Victor Hu covers the challenges, both technical and cultural, of building a data science team and capability in a large, global company.
Lisa Phillips discusses the typical struggles a company runs into when building around-the-clock incident operations and the things Fastly has put in place to make dealing with incidents easier.
Stefan Krawczyk discusses how StitchFix used the cloud to enable over 80 data scientists to be productive and have easy access, covering prototyping, algorithms used, keeping schema in sync, etc.
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.
Mike Olson presents several use cases where big data is collected and analyzed to gather insights from the automotive, insurance, financial, and other sectors.
Scott Clark introduces Bayesian Global Optimization as an efficient way to optimize ML model parameters, explaining the underlying techniques and comparing it to other standard methods.
Debraj GuhaThakurta discusses ML and data analysis processes in Spark using examples written in Python and R.
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.
Rob Harrop discusses the increasing automated field of operations and what the future might hold when machine learning and AI techniques are brought to bear on the problem of systems operations.
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.
Eric Bottard and Ilayaperumal Gopinathan discuss easy composition of microservices with Spring Cloud Data Flow.