This year, my colleague Shobeir and I attended the GraphLab Conference to learn more about GraphLab and data science in general. The event began with a really great introduction by Carlos Guestrin, which illustrated GraphLab’s strategy, vision and practices. After this we attended a few more high-level talks about GraphLab before delving into sessions specifically around data science, data engineering and deployment.
The GraphLab team introduced many exciting new features for GraphLab Create since its last release, including gradient-boosted trees for regression and classifications, deployment capabilities, hyper-parameter tuning, higher capacity SFrames, access to latent factors of matrix factorization methods and much more.
Like many others, I was excited that this year’s Google I/O tickets would be distributed on a lottery basis. Also like many others, I was not lucky enough to win one of the lottery tickets.
So when I heard that Women Who Code had discounted tickets for women in tech, I signed up immediately. Apparently Google made a big effort this year to get more women to participate in the conference, and as a result the number of women attendees rose from 7 percent last year to 20 percent this year!
I recently had the opportunity to attend php[tek] 2014 in Chicago. As Tagged is a site primarily powered by PHP, it was a good opportunity to learn about upcoming features in unreleased versions of PHP, some of the newer features that are underutilized and not as well known, and just how far the language of PHP has evolved since it was released many many years ago.
As an engineer, learning about everything that goes into designing a product is a fascinating lesson for me. In the Interface Design Bootcamp at Smashing Conference, Aarron Walter walked through the steps he takes to go from idea to production.
His analogy to describe the process was the game of golf: start with strong, broad strokes, then use smaller strokes to get to the final point. The five steps he outlined, in increasing levels of granularity, were 1) research, 2) flow, 3) interaction, 4) personality and 5) aesthetics.
Figuring out when something has gone wrong with your app or site is extremely difficult. Anomaly detection was a major theme among speakers this year at Monitorama, an open source monitoring conference. You can create trends based on historical data means trends, and those trends can be extrapolated into predictions of traffic patterns. When live traffic deviates from the prediction, you can try to detect if it is a true anomaly or not.
One of the hardest problems in anomaly detection systems is trying to avoid false positives — you don’t want to be woken up at 2 a.m. to fix a problem when nothing is actually broken in the product. This often leads to a phenomenon called “alert fatigue” where the on-call developer ignores noisy notifications, allowing for real events to sneak through undetected.