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Getting Started with Amazon Bedrock

Through a structured and interactive learning path, you’ll gain hands-on experience developing AI-powered applications, managing secure workflows, and understanding the tools necessary to scale generative AI in production environments. Whether you're just beginning your journey in AI or preparing for AWS certification in machine learning, this course provides the core skills and practical insight needed to succeed in the evolving landscape of generative AI.

This course includes approximately 5:30–6:00 hours of video lectures, combining both foundational theory and real-world demonstrations. It is divided into three comprehensive modules, each broken down into focused lessons that progressively build your knowledge of Bedrock's capabilities.

To assess learning progress, each module includes interactive quizzes and in-video practice questions that reinforce key concepts and application skills.

📘 Module 1: Amazon Bedrock Fundamentals

🧠 Module 2: RAG, Safety & Agents

⚙️ Module 3: Automation, Monitoring & Careers

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What's inside

Syllabus

Amazon Bedrock Fundamentals

This week, we’ll explore the foundational elements of Amazon Bedrock, AWS’s fully managed service for building and scaling generative AI applications with foundation models. You’ll gain a clear understanding of how Bedrock fits into the broader AWS ecosystem and supports serverless, customizable AI solutions. We’ll cover essential topics including the core architecture and pricing model of Bedrock, how to navigate the Bedrock interface, and the use of PartyRock—a no-code playground for quickly prototyping generative AI apps. You’ll also explore responsible AI principles, learn how to evaluate and choose the right foundation models, and see Amazon Bedrock in action through guided demos. By the end of the week, you’ll have a solid understanding of Amazon Bedrock’s capabilities and how to get started with building and experimenting with foundation models in a secure and scalable way.

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Career center

Learners who complete Getting Started with Amazon Bedrock will develop knowledge and skills that may be useful to these careers:

Generative AI Developer

A Generative AI Developer crafts innovative applications that leverage advanced artificial intelligence models to create new content, such as text, images, or code. This dynamic role involves designing, building, and deploying solutions that harness the power of AI to solve complex problems and enhance user experiences. The "Getting Started with Amazon Bedrock" course directly equips learners for this career path by focusing on building AI-powered applications using AWS’s fully managed service. Enrolling in this course specifically provides hands-on experience with foundation model selection, Retrieval Augmented Generation RAG techniques, and agent orchestration. It also delves into practical aspects like navigating the Bedrock interface and prototyping generative AI apps with PartyRock. Learning how to manage secure workflows and scale generative AI in production environments within the AWS ecosystem is crucial for a Generative AI Developer, making this course invaluable for practical application and success in the field.

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Artificial Intelligence Engineer

An Artificial Intelligence Engineer designs, develops, and implements AI systems and applications across various domains. This broad role encompasses everything from model selection and training to deployment and maintenance of intelligent solutions. The "Getting Started with Amazon Bedrock" course is highly relevant for an Artificial Intelligence Engineer, providing a deep dive into AWS's fully managed service for generative AI. It covers essential topics such as understanding the core architecture of Bedrock, evaluating and choosing appropriate foundation models, and integrating AI applications with other AWS services. The course’s emphasis on Retrieval Augmented Generation RAG, agent orchestration, and ensuring responsible AI principles helps build a strong foundation for developing secure, scalable, and sophisticated AI-powered applications. Mastering these skills is critical for designing robust AI solutions capable of thriving in production environments.

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Machine Learning Engineer

A Machine Learning Engineer focuses on building, deploying, and maintaining machine learning models in production environments. This role bridges the gap between data science and software engineering, ensuring models are scalable, efficient, and reliable. The "Getting Started with Amazon Bedrock" course offers significant advantages for a Machine Learning Engineer specializing in generative AI. It provides practical experience in evaluating and selecting foundation models, crucial for optimizing model performance. The course also details the implementation of Retrieval Augmented Generation RAG for enhancing model outputs and configuring Bedrock Agents to automate workflows. Learning about automation through Bedrock Flows, integrating with AWS services like Amazon CloudWatch for monitoring, and understanding how to scale generative AI solutions in production environments are all core competencies directly addressed by this course, making it highly applicable for this engineering discipline.

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Machine Learning Operations Engineer

A Machine Learning Operations Engineer, often called an MLOps Engineer, specializes in the entire lifecycle of machine learning models, from initial experimentation to deployment, monitoring, and maintenance in production. This role focuses on automating and streamlining the processes required to bring AI solutions to fruition. The "Getting Started with Amazon Bedrock" course is exceptionally well-suited for a Machine Learning Operations Engineer. Its modules on "Automation, Monitoring & Careers" directly address key MLOps responsibilities, including workflow automation using Amazon Bedrock Flows and data-driven automation via Bedrock Data Automation. The course also teaches how to monitor and integrate Bedrock applications with AWS services like Amazon CloudWatch and Amazon S3, ensuring operational visibility and performance. Understanding secure workflows and scaling generative AI in production environments are central themes, providing essential skills for robust MLOps practices.

