Highlights from Strata Data Conference in London 2017

Watch highlights covering data-driven business, data engineering, machine learning, and more. From Strata Data Conference in London 2017.

By Mac Slocum
May 24, 2017
The keynote stage at Strata Data Conference The keynote stage at Strata Data Conference (source: O'Reilly Conferences via Flickr)

Experts from across the data world came together in London for Strata Data Conference. Below you’ll find links to highlights from the event.

Using AI to create new jobs

Tim O’Reilly delves into past technological transitions, speculates on the possibilities of AI, and looks at what’s keeping us from making the right choices to govern our creations.

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The science of visual interactions

Miriam Redi investigates how machine learning can detect subjective properties of images and videos, such as beauty, creativity, and sentiment.

Machine learning is a moonshot for us all

Darren Strange asks: What part will we each play in what is sure to be one of the most exciting times in computer science?

What Kaggle has learned from almost a million data scientists

Anthony Goldbloom shares lessons learned from top performers in the Kaggle community and explores the types of machine-learning techniques typically used.

Another one bytes the dust

Using the music industry as an example, Paul Brook shows how modern information points bring new data that changes the way an organization will make decisions.

The data subject first?

Aurélie Pols draws a broad philosophical picture of the data ecosystem and then hones in on the right to data portability.

Real-time intelligence gives Uber the edge

M. C. Srivas covers Uber’s big data architecture and explores the real-time problems Uber needs to solve to make ride sharing smooth.

Lessons from piloting the London Office of Data Analytics

Eddie Copeland explores how the London Office of Data Analytics overcame the barriers to joining, analyzing, and acting upon public sector data at city scale.

Accelerate analytics and AI innovations with Intel

Ziya Ma outlines the challenges for applying machine learning and deep learning at scale and shares solutions that Intel has enabled for customers and partners.

Enabling data science in the enterprise

Mike Olson explains how the UK’s Office of National Statistics is using data science to create repeatable, accurate, and transferable statistical research.

Is finance ready for AI?

Aida Mehonic explores the role artificial intelligent might play in the financial world.

Peeking into the black box: Lessons from the front lines of machine-learning product launches

Grace Huang shares lessons learned from running and interpreting machine-learning experiments.

Post topics: AI & ML, Data

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