by Edgar Roman
Our challenge was to create a login system for little people who might barely read, maybe no email, perhaps no home computer. And we had to watch out for privacy laws - especially tough for minors. But these kids want to play games, write stories, and create online avatars to share and compete against their buddies. Listen to how we developed the PBS KIDS login and moderation system in Django.
by Matt Spitz
There are a plethora of options when it comes to deciding how to add a machine learning component to your python application. In this talk, I'll discuss why python as a language is well-suited to solving these particular problems, the tradeoffs of different machine learning solutions for python applications, and some tricks you can use to get the most out of whatever package you decide to use.
This is the age of data. As more companies expose their datasets through APIs, it's becoming increasingly easier to pull information about users, places, and things. But having this data isn't always enough; we want to understand it, find correlations, and identify trends. Fortunately, the area of computer science known as machine learning has a variety of algorithms specifically designed to do this sort of data wrangling. For the python application developer, there are many off-the-shelf toolkits that include implementations of these algorithms (Orange, NLTK, SHOGUN, PyML and scikit-learn to name just a few), but choosing which one to use can be daunting.
There are a number of tradeoffs one makes when making a selection, depending on the specifics of the implementation and the needs of the application. In this talk, I'll give an overview of some of the packages available and discuss what factors might go into deciding which one to use. I'll also offer some python-specific tricks you can use to work with large amounts of data efficiently.
by Adam Lowry
Understanding the internal state of a running system can be vital to maintaining a high performance, stable system, but conventional approaches such as logging and error handling only expose so much. This talk will touch on how to instrument Python programs in order to observe the state of the system, measure performance, and identify ongoing problems.
Something is wrong with your web application. The time it’s taking to serve requests is growing. Your logs don’t contain enough. Your database appears bored. How do you know what’s going wrong?
In high-performance production servers it’s vital to know as much about the internals of your system as possible. Traditionally this is done by simple methods like logging anything of potential interest or sending error emails with unexpected exceptions. These methods are insufficient, both due to the level of noise inherent in such systems and because of the difficulty in anticipating what metrics are important during an incident.
Environments such as the JVM and .Net VM have advanced tools for communicating with the VM and for applications to expose internal state, but CPython has lacked similar tooling.
This talk will cover what options CPython application developers have for introspecting their programs; new tools for instrumenting, exposing, and compiling performance and behavior metrics; and techniques for diagnosing runtime issues without restarting the process.
Java is in some ways a bogeyman to the Python community -- the language that parents scare their children with, the Cobol of the 21st century. But if we look past the cesspool of JEE it turns out that Java has quietly become an excellent systems environment, one that is still in many ways ahead of its time.
7th–15th March 2012