​​"Generative AI Learning Plan for Developers"​​ is a comprehensive AWS training program designed for developers aiming to build scalable generative AI applications ​​without fine-tuning large language models (LLMs)​​. This 14-hour curriculum blends foundational knowledge with hands-on practice, covering ​​Amazon Bedrock​​, prompt engineering, retrieval-augmented generation (RAG), and LangChain integration. Developers will learn to prototype AI-driven solutions like chatbots, text/code generators, and vision-based applications while addressing security, bias mitigation, and cost optimization. The course culminates in building real-world projects using AWS services, preparing learners to deploy enterprise-ready AI systems and earn AWS certifications.

​​"Amazon Bedrock Getting Started"​​ is a foundational AWS course designed for developers and technical professionals seeking to leverage ​​generative AI​​ through Amazon Bedrock's fully managed service. This course introduces Bedrock's core features, including access to leading foundation models (FMs) like those from Anthropic, Cohere, and Amazon, while emphasizing privacy, security, and serverless architecture. Through guided tutorials, learners explore real-world applications such as chatbot development, embedding generation, and cost-optimized workflows. The curriculum also covers Bedrock's integration with AWS services, model customization via techniques like Retrieval Augmented Generation (RAG), and practical implementation using the AWS Management Console.

​​"Building Generative AI Applications Using Amazon Bedrock"​​ is an advanced 6-hour AWS course tailored for developers aiming to master generative AI application development. It focuses on integrating ​​Amazon Bedrock APIs​​ and ​​LangChain​​ to build scalable solutions like Retrieval Augmented Generation (RAG) systems, AI assistants, and text/code generators. Learners dive into architecture patterns, foundation models (e.g., Claude, Titan), and AWS services such as ​​Bedrock Knowledge Bases​​ and ​​Guardrails​​ for ethical AI deployment. Hands-on labs teach practical skills—from invoking models and optimizing prompts to deploying secure agents—using Jupyter notebooks and AWS console tutorials. Prerequisites include Python proficiency and foundational AWS knowledge.

The ​​AI Language Service Learning Plan​​ is a 4-hour AWS curriculum designed for developers building conversational AI solutions like chatbots, search applications, and contact center automation. Focused on three core AWS services—​​Amazon Kendra​​ (natural language search), ​​Amazon Lex​​ (conversational interfaces), and ​​Amazon Transcribe​​ (speech-to-text)—this program teaches how to integrate these tools for virtual contact center agents and FAQ-driven solutions. Through guided tutorials, learners explore indexing unstructured data with Kendra, designing voice/text chatbots via Lex, and implementing real-time transcription workflows with Transcribe. The plan emphasizes real-world architectures, cost optimization, and AWS Console proficiency, making it ideal for developers creating scalable, AI-driven customer service systems.

​​"Foundations of Prompt Engineering"​​ is a 4-hour intermediate-level AWS course designed for prompt engineers, data scientists, and developers aiming to master ​​generative AI interaction strategies​​. The curriculum bridges foundational concepts like zero-shot/few-shot learning with advanced techniques such as Retrieval Augmented Generation (RAG) and bias mitigation. Learners explore model-specific optimizations for Amazon Titan, Claude, and Jurassic-2, while addressing adversarial risks like prompt injection. Hands-on examples and best practices equip professionals to design precise prompts for business automation, ethical AI deployment, and enhanced model performance.

​​"Amazon Q - Generative AI-powered Assistant Learning Plan"​​ is a 3-hour AWS curriculum designed for IT administrators, developers, and business professionals to master ​​Amazon Q​​, a suite of generative AI tools revolutionizing workflows. The program covers three core products:
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• Amazon Q Developer​​: Enhances coding efficiency with AI-driven code generation, debugging, and Java 8-to-17 migration (demonstrated to save 70% time in case studies like Datapel Systems).
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• Amazon Q Business​​: Connects to enterprise data (e.g., Google Drive, Salesforce) for natural language queries, content creation, and automated workflows like RFQ bidding.​​
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• Amazon Q in QuickSight​​: Transforms BI with AI-powered data storytelling, automated PPT generation, and predictive analytics (reducing dashboard creation time from hours to minutes).​​

Through hands-on tutorials, learners explore architectures integrating AWS services (e.g., Lambda, S3) while prioritizing security via IAM roles and Amazon Bedrock’s abuse detection.

The ​​Machine Learning Learning Plan - Intelligent Document Processing (IDP)​​ is a 2.5-hour AWS curriculum designed for developers and ML practitioners to build AI-powered document automation systems. Focused on ​​Amazon Textract​​ (ML-driven text/data extraction) and ​​Amazon Comprehend​​ (natural language processing), this plan teaches how to process scanned invoices, forms, and reports at scale. Learners explore architectures for extracting structured data (e.g., tables, handwritten notes) from PDFs/JPEGs using Textract's OCR and NLP capabilities, then analyze text for sentiment, entities, and key phrases with Comprehend. Hands-on AWS Builder Labs guide users through real-world IDP workflows, such as automating invoice processing pipelines or building compliance document analyzers.

​​"Amazon Q Business Getting Started"​​ is a 45-minute foundational AWS course designed for developers and business professionals seeking to harness generative AI for enterprise productivity. This course introduces Amazon Q Business—a secure, AI-powered assistant that answers questions, generates content, and automates tasks using company data. Through guided tutorials, learners explore how to connect Q Business to enterprise systems (e.g., SharePoint, Confluence), create AI-driven web experiences, and implement workflows like document summarization or FAQ automation. The curriculum emphasizes real-world architectures, cost optimization, and AWS Management Console proficiency, making it ideal for teams aiming to streamline knowledge management or customer service operations.

The ​​Generative AI Learning Plan for Decision Makers​​ is a 3-hour 17-minute AWS curriculum designed for business leaders and technical decision-makers to strategically adopt generative AI. This plan equips leaders with frameworks to evaluate AI's business impact, mitigate risks, and build organizational readiness. Key modules include:

• Foundational AI literacy​​: Understanding generative AI's capabilities (e.g., content generation, process automation) and ethical risks (bias, data privacy) through real-world examples.
• ​​Project planning​​: Steps to scope AI initiatives, align stakeholders, and integrate AWS services like Amazon Q (for enterprise chatbots) and Bedrock (for custom models).
• ​​Organizational transformation​​: Strategies to upskill teams, establish governance models, and foster an AI-ready culture using AWS's cloud-native tools. The program includes interactive case studies, such as automating customer service workflows with Amazon Q and optimizing marketing campaigns using generative AI.

​​Microsoft Azure AI Fundamentals: AI Overview​​ is a beginner-friendly course designed to demystify artificial intelligence and its practical applications using Microsoft Azure. Structured into four modules, it covers foundational AI concepts like generative AI, computer vision, and natural language processing, while introducing Azure's AI services for real-world solutions such as automated machine learning (AutoML) and responsible AI practices. The course includes hands-on labs (e.g., exploring Azure Machine Learning Studio) and aligns with the ​​AI-900 certification exam​​, making it ideal for developers, business analysts, and non-technical professionals seeking to validate their AI skills.