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Cloud AI Architect

A Cloud AI Architect designs and implements scalable, secure, and cost-effective artificial intelligence solutions within cloud environments. This strategic role requires a deep understanding of cloud platforms and how AI services can be integrated to meet business objectives. The "Getting Started with Amazon Bedrock" course is highly beneficial for a Cloud AI Architect, as it provides a foundational understanding of Amazon Bedrock—AWS’s fully managed service for generative AI. The course explores Bedrock’s core architecture, its place within the broader AWS ecosystem, and how it supports serverless, customizable AI solutions. It covers essential topics like integrating Bedrock with other AWS services, managing secure workflows, and understanding the tools necessary to scale generative AI in production. This comprehensive overview enables an architect to design robust, responsible, and high-performing generative AI solutions leveraging the full potential of AWS.

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Solutions Architect

A Solutions Architect designs comprehensive technical solutions that address specific business challenges, often involving multiple technologies and platforms. This role requires the ability to understand client needs and translate them into a viable, scalable, and secure technical blueprint. The "Getting Started with Amazon Bedrock" course offers an excellent foundation for a Solutions Architect, particularly when designing generative AI-powered applications. It provides a clear understanding of Amazon Bedrock’s capabilities, including foundation model selection, Retrieval Augmented Generation RAG, and agent orchestration. The course also covers how Bedrock integrates with other AWS services, enabling architects to design holistic cloud solutions. Furthermore, the focus on responsible AI principles and managing secure workflows equips an architect to build trusted and compliant AI systems, crucial for successful client engagements and enterprise deployments.

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Technical Consultant

A Technical Consultant advises clients on technology strategies and implements solutions that address their specific business requirements, often specializing in particular platforms or domains. This advisory role demands both deep technical knowledge and excellent communication skills. The "Getting Started with Amazon Bedrock" course is invaluable for a Technical Consultant specializing in generative AI and AWS. The course provides a comprehensive understanding of Amazon Bedrock, its architecture, and how to build and scale generative AI applications. It covers essential concepts such as foundation model selection, responsible AI principles, Retrieval Augmented Generation RAG, and agent orchestration, all of which are critical for advising clients on effective AI strategies. Learning about automation, monitoring, and integration with AWS services empowers a consultant to recommend practical, scalable, and secure solutions tailored to various client needs, establishing expertise in a rapidly evolving field.

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Responsible Artificial Intelligence Specialist

A Responsible Artificial Intelligence Specialist ensures that AI systems are developed and deployed ethically, fairly, and transparently, adhering to regulatory standards and societal values. This critical role involves identifying and mitigating risks associated with AI, such as bias, privacy infringements, and misuse. The "Getting Started with Amazon Bedrock" course directly addresses foundational aspects pertinent to a Responsible Artificial Intelligence Specialist. It explicitly covers responsible AI principles and introduces Amazon Bedrock Guardrails, a powerful toolset for implementing content safety, privacy filters, and responsible AI controls. Learners gain hands-on experience in creating and applying Guardrails, which is invaluable for practical implementation of ethical guidelines. Understanding how Bedrock supports secure workflows and integrates safety mechanisms into generative AI applications is crucial, making this course highly relevant for anyone dedicated to building and governing AI responsibly within the AWS ecosystem.

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AWS Cloud Engineer

An AWS Cloud Engineer designs, implements, and manages cloud infrastructure and services on the Amazon Web Services platform. This role ensures the optimal performance, security, and scalability of cloud resources. The "Getting Started with Amazon Bedrock" course is highly relevant for an AWS Cloud Engineer looking to specialize in artificial intelligence services. The course provides a clear understanding of how Amazon Bedrock fits into the broader AWS ecosystem and supports serverless, customizable AI solutions. It covers essential integration points with other AWS services like Amazon S3 and Amazon CloudWatch for monitoring, which are crucial for building and managing comprehensive cloud environments. The focus on managing secure workflows and scaling generative AI in production helps an engineer understand the specific infrastructure requirements for deploying and maintaining advanced AI applications, enhancing their overall AWS expertise.

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Data Scientist

A Data Scientist analyzes complex datasets to extract insights, build predictive models, and inform strategic decisions, often working with advanced statistical and machine learning techniques. This role typically requires an advanced degree. While the "Getting Started with Amazon Bedrock" course primarily focuses on application development, it may be useful for an aspiring Data Scientist by providing a practical understanding of generative AI models and their operational aspects. The course explores foundation model selection, evaluating diverse models, and understanding responsible AI principles, which are vital for ethical data science practices. Furthermore, the principles of Retrieval Augmented Generation RAG, which enhance model outputs with external knowledge sources, can inform data acquisition and feature engineering strategies. Comprehending how generative AI applications are built and scaled in production environments helps a Data Scientist bridge the gap between model development and practical deployment, offering a broader perspective on the data lifecycle within AI systems.

