{"id":10744,"date":"2024-03-11T12:00:43","date_gmt":"2024-03-11T11:00:43","guid":{"rendered":"https:\/\/www.tecracer.com\/?p=10744"},"modified":"2025-01-20T09:51:32","modified_gmt":"2025-01-20T08:51:32","slug":"developing-generative-ai-applications-on-aws","status":"publish","type":"post","link":"https:\/\/www.tecracer.com\/en\/training-en\/developing-generative-ai-applications-on-aws\/","title":{"rendered":"Developing Generative AI Applications on AWS"},"content":{"rendered":"<hr>\n<ul>\n<li>\n<p>        <img decoding=\"async\" src=\"\/wp-content\/uploads\/2022\/03\/AWS_advanced_tier_consulting_partner_badge.png\" alt=\"\"><\/p>\n<h3>Aws Advanced Training Partner<\/h3>\n<\/li>\n<li>\n<p>        <img decoding=\"async\" src=\"\/wp-content\/uploads\/2022\/03\/AWS_premier_tier_training_partner_badge.png\" alt=\"\"><\/p>\n<h3>Aws Premium Consuting Partner<\/h3>\n<\/li>\n<li>\n<p>        <img decoding=\"async\" src=\"\/wp-content\/uploads\/2023\/02\/200-Cert-Light-English-1.png\" alt=\"\"><\/p>\n<h3>200 Cert<\/h3>\n<\/li>\n<\/ul>\n<p><img decoding=\"async\" src=\"\/wp-content\/uploads\/2022\/01\/aws_training.jpg\" alt=\"\"><\/p>\n<div>\n<h1 data-id=\"page#0-0-0-0\" uk-scrollspy-class=\"\" class=\"uk-scrollspy-inview \">Developing Generative AI Applications on AWS<\/h1>\n<p><span>Please find our upcoming course dates at the end of this page!<\/span><\/p>\n<h2>Course description<\/h2>\n<p><span>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.<\/span><\/p>\n<p><img decoding=\"async\" class=\"size-full wp-image-1424 alignright\" src=\"http:\/\/www.tecracer.com\/wp-content\/uploads\/2022\/03\/AWS-Business-Essentials-150x150-1.png\" alt=\"\" width=\"150\" height=\"150\" \/><\/p>\n<h2>COURSE OBJECTIVES<\/h2>\n<p><strong>In this course, you will learn to:<\/strong><\/p>\n<ul>\n<li>Describe generative AI and how it aligns to machine learning<\/li>\n<li>Define the importance of generative AI and explain its potential risks and benefits<\/li>\n<li>Identify business value from generative AI use cases<\/li>\n<li>Discuss the technical foundations and key terminology for generative AI<\/li>\n<li>Explain the steps for planning a generative AI project<\/li>\n<li>Identify some of the risks and mitigations when using generative AI<\/li>\n<li>Understand how Amazon Bedrock works<\/li>\n<li>Familiarize yourself with basic concepts of Amazon Bedrock<\/li>\n<li>Recognize the benefits of Amazon Bedrock<\/li>\n<li>List typical use cases for Amazon Bedrock<\/li>\n<li>Describe the typical architecture associated with an Amazon Bedrock solution<\/li>\n<li>Understand the cost structure of Amazon Bedrock<\/li>\n<li>Implement a demonstration of Amazon Bedrock in the AWS Management Console<\/li>\n<li>Define prompt engineering and apply general best practices when interacting with foundation models (FMs)<\/li>\n<li>Identify the basic types of prompt techniques, including zero-shot and few-shot learning<\/li>\n<li>Apply advanced prompt techniques when necessary for your use case<\/li>\n<li>Identify which prompt techniques are best suited for specific models<\/li>\n<li>Identify potential prompt misuses<\/li>\n<li>Analyze potential bias in FM responses and design prompts that mitigate that bias<\/li>\n<li>Identify the components of a generative AI application and how to customize an FM<\/li>\n<li>Describe Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIs<\/li>\n<li>Identify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications<\/li>\n<li>Describe how to integrate LangChain with LLMs, prompt