You are ready to level up your skills. Or, you've already been playing accidental architect, and need to have a structured plan to be designated as one. Well, your wait is over.

From the author of O'Reilly's best-selling “Head First Software Architecture” comes a full-day workshop that covers all that you need to start thinking architecturally. Everything from the difference between design and architecture, and modern description of architecture, to the skills you'll need to develop to become a successful architect, this workshop will be your one stop shop.

Microservices has emerged as both a popular and powerful architecture, yet the promised benefits overwhelmingly fail to materialize. Industry analyst, Gartner, estimates that “More than 90% of organizations who try to adopt microservices will fail…” If you hope to be part of that successful 10%, read on…

How do we go from requirements to architecture? When the requirements change, when and what do we change in the architecture? A good architecture has to be relevant to the application it serves, but how can we accomplish that.

Event-driven architecture (EDA) is a design principle in which the flow of a system’s operations is driven by the occurrence of events instead of direct communication between services or components. There are many reasons why EDA is a standard architecture for many moderate to large companies. It offers a history of events with the ability to rewind the ability to perform real-time data processing in a scalable and fault-tolerant way. It provides real-time extract-transform-load (ETL) capabilities to have near-instantaneous processing. EDA can be used with microservice architectures as the communication channel or any other architecture.

In this workshop, we will discuss the prevalent principles regarding EDA, and you will gain hands-on experience performing and running standard techniques.

This interactive, hands-on workshop is designed for software developers and architects eager to explore cutting-edge AI technologies. We’ll delve deep into Retrieval-Augmented Generation (RAG) and GraphRAG, equipping participants with the knowledge and skills to build autonomous agents capable of intelligent reasoning, dynamic data retrieval, and real-time decision-making.

Through practical exercises, real-world use cases, and collaborative discussions, you’ll learn how to create applications that leverage external knowledge sources and relational data structures. By the end of the day, you’ll have a solid understanding of RAG and GraphRAG and the ability to integrate these methodologies into production-ready autonomous agents.

We have seen how Retrieval Augmented Generation (RAG) systems can help prop up Large Language Models (LLMs) to avoid some of their worst tendencies. But that is just the beginning. The cutting edge stateoftheart systems are Multimodal and Agentic, involving additional models, tools, and reusable agents to break problems down in separate pieces, transform and aggregate the results, and validate the results before returning them to the user.

Come get introduced to some of the latest and greatest techniques for maximizing the value of your LLMbased systems while minimizing the risk.

Since ChatGPT rocketed the potential of generative AI into the collective consciousness there has been a race to add AI to everything. Every product owner has been salivating at the possibility of new AIPowered features. Every marketing department is chomping at the bit to add a “powered by AI” sticker to the website. For the average layperson playing with ChatGPT's conversational interface, it seems easy however integrating these tools securely, reliably, and in a costeffective manner requires much more than simply adding a chat interface. Moreover, getting consistent results from a chat interface is more than an art than a science. Ultimately, the chat interface is a nice gimmick to show off capabilities, but serious integration of these tools into most applications requires a more thoughtful approach.

On the one hand, Machine Learning (ML) and AI Systems are just more software and can be treated as such from our development efforts. On the other hand, they behave very differently and our capacity to test, verify, validate, and scale them requires a different set of perspectives and skills.

This presentation will walk you through some of these unexpected differences and how to plan for them. No specific background in ML/AI is required, but you are encouraged to be generally aware of these fields. The AI Crash Course would be a good start.

Domain Driven Design has been guiding large development projects since 2003, when the seminal book by Eric Evans came out. Domain Driven Design is split up into two parts: Strategic and Tactical. One of the issues is that the Strategic part becomes so involved and intense that we lose focus on implementing these sorts of things. This presentation swaps this focus as topic pairs. For example, when we create a bounded context, is that a microservice or part of the subdomain? When we create a domain event, what does that eventually become? How do other tactical patterns fit into what we decide in the strategic phase?

This workshop will explore the principles of the Ports and Adapters pattern (also called the Hexagonal Architecture) and demonstrate how to refactor legacy code or design new systems using this approach. You’ll learn how to organize your domain logic and move UI and infrastructure code into appropriate places within the architecture. The session will also cover practical refactoring techniques using IntelliJ and how to apply Domain Driven Design (DDD) principles to ensure your system is scalable, maintainable, and well-structured.

Large Language Models (LLMs) such as ChatGPT and Llama have impressed us with what they can do. They have also horrified us with what they actually do when they are employed with no protection: hallucinations, stale knowledge bases, no conceptual basis for reasoning, and a capacity for toxic and inappropriate content generation. Rather than avoid them altogether or risk legal liability or brand damage, we can put some guardrails around them to benefit from their best traits without fearing their worst.

Retrieval Augmented Generation (RAG) systems augment the process to make it behave more to our liking. Come hear what you can do to benefit from AI systems without fearing them.

If you are getting tired of the appearance of new types of databases… too bad. We are increasingly relying on a variety of data storage and retrieval systems for specific purposes. Data does not have a single shape and indexing strategies that work for one are not necessarily good fits for others. So after hierarchical, relational, object, graph, columnoriented, document, temporal, appendonly, and everything else, get ready for Vector Databases to assist in the systematization of machine learning systems.