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AI Integration Specialist

An AI Integration Specialist focuses on seamlessly embedding artificial intelligence capabilities into existing software systems, workflows, and business processes. This role ensures that AI solutions are not standalone but rather enhance and augment an organization's current technological landscape. The "Getting Started with Amazon Bedrock" course is particularly helpful for an AI Integration Specialist. It provides detailed insight into how Amazon Bedrock integrates with other AWS services, which is fundamental for creating interconnected AI solutions within a cloud ecosystem. The course covers automation techniques using Bedrock Flows and Bedrock Data Automation, crucial for streamlining AI workflows. Understanding foundation model selection and agent orchestration also helps in designing effective integration strategies, ensuring that generative AI applications function cohesively and securely within broader enterprise systems. This knowledge is key for successful and scalable AI adoption.

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DevOps Engineer

A DevOps Engineer focuses on automating, integrating, and monitoring the software development lifecycle, ensuring efficient and reliable deployment and operations. This role emphasizes collaboration between development and operations teams to streamline processes. The "Getting Started with Amazon Bedrock" course may be helpful for a DevOps Engineer specializing in AI/ML operations. The module on "Automation, Monitoring & Careers" directly addresses key DevOps principles through its exploration of workflow automation using Amazon Bedrock Flows and data-driven automation with Bedrock Data Automation. Furthermore, the course teaches how to monitor and integrate Bedrock applications with essential AWS services like Amazon CloudWatch and Amazon S3. This knowledge is crucial for a DevOps Engineer tasked with ensuring operational visibility, performance, and scaling generative AI solutions efficiently in production environments, thereby enhancing their ability to support AI-driven projects.

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Prompt Engineer

A Prompt Engineer specializes in crafting and refining inputs for generative artificial intelligence models to elicit optimal, desired outputs. This role requires a deep understanding of how large language models interpret and respond to instructions, aiming to maximize effectiveness and minimize unwanted results. The "Getting Started with Amazon Bedrock" course may be useful for an aspiring Prompt Engineer by providing a foundational understanding of generative AI and Amazon Bedrock. The course covers evaluating and choosing the right foundation models, which is critical for understanding their capabilities and limitations when designing prompts. Furthermore, learning about Retrieval Augmented Generation RAG, which enhances LLM outputs with external knowledge sources, offers insight into how context influences model responses. This course helps a Prompt Engineer understand the underlying mechanisms of generative AI applications, enabling them to craft more effective and contextually rich prompts within the AWS ecosystem.

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AI Product Manager

An AI Product Manager defines the strategy, roadmap, and features for artificial intelligence products, bridging the gap between technical teams, business stakeholders, and customer needs. This role requires a blend of business acumen and technical understanding. The "Getting Started with Amazon Bedrock" course may be helpful for an AI Product Manager by providing a practical understanding of generative AI capabilities and limitations. The course explores foundation model selection, responsible AI principles, and how to build AI-powered applications. Learning about PartyRock, a no-code playground for prototyping generative AI apps, can be particularly valuable for rapid iteration and demonstrating concepts. Understanding how to manage secure workflows and scale generative AI in production environments also equips a product manager to make informed decisions about product feasibility, development timelines, and compliance, ensuring successful product delivery within the AWS ecosystem.

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Data Engineer

A Data Engineer designs, builds, and maintains robust data pipelines and infrastructure, ensuring data is accessible, reliable, and optimized for analysis and application. While more focused on upstream data processes, the "Getting Started with Amazon Bedrock" course may be helpful for a Data Engineer, especially one involved in supporting generative AI initiatives and data professionals. The course explores how Retrieval Augmented Generation RAG enhances large language model outputs with external knowledge sources, which often relies on well-structured data pipelines. Furthermore, the module on "Automation, Monitoring & Careers" introduces Bedrock Data Automation BDA and integration with AWS services like Amazon S3, which are critical for managing and preparing data for AI applications. Understanding the data requirements and consumption patterns of generative AI systems through this course can help a Data Engineer optimize data strategies to effectively support the development and scaling of AI solutions.

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Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Getting Started with Amazon Bedrock.

AWS Certified Developer Associate Practice Tests

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Study guide for the AWS Certified Bedrock Developer Associate exam. It covers all the topics included in the exam and provides practice questions and mock exams. It valuable resource for anyone who wants to prepare for this certification.

Getting Started with Amazon Bedrock

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What's inside

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