📖 Introduction
At re:Invent 2025, AWS placed Generative AI at the center, moving from simple chats to agents that understand context, execute tasks, and integrate natively with infrastructure, security, and data services. Within this approach, AWS launched in Skill Builder a learning path with 33 courses and more than 60 hours to learn these new services, from fundamental to advanced level.
🔍 Why is this re:Invent a turning point?
The big novelty this year is how generative AI stops being an isolated component and becomes a central engine that drives automation, security, infrastructure, and operations. We are entering a stage where:
- 🤖 Agents not only process language: they execute real actions in AWS.
- 🔧 IaC automation i…
📖 Introduction
At re:Invent 2025, AWS placed Generative AI at the center, moving from simple chats to agents that understand context, execute tasks, and integrate natively with infrastructure, security, and data services. Within this approach, AWS launched in Skill Builder a learning path with 33 courses and more than 60 hours to learn these new services, from fundamental to advanced level.
🔍 Why is this re:Invent a turning point?
The big novelty this year is how generative AI stops being an isolated component and becomes a central engine that drives automation, security, infrastructure, and operations. We are entering a stage where:
- 🤖 Agents not only process language: they execute real actions in AWS.
- 🔧 IaC automation is complemented by intelligent flows that detect, decide, and act.
- 🔓 Securit y is transformed thanks to the ability to analyze large volumes of logs in seconds, where every minute is critical.
- 🗂️ Data engineering and observability are rewritten with agents that contextualize, correlate, and recommend.
To support this technological leap, AWS launched new services (some very recent) and updated others, which motivated the design of an integrated learning path to learn them in a structured way.
🛠️Learning path details
- 33 total courses and more than 60 hours of content.
- 26 fundamental-level courses, 4 intermediate, and 3 advanced, combining updates of existing services with completely new launches.
📘 Service Overviews & Course Levels
The learning path organizes 33 courses by technical depth to help learners navigate new AWS services efficiently.
Course Levels:
- 🟢 Beginner (
26 courses): Introduces core services and fundamental concepts. - 🟡 Intermediate (
4 courses): Covers integration, automation, and real-world deployments. - 🔴 Advanced (
3 courses): Focuses on autonomous agents, high-performance compute, and advanced security.
Kiro
It is a development environment (IDE) with AI agents that start from a written specification and generate code, tests, and documentation, helping to design and maintain applications more quickly and consistently. ⏱ 3:30 hours | 📚 3 courses
- 🟢 Kiro Getting Started
- 🟢 Introduction to Kiro powers (Update)
- 🟡 Spec-Driven Development with Kiro
Amazon Nova 2
It is a family of multimodal generative AI models (text, image, audio, video) designed for advanced reasoning, conversational assistants, and content generation in enterprise applications. ⏱ 04:15 hours | 📚 4 courses
- 🟢 Amazon Nova 2: Understanding Models (New)
- 🟢 Amazon Nova 2 Sonic: Next-Generation Conversational AI (Update)
- 🟢 Introduction to Amazon Nova Forge (New)
- 🟡 Extended Thinking with Amazon Nova (Update)
Amazon Quick Suite
An integrated analytics and business intelligence platform powered by generative AI that unifies agents for research, data visualization, and workflow automation, accessible via chat and embedded in tools like browser, Slack, or Office. ⏱ 03:10 hours | 📚 3 courses
- 🟢 Introduction to Amazon Quick Suite
- 🟢 Getting Started with Administering Amazon Quick Suite
- 🟡 Amazon Quick Automate – Building Intelligent Workflows (Update)
AWS DevOps Agent
AI agent for operations that analyzes events and metrics, automates incident response, assists with root cause analysis, and suggests preventive actions to improve reliability. ⏱ 1:00 hour | 📚 1 course
- 🟢 Introduction to AWS DevOps Agent (New)
AWS AI Factories
It is a dedicated AI infrastructure solution deployed in the customer’s data center, with specialized hardware to train and run models while maintaining data sovereignty. ⏱ 00:30 minutes | 📚 1 course
- 🟢 Introduction to AWS AI Factories (New)
Amazon SageMaker