templates, chains, chat models, text<br \/>embeddings models, document loaders, retrievers, and Agents for Amazon Bedrock<\/li>\n<li>Describe architecture patterns that you can implement with Amazon Bedrock for building generative AI applications<\/li>\n<li>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<\/li>\n<\/ul>\n<h2>INTENDED AUDIENCE<\/h2>\n<p><strong>This course is intended for:<\/strong><\/p>\n<ul>\n<li>Software developers interested in using LLMs without fine-tuning<\/li>\n<\/ul>\n<h2>PREREQUISITES<\/h2>\n<p><strong>We recommend that attendees of this course have:<\/strong><\/p>\n<ul>\n<li>Completed <a href=\"https:\/\/www.tecracer.com\/en\/training-en\/amazon-aws-technical-essentials-training-details\/\">AWS Technical Essentials<\/a>\u00a0<\/li>\n<li>Intermediate-level proficiency in Python<\/li>\n<\/ul>\n<h2>ACTIVITIES<\/h2>\n<p><strong>This course includes:<\/strong><\/p>\n<ul>\n<li>Presentations<\/li>\n<li>Demonstrations<\/li>\n<li>Group exercises<\/li>\n<\/ul>\n<h2>COURSE DURATION \/ PRICE<\/h2>\n<ul>\n<li>2 days<\/li>\n<li>\u20ac 1,500.00 <span>(excl. tax) per person (DE)<\/span><\/li>\n<\/ul>\n<h2>Course outline<\/h2>\n<\/div>\n<ul>\n<li>\n<h3>Day 1<\/h3>\n<div>\n<ul>\n<li><strong>Module 1: Introduction to Generative AI \u2013 Art of the Possible<\/strong>\n<ul>\n<li>Overview of ML<\/li>\n<li>Basics of generative AI<\/li>\n<li>Generative AI use cases<\/li>\n<li>Generative AI in practice<\/li>\n<li>Risks and benefits<\/li>\n<\/ul>\n<\/li>\n<li><strong>Module 2: Planning a Generative AI Project<\/strong>\n<ul>\n<li>Generative AI fundamentals<\/li>\n<li>Generative AI in practice<\/li>\n<li>Generative AI context<\/li>\n<li>Steps in planning a generative AI project<\/li>\n<li>Risks and mitigation<\/li>\n<\/ul>\n<\/li>\n<li><strong>Module 3: Getting Started with Amazon Bedrock<\/strong>\n<ul>\n<li>Introduction to Amazon Bedrock<\/li>\n<li>Architecture and use cases<\/li>\n<li>How to use Amazon Bedrock<\/li>\n<li>Demonstration: Setting up Bedrock access and using playgrounds<\/li>\n<\/ul>\n<\/li>\n<li><strong>Module 4: Foundations of Prompt Engineering<\/strong>\n<ul>\n<li>Basics of foundation models<\/li>\n<li>Fundamentals of prompt engineering<\/li>\n<li>Basic prompt techniques<\/li>\n<li>Advanced prompt techniques<\/li>\n<li>Model-specific prompt techniques<\/li>\n<li>Demonstration: Fine-tuning a basic text prompt<\/li>\n<li>Addressing prompt misuses<\/li>\n<li>Mitigating bias<\/li>\n<li>Demonstration: Image bias mitigation<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/div>\n<\/li>\n<li>\n<h3>Day 2<\/h3>\n<div>\n<ul>\n<li><strong>Module 5: Amazon Bedrock Application Components<\/strong>\n<ul>\n<li>Overview of generative AI application components<\/li>\n<li>Foundation models and the FM interface<\/li>\n<li>Working with datasets and embeddings<\/li>\n<li>Demonstration: Word embeddings<\/li>\n<li>Additional application components<\/li>\n<li>Retrieval Augmented Generation (RAG)<\/li>\n<li>Model fine-tuning<\/li>\n<li>Securing generative AI applications<\/li>\n<li>Generative AI application architecture<\/li>\n<\/ul>\n<\/li>\n<li><strong>Module 6: Amazon Bedrock Foundation Models<\/strong>\n<ul>\n<li>Introduction to Amazon Bedrock foundation models<\/li>\n<li>Using Amazon Bedrock FMs for inference<\/li>\n<li>Amazon Bedrock methods<\/li>\n<li>Data protection and auditability<\/li>\n<li>Demonstration: Invoke Bedrock model for text generation using zero-shot prompt<\/li>\n<\/ul>\n<\/li>\n<li><strong>Module 7: LangChain<\/strong>\n<ul>\n<li>Optimizing