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

Knowledge Graphs: Architecture and Information

1:30 PM MDT

Knowledge graphs are a rapidly emerging concept for machine-processable models of complex and dynamic domains. They represent the intersection of Web architecture and information. If your organization wants to resolve its most pernicious data integration problems or facilitate machine learning initiatives, knowledge graphs are likely to be part of your future.

We will discuss the emergence of Knowledge Graphs as an emerging solution to a missing capability in most organization's IT strategies. We will discuss how some of the biggest organizations in the world are heading in this direction, it's impact on API design and more. We will focus on specific tools, platforms and standards that are making Knowledge Graphs a crucial part of your overall solutions.

LLVM : Architecture and Language

3:15 PM MDT

The LLVM Project has been around for over a decade, but is increasingly important as a compiler infrastructure to get reuse and portability, shared optimizations and a faster time to market. It achieves this by having a pluggable, layered architecture compared to other compiler infrastructure. Many newer programming languages have chosen it as the basis of their toolchain including Swift, Julia, Rust and more.

In this talk, we will talk about the tools, components and layers of LLVM and how it is helping usher in new visions of portability and reuse.

IPFS : Architecture and Decentralization

5:00 PM MDT

Decentralization and Content-based addressing represent a significant advancement in the development of stable, scalable, censorship-resistant systems. They require a remarkable amount of architectural thinking to work effectively. The Interplanetary File System (IPFS) is an umbrella project covering a cornucopia of extremely well designed layers that will prop up and extend the Web in many new directions. Come here about a future that looks a little bit like combining the Web with Git, Bittorrent, Self-certifying file systems, Distributed Hash Tables and more.

We will discuss the architectural layers of this approach and what each brings to the table.

Machine Learning Workshop

8:30 AM MDT

Machine Learning is all the rage, but many developers have no idea what it is, what they can expect from it or how to start to get into this huge and rapidly-changing field. The ideas draw from the fields of Artificial Intelligence, Numerical Analysis, Statistics and more. These days, you'll generally have to be a CUDA-wielding Python developer to boot. This workshop will gently introduce you to the ideas and tools, show you several working examples and help you build a plan to for diving deeper into this exciting new field.

We will cover:

• The differences between data science, AI and machine learning
• The Five Tribes of Machine Learning (as defined by Pedro Domingos)
• Walkthroughs of some of the main algorithms
• Examples in Java, R and Python
• Tools such as Tensorflow and Pytorch
• The impact of GPUs on machine learning
• Stories about how companies are being successful with machine learning
• A discussion about the likely impacts of machine learning on the job market and society

Machine Learning Workshop

10:30 AM MDT

Machine Learning is all the rage, but many developers have no idea what it is, what they can expect from it or how to start to get into this huge and rapidly-changing field. The ideas draw from the fields of Artificial Intelligence, Numerical Analysis, Statistics and more. These days, you'll generally have to be a CUDA-wielding Python developer to boot. This workshop will gently introduce you to the ideas and tools, show you several working examples and help you build a plan to for diving deeper into this exciting new field.

We will cover:

• The differences between data science, AI and machine learning
• The Five Tribes of Machine Learning (as defined by Pedro Domingos)
• Walkthroughs of some of the main algorithms
• Examples in Java, R and Python
• Tools such as Tensorflow and Pytorch
• The impact of GPUs on machine learning
• Stories about how companies are being successful with machine learning
• A discussion about the likely impacts of machine learning on the job market and society