iOS Code Signing - Part 2
Update (10/31/2016): We’ve written a newer blog post about how we test, integrate and deploy our iOS app. It complements the information here, and includes up-to-date details about our current process.
Update (10/31/2016): We’ve written a newer blog post about how we test, integrate and deploy our iOS app. It complements the information here, and includes up-to-date details about our current process.
If you ever spent time in the field of marketing analytics, chances are that you have analyzed the existence of a causal impact from a new local TV campaign, a major PR event, or the emergence of a new local competitor. From an analytical standpoint these types of events all have one thing in common: The impact cannot be tracked at the individual customer level and hence we have to analyze the impact from a bird's eye view using time series analysis at the market level. Data science may be changing at a fast pace but this is an old-school use-case that is still very relevant no matter what industry you're in.
I am a tech talk and blog post addict! In the evenings I frequently run on my treadmill and watch at least one tech talk. At our last offsite in San Francisco a few of my co-workers encouraged me to write a post of my favorite tech talks and articles from 2015. Well here they are, in no particular order:
Stitch Fix recently released its first iPhone app! What started as a simple, single-storyboard application is now a complex application with entirely programmatic views. The transformation from storyboard to programmatic views was not straightforward–we experienced the good and the bad of storyboards, .xibs, programmatic views, and in-between hybrids.
Jupyter and D3 have both become staples in the data science toolkit: Jupyter for interactive data analysis and D3 for interactive data visualization. There has recently been a growing array of options for using the two together - such as mpld3, bokeh, plotly and others - but these tools usually focus on the use case of a Python or R programmer who would rather not dig too far into JavaScript, and thus somewhat limit the otherwise immense flexibility available with D3. For those who want the full breadth of possibilities, there is another approach shown below. Be sure to play with the force graph!
Neural networks provide a vast array of functionality in the realm of statistical modeling, from data transformation to classification and regression. Unfortunately, due to the computational complexity and generally large magnitude of data involved, the training of so called deep learning models has been historically relegated to only those with considerable computing resources. However with the advancement of GPU computing, and now a large number of easy-to-use frameworks, training such networks is fully accessible to anybody with a simple knowledge of Python and a personal computer. In this post we’ll go through the process of training your first neural network in Python using an exceptionally readable framework called Chainer. You can follow along this post through the tutorial here or via the Jupyter Notebook.
Here at Stitch Fix, we have many different apps and services. As our infrastructure grows, so does the need to create and define more “micro-services” to centralize and isolate important shared behavior and data. Testing is a very important part of our development process. As we started creating more and more services, we realized that we had to change the way we think about integration tests when services are involved. Here’s a typical workflow that you may be familiar with:
When we our betters see bearing our WOEs,
We scarcely think our miseries our foes.
From King Lear
I think it’s safe to say that on-call week is never something we software engineers eagerly anticipate. I know from past experience, my stress levels tend to uptick, my personal productivity expectations decline, and my eyes are always trained on my email, hoping the dreaded pagerduty alert will not jump from the shadows. But I have to ask myself, what’s driving these negative emotions? And more importantly, are they pointing to possible improvements we as software engineers should implement to make on call duties less of a burden? At Stitch Fix, we’ve acknowledged these concerns and recently implemented strategies to help make on call duties less stressful and more productive.
I know it’s trite to say that interviewing for a job is really stressful, but I hope you’ll cut me some slack. The experience is still a little fresh. This summer I dove headfirst into the gauntlet that is Job Hunting for the first time in six years. I’d heard nightmarish stories about what awaited me: riddles and puzzles, trick question whiteboard code exercises, and grueling panel interview sessions. I was, if you’re going to force me to be perfectly honest, scared out of my mind.