LLM performance<\/li>\n<li>Using models with LangChain<\/li>\n<li>Constructing prompts<\/li>\n<li>Demonstration: Bedrock with LangChain using a prompt that includes context<\/li>\n<li>Structuring documents with indexes<\/li>\n<li>Storing and retrieving data with memory<\/li>\n<li>Using chains to sequence components<\/li>\n<li>Managing external resources with LangChain agents<\/li>\n<\/ul>\n<\/li>\n<li><strong>Module 8: Architecture Patterns<\/strong>\n<ul>\n<li>Introduction to architecture patterns<\/li>\n<li>Text summarization<\/li>\n<li>Demonstration: Text summarization of small files with Anthropic Claude<\/li>\n<li>Demonstration: Abstractive text summarization with Amazon Titan using LangChain<\/li>\n<li>Question answering<\/li>\n<li>Demonstration: Using Amazon Bedrock for question answering<\/li>\n<li>Chatbot<\/li>\n<li>Demonstration: Conversational interface \u2013 Chatbot with AI21 LLM<\/li>\n<li>Code generation<\/li>\n<li>Demonstration: Using Amazon Bedrock models for code generation<\/li>\n<li>LangChain and agents for Amazon Bedrock<\/li>\n<li>Demonstration: Integrating Amazon Bedrock models with LangChain agents<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/div>\n<\/li>\n<\/ul>\n<div>\n<p><strong>IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.<\/strong><\/p>\n<p>Course materials are in English, on request also in German (if available).<br \/>The Course language is German, on request also in English.<\/p>\n<\/div>\n<hr>\n<div>\n<h3>Neue Termine in Planung!<\/h3>\n<\/div>\n<p><!--more--><br \/>\n<!-- {\"name\":\"SingleAWSTraining\",\"type\":\"layout\",\"children\":[{\"type\":\"section\",\"props\":{\"animation\":\"fade\",\"animation_delay\":\"100\",\"image_position\":\"center-center\",\"style\":\"default\",\"title_breakpoint\":\"xl\",\"title_position\":\"top-left\",\"title_rotation\":\"left\",\"vertical_align\":\"middle\",\"width\":\"default\"},\"children\":[{\"type\":\"row\",\"props\":{\"layout\":\"2-5,3-5\"},\"children\":[{\"type\":\"column\",\"props\":{\"image_position\":\"center-center\",\"order_first\":\"m\",\"position_sticky_breakpoint\":\"m\",\"vertical_align\":\"middle\",\"width_medium\":\"2-5\"},\"children\":[{\"type\":\"headline\",\"props\":{\"title_element\":\"h1\"},\"source\":{\"query\":{\"name\":\"posts.singlePost\"},\"props\":{\"content\":{\"filters\":{\"search\":\"\"},\"name\":\"title\"}}}},{\"type\":\"divider\",\"props\":{\"divider_element\":\"hr\",\"divider_style\":\"small\"}},{\"type\":\"text\",\"props\":{\"column_breakpoint\":\"m\",\"margin\":\"default\",\"yooessentials_access_conditions\":[],\"yooessentials_access_mode\":\"AND\"},\"source\":{\"query\":{\"name\":\"posts.singlePost\"},\"props\":{\"content\":{\"filters\":{\"search\":\"\"},\"name\":\"field.kursbeschreibung\"}}}},{\"type\":\"grid\",\"props\":{\"content_column_breakpoint\":\"m\",\"filter_align\":\"left\",\"filter_all\":true,\"filter_grid_breakpoint\":\"m\",\"filter_grid_width\":\"auto\",\"filter_position\":\"top\",\"filter_style\":\"tab\",\"grid_default\":\"2\",\"grid_medium\":\"\",\"grid_small\":\"4\",\"icon_width\":80,\"image_align\":\"top\",\"image_grid_breakpoint\":\"m\",\"image_grid_width\":\"1-2\",\"image_svg_color\":\"emphasis\",\"item_animation\":true,\"link_style\":\"default\",\"link_text\":\"Read