Track: Machine Learning 2.0
Machine Learning has made our lives more productive from hailing a ride via Uber’s advanced ML-driven rider and driver matching, or Google Now predicting information you’d need before you need it. Machine learning has also made our lives safer allowing people to rent strangers’ houses via Airbnb or reducing the risk of fraud during online purchases. Recent advances in deep learning have brought more new technologies within our reach including self-driving cars, machine translation, predicting weather several years ahead, automated stock trading and more! In this track, come hear from practitioners about some interesting applications of machine learning and recent practical advances in deep learning.
Architectures You've Always Wondered about
Case studies from the most relevant names in software
How our industry is being attacked and what you can do about it.
Chaos & Resilience
Failures, edge cases and how we're embracing them.
Developer Experience: Level up your engineering effectiveness
Trends, tools and projects that we're using to maximally empower your developers.
High Velocity Dev Teams
Working Smarter as a team. Improving value delivery of engineers. Lean and Agile principles.
Immutable Infrastructures: Orchestration, Serverless, and More
What's next in infrastructure. How cloud function like lambda are making their way into production.
Innovations in Fintech
Technology, tools and techniques supporting modern financial services
Java - Propelling the Ecosystem Forward
Lessons from 8, prepping for 9, and peeking ahead at 10. Innovators in Java.
Machine Learning 2.0
Machine Learning 2.0, Deep Learning & Deep Learning Datasets
Microservices: Patterns & Practices
Practical experiences and lessons with Microservices
Modern Clientside Apps
Reactive, cross platform, progressive - webapp tech today
Modern CS in the Real World
Applied, practical, & real-world dive into industry adoption of modern CS
Next Gen APIs
Tooling, techniques, & practices building APIs today
Maximizing your impact as an engineer, as a leader, and as a person
Stream Processing at Large
Rapidly moving data at scale.