Track: Modern Learning Systems
Day of week:
Breakthroughs in fundamental algorithms, hardware and tooling mean that modern learning systems look very different to those deployed just a few years ago. In this session we'll cover the practical, real world use of the latest machine learning technologies in production environments.
We'll learn about the technical details of deep learning and artificial intelligence products from the people who built and deployed them in extremely large scale, high profile systems. We'll hear about the latest libraries and toolkits, which make prototyping and productionizing new ideas easier and quicker. And we'll learn about how we can make use best practices from software engineering to make this historically fragile and costly area of software development more rigorous and reliable.
by Stephen Whitworth
Co-founder and Machine Learning Engineer @Ravelin
Machine learning is powering huge advances in products that we know and love. As a result, ever growing parts of the systems we build are changing from the deterministic to the probabilistic. The accuracy of machine learning applications can quickly deteriorate in the wild without strategies for testing models, instrumenting their behaviour and the ability to introspect and debug incorrect predictions. Wouldn't it be nice to have the best of the software engineering and machine learning...
by Anjuli Kannan
Software Engineer @GoogleBrain
Anjuli will describe the algorithmic, scaling and deployment considerations involved in an extremely prominent application of cutting-edge deep learning in a user-facing product: the Smart Reply feature of Google Inbox.
by Micha Gorelick
Research Engineer @FastForwardLabs, Keras Contributor
The rapidly growing and changing zoo of deep learning frameworks can be hard to keep up with. In this talk Micha will begin by introducing deep learning at a conceptual level. He'll then give an overview of what frameworks like Tensorflow, Keras, Theano, etc. do, and where their strengths and weaknesses lie. He'll then show to build a non-trivial deep learning system using Keras, a particularly practical high level framework suitable for prototypes and production.
Architecting for Failure
Building fault tolerate systems that are truly resilient
Architectures You've Always Wondered about
QCon classic track. You know the names. Hear their lessons and challenges.
Containers - State Of The Art
What is the state of the art, what's next, & other interesting questions on containers.
Dark Code: The Legacy/Tech Debt Dilemma
How do you evolve your code and modernize your architecture when you're stuck with part legacy code and technical debt? Lessons from the trenches.
Data Engineering : Where the Rubber meets the Road in Data Science
Science does not imply engineering. Engineering tools and techniques for Data Scientists
Engineering Culture @ <insert cool company names here>
Culture, Organization Structure, Modern Agile War Stories
Fast & Furious: Ad Serving, Finance, & Performance
Learn some of the tips and technicals of high speed, low latency systems in Ad Serving and Finance
Java: Interesting Stuff in the Space
What do you need to know about Java: JDK9, low latency, and more.
Modern CS in the Real World
Applied, practical, & real-world dive into industry adoption of modern CS ideas
Modern Distributed Architectures
Migrating, deploying, and realizing modern cloud architecture.
Modern Learning Systems
Real world use of the latest machine learning technologies in production environments
Observability Done Right: Automating Insight & Software Telemetry
Tools, practices, and methods to know what your system is doing
Performance myths that need busting and the tools & techniques to get there
Practical Cryptography & Blockchains: Beyond the Hype
Looking past the hype of blockchain technologies, alternate title: Weaselfree Cryptography & Blockchain
Security: The Attacker's Mindset
How Attackers Think. Penetration testing techniques, exploits, toolsets, and skills of software hackers
Softskills: Essential Skills for Developers
Skills for the developer in the workplace
Workhorse Languages, Not Called Java
Workhorse languages not called Java.