more\",\"margin\":\"default\",\"meta_align\":\"below-title\",\"meta_element\":\"div\",\"meta_style\":\"text-meta\",\"panel_style\":\"tile-default\",\"parallax_easing\":\"1\",\"show_content\":false,\"show_image\":true,\"show_link\":false,\"show_meta\":false,\"show_title\":false,\"title_align\":\"top\",\"title_element\":\"h3\",\"title_grid_breakpoint\":\"m\",\"title_grid_width\":\"1-2\",\"title_hover_style\":\"reset\"},\"children\":[{\"type\":\"grid_item\",\"props\":{\"image\":\"wp-content\\\/uploads\\\/2022\\\/03\\\/AWS_advanced_tier_consulting_partner_badge.png\",\"title\":\"Aws Advanced Training Partner\"}},{\"type\":\"grid_item\",\"props\":{\"image\":\"wp-content\\\/uploads\\\/2022\\\/03\\\/AWS_premier_tier_training_partner_badge.png\",\"title\":\"Aws Premium Consuting Partner\"}},{\"type\":\"grid_item\",\"props\":{\"image\":\"wp-content\\\/uploads\\\/2023\\\/02\\\/200-Cert-Light-English-1.png\",\"title\":\"200 Cert\"}}]}]},{\"type\":\"column\",\"props\":{\"image_position\":\"center-center\",\"order_first\":\"xs\",\"position_sticky_breakpoint\":\"m\",\"width_medium\":\"3-5\"},\"children\":[{\"type\":\"image\",\"props\":{\"animation\":\"none\",\"class\":\"heroimg\",\"container_padding_remove\":false,\"image\":\"wp-content\\\/uploads\\\/2022\\\/01\\\/aws_training.jpg\",\"image_box_decoration\":\"mask\",\"image_height\":\"600\",\"image_svg_animate\":false,\"image_svg_color\":\"\",\"image_svg_inline\":true,\"image_width\":\"600\",\"margin\":\"default\",\"text_align\":\"right\",\"visibility\":\"s\"}}]}]}],\"name\":\"HeroTraining\"},{\"type\":\"section\",\"props\":{\"image_position\":\"center-center\",\"style\":\"muted\",\"title_breakpoint\":\"xl\",\"title_position\":\"top-left\",\"title_rotation\":\"left\",\"vertical_align\":\"middle\",\"width\":\"default\"},\"children\":[{\"type\":\"row\",\"children\":[{\"type\":\"column\",\"props\":{\"image_position\":\"center-center\",\"position_sticky_breakpoint\":\"m\"},\"children\":[{\"type\":\"text\",\"props\":{\"column_breakpoint\":\"m\",\"content\":\"\n\n<h1 data-id=\\\"page#0-0-0-0\\\" uk-scrollspy-class=\\\"\\\" class=\\\"uk-scrollspy-inview \\\">Developing Generative AI Applications on AWS<\\\/h1>\\n\n\n<p><span>Please find our upcoming course dates at the end of this page!<\\\/span><\\\/p>\\n\n\n<h2>Course description<\\\/h2>\\n\n\n<p><span>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.<\\\/span><\\\/p>\\n\n\n<p><img class=\\\"size-full wp-image-1424 alignright\\\" src=\\\"http:\\\/\\\/www.tecracer.com\\\/wp-content\\\/uploads\\\/2022\\\/03\\\/AWS-Business-Essentials-150x150-1.png\\\" alt=\\\"\\\" width=\\\"150\\\" height=\\\"150\\\" \\\/><\\\/p>\\n\n\n<h2>COURSE OBJECTIVES<\\\/h2>\\n\n\n<p><strong>In this course, you will learn to:<\\\/strong><\\\/p>\\n\n\n<ul>\\n\n\n<li>Describe generative AI and how it aligns to machine learning<\\\/li>\\n\n\n<li>Define the importance of generative AI and explain its potential risks and benefits<\\\/li>\\n\n\n<li>Identify business value from generative AI use cases<\\\/li>\\n\n\n<li>Discuss the technical foundations and key terminology for generative AI<\\\/li>\\n\n\n<li>Explain the steps for planning a generative AI project<\\\/li>\\n\n\n<li>Identify some of the risks and mitigations when using generative AI<\\\/li>\\n\n\n<li>Understand how Amazon Bedrock works<\\\/li>\\n\n\n<li>Familiarize yourself with basic concepts of Amazon Bedrock<\\\/li>\\n\n\n<li>Recognize the benefits of Amazon Bedrock<\\\/li>\\n\n\n<li>List typical use cases for Amazon Bedrock<\\\/li>\\n\n\n<li>Describe the typical architecture associated with an Amazon Bedrock solution<\\\/li>\\n\n\n<li>Understand the cost structure of Amazon Bedrock<\\\/li>\\n\n\n<li>Implement a demonstration of Amazon Bedrock in the AWS Management Console<\\\/li>\\n\n\n<li>Define