_welcome
104 adding-guardrails-for-inputs-and-responses
105 exercise-amazon-bedrock-guardrails_Welcome
106 exercise-amazon-bedrock-guardrails_guardrails
107 exercise-amazon-bedrock-guardrails_instructions
108 responsible-ai_instructions
109 responsible-ai_security

working-with-foundation-models
110 choosing-a-foundation-model
111 foundation-models_foundation-models-reference
112 foundation-models_instructions
113 foundation-models_models-supported
114 prompt-engineering
115 prompt-engineering-guide_instructions
116 demo-amazon-q-developer-dev-agent
117 demo-amazon-q-developer-dev-agent-feature-dev
118 demo-amazon-q-developer-documentation-agent
119 tech-talk-whats-possible-with-generative-ai

course-wrap-up
120 glossary-course-1_instructions
121 post-course-survey_instructions

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