Brian Sletten

API Design

9:00 AM MDT

Application Programmer Interfaces (APIs) by definition are directed at software developers. They should, therefore, strive to be useful and easy to use for developers. However, when engaging design elements from the Web, they can be useful in much larger ways than simply serializing states in JSON.

There is no right or perfect API design. There are, however, elements and choices that induce certain properties. This workshop will walk you through various approaches to help you find the developer experience and long-term strategies that work for you, your customers and your organization.

We will cover:

The Web Architecture as the basis of our APIs
The REST Architectural Style and its motivations
The Richardson Maturity Model as a way of discussing design choices and induced properties
The implications of contentnegotiation and representation choices such as JSON or JSONLD
The emergence of metadata approaches to describing and using APIs such as OpenAPI and HydraCG
Security considerations
Client technologies
API Management approaches

AI Crash Course : Catching Up to the Future

8:30 AM MDT

It's not just you. Everyone is basically thinking the same thing: When did this happen?

We've gone from slow but steady material advances in machine learning to a seeming explosion and ubiquity of AI-based features, products, and solutions. Even more, we're all expected to know how to adopt, use, and think about all of these magical new capabilities.

Equal parts amazing and terrifying, what you need to know about these so-called “AI” solutions is much easier to understand and far less magical than it may seem. This is your chance to catch up with the future and figure out what it means for you.

In this two part presentation, we will cover why this time it is different, except where it isn't. I won't assume much background and won't discuss much math.

A brief history of AI
Machine Learning
Deep Learning
Deep Reinforcement Learning
The Rise of Generative AI
Large Language Models and RAG
Multimodal Systems
Bias, Costs, and Environmental Impacts
AI Reality Check

At the end of these sessions, you will be conversant with the major topics and understand better what to expect and where to spend your time in learning more.

AI Crash Course : Catching Up to the Future

10:30 AM MDT

It's not just you. Everyone is basically thinking the same thing: When did this happen?

We've gone from slow but steady material advances in machine learning to a seeming explosion and ubiquity of AI-based features, products, and solutions. Even more, we're all expected to know how to adopt, use, and think about all of these magical new capabilities.

Equal parts amazing and terrifying, what you need to know about these so-called “AI” solutions is much easier to understand and far less magical than it may seem. This is your chance to catch up with the future and figure out what it means for you.

In this two part presentation, we will cover why this time it is different, except where it isn't. I won't assume much background and won't discuss much math.

A brief history of AI
Machine Learning
Deep Learning
Deep Reinforcement Learning
The Rise of Generative AI
Large Language Models and RAG
Multimodal Systems
Bias, Costs, and Environmental Impacts
AI Reality Check

At the end of these sessions, you will be conversant with the major topics and understand better what to expect and where to spend your time in learning more.

Building Production ML/AI Systems

1:00 PM MDT

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.

We will cover:

Matching Capabilities to Needs
Performance Tuning
Vector Databases
Testing Strategies
MLOPs/AIOps Techniques
Evolving these Systems Over Time

Building Production ML/AI Systems

3:00 PM MDT

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.

We will cover:

Matching Capabilities to Needs
Performance Tuning
Vector Databases
Testing Strategies
MLOPs/AIOps Techniques
Evolving these Systems Over Time

Automating API Evolution with OpenRewrite

5:00 PM MDT

One of the nice operational features of the REST architectural style as an approach to API Design is that is allows for separate evolution of the client and server. Depending on the design choices a team makes, however, you may be putting a higher burden on your clients than you intend when you introduce breaking changes.

 By taking advantage of the capabilities of OpenRewrite, we can start to manage the process of independent evolution while minimizing the impact. Code migration and refactoring can be used to transition existing clients away from older or deprecated APIs and toward new versions with less effort than trying to do it by hand.

In this talk we will focus on:

Managing API lifecycle changes by automating the migration from deprecated to supported APIs.
Discussing API evolution strategies and when they require assisted refactoring and when they don’t.
*Integrating OpenRewrite into API-first development to ensure client code is always up-to-date with ease.

Quantum and Biological Systems

It's Gonna Get Weird

7:30 PM MDT

Our industry is in the process of changing our understanding of computational systems. The combination of extreme computational and energy power demand is a key part of modern data centers and runtime platforms. How many calculations can we produce at what energy cost? The limitations are a confluence of material science, system design complexity, and the fundamental laws of physics.