prompt engineering and apply general best practices when interacting with foundation models (FMs)<\\\/li>\\n\n\n<li>Identify the basic types of prompt techniques, including zero-shot and few-shot learning<\\\/li>\\n\n\n<li>Apply advanced prompt techniques when necessary for your use case<\\\/li>\\n\n\n<li>Identify which prompt techniques are best suited for specific models<\\\/li>\\n\n\n<li>Identify potential prompt misuses<\\\/li>\\n\n\n<li>Analyze potential bias in FM responses and design prompts that mitigate that bias<\\\/li>\\n\n\n<li>Identify the components of a generative AI application and how to customize an FM<\\\/li>\\n\n\n<li>Describe Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIs<\\\/li>\\n\n\n<li>Identify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications<\\\/li>\\n\n\n<li>Describe how to integrate LangChain with LLMs, prompt templates, chains, chat models, text<br \\\/>embeddings models, document loaders, retrievers, and Agents for Amazon Bedrock<\\\/li>\\n\n\n<li>Describe architecture patterns that you can implement with Amazon Bedrock for building generative AI applications<\\\/li>\\n\n\n<li>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<\\\/li>\\n<\\\/ul>\\n\n\n<h2>INTENDED AUDIENCE<\\\/h2>\\n\n\n<p><strong>This course is intended for:<\\\/strong><\\\/p>\\n\n\n<ul>\\n\n\n<li>Software developers interested in using LLMs without fine-tuning<\\\/li>\\n<\\\/ul>\\n\n\n<h2>PREREQUISITES<\\\/h2>\\n\n\n<p><strong>We recommend that attendees of this course have:<\\\/strong><\\\/p>\\n\n\n<ul>\\n\n\n<li>Completed <a href=\\\"https:\\\/\\\/www.tecracer.com\\\/en\\\/training-en\\\/amazon-aws-technical-essentials-training-details\\\/\\\">AWS Technical Essentials<\\\/a>\\u00a0<\\\/li>\\n\n\n<li>Intermediate-level proficiency in Python<\\\/li>\\n<\\\/ul>\\n\n\n<h2>ACTIVITIES<\\\/h2>\\n\n\n<p><strong>This course includes:<\\\/strong><\\\/p>\\n\n\n<ul>\\n\n\n<li>Presentations<\\\/li>\\n\n\n<li>Demonstrations<\\\/li>\\n\n\n<li>Group exercises<\\\/li>\\n<\\\/ul>\\n\n\n<h2>COURSE DURATION \\\/ PRICE<\\\/h2>\\n\n\n<ul>\\n\n\n<li>2 days<\\\/li>\\n\n\n<li>\\u20ac 1,500.00 <span>(excl. tax) per person (DE)<\\\/span><\\\/li>\\n<\\\/ul>\\n\n\n<h2>Course outline<\\\/h2>\",\"margin\":\"default\"}},{\"type\":\"accordion\",\"props\":{\"collapsible\":true,\"content_column_breakpoint\":\"m\",\"image_align\":\"top\",\"image_grid_breakpoint\":\"m\",\"image_grid_width\":\"1-2\",\"image_svg_color\":\"emphasis\",\"link_style\":\"default\",\"link_text\":\"Read more\",\"show_image\":true,\"show_link\":true},\"children\":[{\"type\":\"accordion_item\",\"props\":{\"content\":\"\n\n<ul>\\n\n\n<li><strong>Module 1: Introduction to Generative AI \\u2013 Art of the Possible<\\\/strong>\\n\n\n<ul>\\n\n\n<li>Overview of ML<\\\/li>\\n\n\n<li>Basics of generative AI<\\\/li>\\n\n\n<li>Generative AI use cases<\\\/li>\\n\n\n<li>Generative AI in practice<\\\/li>\\n\n\n<li>Risks and benefits<\\\/li>\\n<\\\/ul>\\n<\\\/li>\\n\n\n<li><strong>Module 2: Planning a Generative AI Project<\\\/strong><br \\\/>\\n\n\n<ul>\\n\n\n<li>Generative AI fundamentals<\\\/li>\\n\n\n<li>Generative AI in practice<\\\/li>\\n\n\n<li>Generative AI context<\\\/li>\\n\n\n<li>Steps in planning a generative AI project<\\\/li>\\n\n\n<li>Risks and mitigation<\\\/li>\\n<\\\/ul>\\n<\\\/li>\\n\n\n<li><strong>Module 3: Getting Started with Amazon Bedrock<\\\/strong><br \\\/>\\n\n\n<ul>\\n\n\n<li>Introduction to Amazon Bedrock<\\\/li>\\n\n\n<li>Architecture and use cases<\\\/li>\\n\n\n<li>How to use