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Track: Predictive Architectures and ML

Day of week: Monday

AI systems are ubiquitous in today’s world and help us become more efficient and productive. Whether it is sifting through large amounts of information for faster decision making (eg., Twitter, Netflix, Spotify, LinkedIn), Financial portfolio optimization, ridesharing or automation.

In this track we will learn what it takes to build an AI system at scale! Big Data Engineering is often the foundation of these systems and AI/ML helps develop deeper personalization. AI systems typically exploit past user behavior, user similarities and item similarities to generate a list of information items that is personally tailored to an end-user’s preferences. In addition to learning the state of the art in this field, the track will also aim to discuss topics around fair representation, security and privacy in building such systems.

Track Host: Hema Raghavan

Senior Manager & Heading AI for Growth and Communication Relevance @LinkedIn

Hema Raghavan heads the team that builds AI and ML at LinkedIn solutions for fueling the professional social network’s growth. Prior to that, she was a Research Staff Member at IBM T.J Watson. Hema started her career in the industry in Yahoo Labs. Her interests span the broad area of applications of AI and her experience spans a spectrum of products she has built in the areas of Search, Advertising, Question Answering and Recommendations. She has published in several conferences like WWW, SIGIR, ACL and COLING.

Tracks

Predictive Architectures and ML

Learn about cutting-edge ML applications and their underlying architectures.
Mission Critical Data Engineering

Explore a variety of data engineering use-cases and applications
Production Readiness

Observability, emergency response, capacity planning, release processes, and SLOs for availability and latency.
Humane Leadership

A look at leadership with an emphasis on empathy, taking chances and building other leaders within organizations and teams
Developer Experience: The Art and Science of Reducing Friction

Explore how to reduce developer friction between teams and stakeholders.
Blameless Culture

Absorb the lessons learned from failures and outages in a human-centric process.
Modern Computer Science in the Real World

Learn how companies are applying recent CS research to tackle concurrency, distributed data, and coordination.
Architectures You’ve Always Wondered About

Join companies like Google, Netflix, Bloomberg, BBC, and more as they share an inside glimpse on their next-gen architectures and challenges of delivering at massive scale.
Bare Knuckle Performance

Learn from practitioners on the challenges and benefits of architecting for performance and much more.
Java - The Interesting Bits

Learn the new features in the recent and near-future releases of Java and the JVM and what they offer.
Ethical Considerations in Consciously Designed Software

Design considerations for various contexts, locations, security and privacy requirements.
Operating Microservices

Learn from practitioners operating and evolving systems in performance demanding environments.
Shift-Left Cybersecurity: Developer Accountability for Security

Learn how to make security an inherent part of the software development process.
Native Compilation Is Back (A Look at Non-Vm Compilation Targets)

Issues with native compilation for in browser-based and server-side environments
Troubleshooting in Production

Learn debugging strategies for complex and high stakes environments where standard debuggers and profilers fail.

2019 Schedule