ENTRIES TAGGED "modelers"
A new crop of data science tools for deploying, monitoring, and maintaining models
What happens after data scientists build analytic models? Model deployment, monitoring, and maintenance are topics that haven’t received as much attention in the past, but I’ve been hearing more about these subjects from data scientists and software developers. I remember the days when it took weeks before models I built got deployed in production. Long delays haven’t entirely disappeared, but I’m encouraged by the discussion and tools that are starting to emerge.
The problem can often be traced to the interaction between data scientists and production engineering teams: if there’s a wall separating these teams, then delays are inevitable. In contrast having data scientists work more closely with production teams makes rapid iteration possible. Companies like Linkedin, Google, and Twitter work to make sure data scientists know how to interface with their production environment. In many forward thinking companies, data scientists and production teams work closely on analytic projects. Even a high-level understanding of production environments help data scientists develop models that are feasible to deploy and maintain.
Models generally have to be recoded before deployment (e.g., data scientists may favor Python, but production environments may require Java). PMML, an XML standard for representing analytic models, has made things easier. Companies who have access to in-database analytics1, may opt to use their database engines to encode and deploy models.
A math band-aid will distract us from fixing the problems that so desperately need fixing.
This piece originally appeared on Mathbabe. We’re also including Jordan Ellenberg’s counter-point to Cathy’s original post as well as Cathy’s response to Jordan. All of these pieces are republished with permission.
I just finished reading Nate Silver’s newish book, The Signal and the Noise: Why so many predictions fail – but some don’t.
The good news
First off, let me say this: I’m very happy that people are reading a book on modeling in such huge numbers – it’s currently eighth on the New York Times best seller list and it’s been on the list for nine weeks. This means people are starting to really care about modeling, both how it can help us remove biases to clarify reality and how it can institutionalize those same biases and go bad.
As a modeler myself, I am extremely concerned about how models affect the public, so the book’s success is wonderful news. The first step to get people to think critically about something is to get them to think about it at all.
Moreover, the book serves as a soft introduction to some of the issues surrounding modeling. Silver has a knack for explaining things in plain English. While he only goes so far, this is reasonable considering his audience. And he doesn’t dumb the math down.
In particular, Silver does a nice job of explaining Bayes’ Theorem. (If you don’t know what Bayes’ Theorem is, just focus on how Silver uses it in his version of Bayesian modeling: namely, as a way of adjusting your estimate of the probability of an event as you collect more information. You might think infidelity is rare, for example, but after a quick poll of your friends and a quick Google search you might have collected enough information to reexamine and revise your estimates.)
The bad news
Having said all that, I have major problems with this book and what it claims to explain. In fact, I’m angry.
It would be reasonable for Silver to tell us about his baseball models, which he does. It would be reasonable for him to tell us about political polling and how he uses weights on different polls to combine them to get a better overall poll. He does this as well. He also interviews a bunch of people who model in other fields, like meteorology and earthquake prediction, which is fine, albeit superficial.
What is not reasonable, however, is for Silver to claim to understand how the financial crisis was a result of a few inaccurate models, and how medical research need only switch from being frequentist to being Bayesian to become more accurate. Read more…