Amazon Bedrock<\\\/li>\\n\n\n<li>Demonstration: Setting up Bedrock access and using playgrounds<\\\/li>\\n<\\\/ul>\\n<\\\/li>\\n\n\n<li><strong>Module 4: Foundations of Prompt Engineering<\\\/strong><br \\\/>\\n\n\n<ul>\\n\n\n<li>Basics of foundation models<\\\/li>\\n\n\n<li>Fundamentals of prompt engineering<\\\/li>\\n\n\n<li>Basic prompt techniques<\\\/li>\\n\n\n<li>Advanced prompt techniques<\\\/li>\\n\n\n<li>Model-specific prompt techniques<\\\/li>\\n\n\n<li>Demonstration: Fine-tuning a basic text prompt<\\\/li>\\n\n\n<li>Addressing prompt misuses<\\\/li>\\n\n\n<li>Mitigating bias<\\\/li>\\n\n\n<li>Demonstration: Image bias mitigation<\\\/li>\\n<\\\/ul>\\n<\\\/li>\\n<\\\/ul>\",\"title\":\"Day 1\"}},{\"type\":\"accordion_item\",\"props\":{\"content\":\"\n\n<ul>\\n\n\n<li><strong>Module 5: Amazon Bedrock Application Components<\\\/strong><br \\\/>\\n\n\n<ul>\\n\n\n<li>Overview of generative AI application components<\\\/li>\\n\n\n<li>Foundation models and the FM interface<\\\/li>\\n\n\n<li>Working with datasets and embeddings<\\\/li>\\n\n\n<li>Demonstration: Word embeddings<\\\/li>\\n\n\n<li>Additional application components<\\\/li>\\n\n\n<li>Retrieval Augmented Generation (RAG)<\\\/li>\\n\n\n<li>Model fine-tuning<\\\/li>\\n\n\n<li>Securing generative AI applications<\\\/li>\\n\n\n<li>Generative AI application architecture<\\\/li>\\n<\\\/ul>\\n<\\\/li>\\n\n\n<li><strong>Module 6: Amazon Bedrock Foundation Models<\\\/strong>\\n\n\n<ul>\\n\n\n<li>Introduction to Amazon Bedrock foundation models<\\\/li>\\n\n\n<li>Using Amazon Bedrock FMs for inference<\\\/li>\\n\n\n<li>Amazon Bedrock methods<\\\/li>\\n\n\n<li>Data protection and auditability<\\\/li>\\n\n\n<li>Demonstration: Invoke Bedrock model for text generation using zero-shot prompt<\\\/li>\\n<\\\/ul>\\n<\\\/li>\\n\n\n<li><strong>Module 7: LangChain<\\\/strong><br \\\/>\\n\n\n<ul>\\n\n\n<li>Optimizing LLM performance<\\\/li>\\n\n\n<li>Using models with LangChain<\\\/li>\\n\n\n<li>Constructing prompts<\\\/li>\\n\n\n<li>Demonstration: Bedrock with LangChain using a prompt that includes context<\\\/li>\\n\n\n<li>Structuring documents with indexes<\\\/li>\\n\n\n<li>Storing and retrieving data with memory<\\\/li>\\n\n\n<li>Using chains to sequence components<\\\/li>\\n\n\n<li>Managing external resources with LangChain agents<\\\/li>\\n<\\\/ul>\\n<\\\/li>\\n\n\n<li><strong>Module 8: Architecture Patterns<\\\/strong><br \\\/>\\n\n\n<ul>\\n\n\n<li>Introduction to architecture patterns<\\\/li>\\n\n\n<li>Text summarization<\\\/li>\\n\n\n<li>Demonstration: Text summarization of small files with Anthropic Claude<\\\/li>\\n\n\n<li>Demonstration: Abstractive text summarization with Amazon Titan using LangChain<\\\/li>\\n\n\n<li>Question answering<\\\/li>\\n\n\n<li>Demonstration: Using Amazon Bedrock for question answering<\\\/li>\\n\n\n<li>Chatbot<\\\/li>\\n\n\n<li>Demonstration: Conversational interface \\u2013 Chatbot with AI21 LLM<\\\/li>\\n\n\n<li>Code generation<\\\/li>\\n\n\n<li>Demonstration: Using Amazon Bedrock models for code generation<\\\/li>\\n\n\n<li>LangChain and agents for Amazon Bedrock<\\\/li>\\n\n\n<li>Demonstration: Integrating Amazon Bedrock models with LangChain agents<\\\/li>\\n<\\\/ul>\\n<\\\/li>\\n<\\\/ul>\",\"title\":\"Day 2\"}}]},{\"type\":\"text\",\"props\":{\"column_breakpoint\":\"m\",\"content\":\"\n\n<p><\\\/p>\\n\n\n<p><strong>IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. 