Aditya Kalro discusses using large-scale data for Machine Learning (ML) research and some of the tools Facebook uses to manage the entire process of training, testing, and deploying ML models.
Scott Le Grand describes his work at NVidia, Amazon and Teza, including the DSSTNE distributed deep learning framework.
Chinmay Soman and Yi Pan discuss how Uber and LinkedIn use Apache Samza, Calcite and Pinot along with the analytics platform AthenaX to transform data to make it available for querying in minutes.
Tom Gianos and Dan Weeks discuss Netflix' overall big data platform architecture, focusing on Storage and Orchestration, and how they use Parquet on AWS S3 as their data warehouse storage layer.
Charity Majors talks about what it means to do quality operations and software engineering in the year 2016 and beyond, as well as the implications for engineering teams and social systems.
Mārtiņš Kalvāns and Matti Pehrs overview the Data Infrastructure at Spotify, diving into some of the data infrastructure components, such us Event Delivery, Datamon and Styx.
Chun-Ho Hung and Nikhil Garg discuss Quanta, Quora's counting system powering their high-volume near-real-time analytics, describing the architecture, design goals, constraints, and choices made.
Nikhil Garg talks about the various Machine Learning problems that are important for Quora to solve in order to keep the quality high at such a massive scale.
Preslav Le talks about how Dropbox’s infrastructure evolved over the years, how it looks today, as well the challenges and lessons learned on the way.
Aaron Spiegel reviews common scaling techniques for both relational and NoSQL databases, discussing trade-offs of these techniques and their effect on query flexibility, transactions and consistency.
Noriaki Tatsumi discusses building a microservices architecture on Spring Cloud that's reliable, resilient, and scalable.
Adam Krieger discusses why containers have an impact on the entire organization, not just the developers through scalability, confidence of isolation and the reduction of cycle time.
CONTENT IN THIS BOX PROVIDED BY OUR SPONSOR
Synthetic and Real User Monitoring: Complementary solutions for holistic monitoring.
Measure end user performance with valuable insights by combining active and passive monitoring.
Website uptime monitoring: Adding value to your services.
Offer uptime monitoring as a complementary service to your customers to nurture deeper trust and client loyalty.
Web hosting issues and solutions.
Be an efficient Web Hosting Service Provider by eliminating the issues that hinder a good Web Hosting environment.
Optimize response time as a means to drive traffic to your website.
Reduce the load time of your web pages, keep track of your website performance and ensure that your business does not lose a customer.