It's about to get weird as we enter the world of quantum and biological systems.

We started with coprocessors, FPGAs, ASICs, GPUs, and DSPs as lowerpower, highperformance custom hardware. We're now seeing the emergence of neural processing units and tensor processing units as well.

But we are on the cusp of enormous shifts in what's possible computationally with the advent of quantum and biological systems. Not every computational element is suitable for every problem, but quantum computing will make some problems impossibly fast to handle. Artificial biological brains will be able to computations, like the human brain, with the power budget of a light bulb.

Come hear how things are already in the process of changing as well as what is likely to come next.

Vector Databases : Accelerating Learning and Discovery

9:00 AM MDT

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.

This will be an overview of the benefits of vectors databases as well as an introduction to the major players.

We will focus on open source versus commercial players, hosted versus local deployments, and the attempts to add vector search capabilities to existing storage systems.

We will cover:

• A brief overview of vectors
• Why vectors are so important to machine learning and datadriven systems
• Overview of the offerings
• Adding vector search to other systems
• Sample use cases shown with one of the key open source engines

Vector Databases : Accelerating Learning and Discovery

11:00 AM MDT

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.

This will be an overview of the benefits of vectors databases as well as an introduction to the major players.

We will focus on open source versus commercial players, hosted versus local deployments, and the attempts to add vector search capabilities to existing storage systems.

We will cover:

• A brief overview of vectors
• Why vectors are so important to machine learning and datadriven systems
• Overview of the offerings
• Adding vector search to other systems
• Sample use cases shown with one of the key open source engines

Advanced RAG : Multimodal and Agentic Systems

1:30 PM MDT

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.

We will cover:

• The LangChain and LlamaIndex Frameworks
• Naive and Intermediate RAG Systems
• Multimodal Models (Mixing audio, text, images, and videos)
• Chatbots
• Summarization Services
• Agent Protocols
• Agent Design Patterns

Advanced RAG : Multimodal and Agentic Systems

3:15 PM MDT

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.

We will cover:

• The LangChain and LlamaIndex Frameworks
• Naive and Intermediate RAG Systems
• Multimodal Models (Mixing audio, text, images, and videos)
• Chatbots
• Summarization Services
• Agent Protocols
• Agent Design Patterns

Automating Security Fixes with OpenRewrite: Patching Vulnerabilities Across the Codebase

5:00 PM MDT

Security problems empirically fall into two categories: bugs and flaws. Roughly half of the problems we encounter in the wild are bugs and about half are design flaws. A significant number of the bugs can be found through automated testing tools which frees you up to focus on the more pernicious design issues. 

 In addition to detecting the presence of common bugs, however, we can also imagine automating the application of corrective refactoring. In this talk, I will discuss using OpenRewrite to fix common security issues and keep them from coming back.

In this talk we will focus on:

Using OpenRewrite to automatically identify and fix known security vulnerabilities.
Integrating security scans with OpenRewrite for continuous improvement.
*Free up your time to address larger concerns by addressing the pedestrian but time-consuming security bugs.

Full Stack Engineering - Encryption

8:30 AM MDT

If you ask the typical technologist how to build a secure system, they will include encryption in the solution space. While this is a crucial security feature, in and of itself, it is an insufficient part of the plan. Additionally, there are a hundred ways it could go wrong. How do you know if you're doing it right? How do you know if you're getting the protections you expect?

Encryption isn't a single thing. It is a collection of tools combined together to solve problems of secrecy, authentication, integrity, and more. Sometimes those tools are deprecated because they no longer provide the protections that they once did.Technology changes. Attacks change. Who in your organization is tracking and validating your encryption strategy? How are quantum computing advancements going to change the game?No background will be assumed and not much math will be shown.

Full Stack Engineering - Encryption

10:30 AM MDT

If you ask the typical technologist how to build a secure system, they will include encryption in the solution space. While this is a crucial security feature, in and of itself, it is an insufficient part of the plan. Additionally, there are a hundred ways it could go wrong. How do you know if you're doing it right? How do you know if you're getting the protections you expect?

Encryption isn't a single thing. It is a collection of tools combined together to solve problems of secrecy, authentication, integrity, and more. Sometimes those tools are deprecated because they no longer provide the protections that they once did.Technology changes. Attacks change. Who in your organization is tracking and validating your encryption strategy? How are quantum computing advancements going to change the game?No background will be assumed and not much math will be shown.