It is AWS’s managed machine learning platform that offers notebooks, data preparation tools, model training, and model deployment, now with more serverless options and a focus on foundation models. In this latest update, it includes a set of “SageMaker AI” capabilities such as serverless notebooks, simplified customization of foundation models, and elastic training with HyperPod to scale without managing infrastructure. ⏱ 03:30 hours | 📚 4 courses
- 🟢 Introduction to Amazon SageMaker Notebooks (Update)
- 🟢 Introduction to Model Customization in Amazon SageMaker AI (Update)
- 🔴 Elastic Training on Amazon SageMaker HyperPod (New)
- 🔴 Checkpointless Training on Amazon SageMaker HyperPod (New)
AWS Security Agent
Security agent that reviews from code to production environment, automates configuration assessments and penetration tests, and generates recommendations to reduce risk throughout the development lifecycle. ⏱ 00:30 minutes | 📚 1 course
- 🟢 Introduction to AWS Security Agent (Tech Preview) (Update)
Amazon Bedrock
It is the service that allows building and operating AI agents based on foundation models, with security controls, continuous evaluation, and policies to govern their behavior. ⏱ 02:10 hours | 📚 2 courses
- 🟢 AgentCore Evaluation on Amazon Bedrock (New)
- 🟢 AgentCore Policy on Amazon Bedrock (New)
Amazon EC2
This service has new compute instances with next-generation GPUs designed to train and serve large AI models with high performance. The new instances are optimized for frontier model training, combining next-generation GPUs with network and storage improvements to offer several times more performance than previous generations. ⏱ 02:30 hours | 📚 5 courses
- 🟢 Introduction to Amazon EC2 P6e-GB300 UltraServers (Update)
- 🟢 Introduction to Capacity Manager for Amazon EC2 (Update)
- 🟢 Introduction to Amazon EC2 Instance Attestation (New)
- 🟢 Introduction to Amazon EC2 P6-B300 Instances (New)
- 🟢 Introduction to Capacity Manager for Amazon EC2 (New)
Amazon S3 Vectors
It is an S3 capability to store vectors (embeddings) and perform semantic and similarity searches on documents, images, or other objects. ⏱ 1:00 hour | 📚 1 course
- 🟢 Amazon S3 Vectors Getting Started (Update)
Amazon FSx for NetApp ONTAP
Fully managed service that provides ONTAP file systems with enterprise features (snapshots, clones, replication) and the elasticity and pay-as-you-go model of AWS cloud. ⏱ 1:15 hours | 📚 1 course
- 🟢 Amazon FSx for NetApp ONTAP Primer (Update)
Amazon Aurora PostgreSQL It is a relational database compatible with PostgreSQL that adds policies to hide or transform sensitive data. This new functionality allows defining dynamic masking policies so that sensitive data is displayed differently depending on the user’s role, reinforcing access control at column and row level. ⏱ 1:30 hours | 📚 1 course
- 🔴 Dynamic Data Masking in Aurora PostgreSQL (New)
AWS Transform
It is a suite of AI-powered tools to modernize .NET applications, full-stack Windows, and custom code, automating analysis, refactoring, and migration to accelerate legacy modernization toward cloud-native architectures. ⏱ 03:00 hours | 📚 3 courses
- 🟢 AWS Transform for .NET Getting Started (Update)
- 🟢 AWS Transform Custom (New)
- 🟢 AWS Transform Full-Stack Windows (New)
🚀 Conclusion: AI as the Engine of the Cloud Ecosystem
AWS re:Invent 2025 marks a decisive turning point: Generative AI has moved beyond being an isolated tool to become the central engine that drives the transformation of the cloud ecosystem.
This learning path of 33 courses is not just a set of trainings but a strategic roadmap showing how infrastructure, security, and operations converge with AI to enable a new generation of solutions.
The incorporation of agents, along with the evolution of compute and security improvements, is creating environments that are much more autonomous, efficient, and prepared for new use cases.
Specialized infrastructure plays a key role, where AWS AI Factories ensure data sovereignty in regulated industries, while the new EC2 instances optimized for AI increase performance for model training and deployment at scale. In this set of updates, it is clear that foundation models are becoming more powerful and are a fundamental part of decision-making, intelligent automation, and the creation of AI-powered products, generating real competitive advantage for organizations that can combine AI + infrastructure + security as a single strategy.
Therefore, this learning path is the ideal starting point to learn the new features, prepare your skills, and put them into practice in your next project within the AWS ecosystem.