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Course description 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 [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":3097,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[130,129],"tags":[206,148],"class_list":["post-10744","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-aws-training-en","category-training-en","tag-machine-learning-en","tag-speciality-courses"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Developing Generative AI Applications on AWS - tecRacer<\/title>\n<meta name=\"description\" content=\"Developing Generative AI Applications on AWS Training from the AWS Advanced Tier Training Partner. \u2705 Learn more and book your training today!\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.tecracer.com\/en\/training-en\/developing-generative-ai-applications-on-aws\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Developing Generative AI Applications on AWS\" \/>\n<meta property=\"og:description\" content=\"Developing Generative AI Applications on AWS Training from the AWS Advanced Tier Training Partner. \u2705 Learn more and book your training today!\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.tecracer.com\/en\/training-en\/developing-generative-ai-applications-on-aws\/\" \/>\n<meta property=\"og:site_name\" content=\"tecRacer\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/tecRacer\/\" \/>\n<meta property=\"article:published_time\" content=\"2024-03-11T11:00:43+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-01-20T08:51:32+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.tecracer.com\/wp-content\/uploads\/2022\/03\/AWS-Business-Essentials-150x150-1.png\" \/>\n\t<meta property=\"og:image:width\" content=\"150\" \/>\n\t<meta property=\"og:image:height\" content=\"150\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Gemma Corrado\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@tecRacer\" \/>\n<meta name=\"twitter:site\" content=\"@tecRacer\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Gemma Corrado\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.tecracer.com\\\/en\\\/training-en\\\/developing-generative-ai-applications-on-aws\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.tecracer.com\\\/en\\\/training-en\\\/developing-generative-ai-applications-on-aws\\\/\"},\"author\":{\"name\":\"Gemma Corrado\",\"@id\":\"https:\\\/\\\/www.tecracer.com\\\/en\\\/#\\\/schema\\\/person\\\/70fd9edb47731f96225be586e039c8d6\"},\"headline\":\"Developing Generative AI Applications on AWS\",\"datePublished\":\"2024-03-11T11:00:43+00:00\",\"dateModified\":\"2025-01-20T08:51:32+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.tecracer.com\\\/en\\\/training-en\\\/developing-generative-ai-applications-on-aws\\\/\"},\"wordCount\":790,\"publisher\":{\"@id\":\"https:\\\/\\\/www.tecracer.com\\\/en\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/www.tecracer.com\\\/en\\\/training-en\\\/developing-generative-ai-applications-on-aws\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.tecracer.com\\\/wp-content\\\/uploads\\\/2022\\\/03\\\/AWS-Business-Essentials-150x150-1.png\",\"keywords\":[\"Machine Learning\",\"Speciality Courses\"],\"articleSection\":[\"AWS Training\",\"Training\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.tecracer.com\\\/en\\\/training-en\\\/developing-generative-ai-applications-on-aws\\\/\",\"url\":\"https:\\\/\\\/www.tecracer.com\\\/en\\\/training-en\\\/developing-generative-ai-applications-on-aws\\\/\",\"name\":\"Developing Generative AI Applications on AWS - 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