That sounds like an argument for making more general models instead of specialized models. But wouldn't that multiply the requirement for data? e.g. If you are trying to make predictions based on user behavior wouldn't it make sense to make two models for Desktop vs Mobile as the user behavior could be vastly different. There are enough articles out there which say that Mobile UX is very different than Desktop UX.

Would luigi https://github.com/spotify/luigi and know about airflow. But when trying to search for mystro didn't get relevant results for ML worfkflow management

Data Science

Overfitting to your training data can be avoided by evaluating your machine learning algorithm on a holdout test dataset, but what about overfitting to the test data?  Turns out it can be done, easily, and you have to be very careful to avoid it.  But an algorithm from the field of privacy research shows promise for keeping your test data safe from accidental overfitting

Thresholdout: Down with Overfitting

We're back again with friend of the pod Walt, former software engineer extraordinaire and current data scientist extraordinaire, to talk about some best practices from software engineering that are ready to jump the fence over to data science.  If last week's episode was for software engineers who are interested in becoming more like data scientists, then this week's episode is for data scientists who are looking to improve their game with best practices from software engineering.

What Data Scientists Can Learn from Software Engineers

It's been a busy hurricane season in the Southeastern United States, with millions of people making life-or-death decisions based on the forecasts around where the hurricanes will hit and with what intensity.  In this episode we'll deconstruct those models, talking about the different types of models, the theory behind them, and how they've evolved through the years.

Hurricane Forecasting

This week, we've got a fun paper by our friends at Google about the hidden costs of maintaining machine learning workflows.  If you've worked in software before, you're probably familiar with the idea of technical debt, which are inefficiencies that crop up in the code when you're trying to go fast.  You take shortcuts, hard-code variable values, skimp on the documentation, and generally write not-that-great code in order to get something done quickly, and then end up paying for it later on.  This is technical debt, and it's particularly easy to accrue with machine learning workflows.  That's the premise of this episode's paper.

Machine Learning: The High Interest Credit Card of Technical Debt

In this episode of the AI in Industry podcast, we speak with Marshall Choy, VP of Product at SambaNova, an AI hardware firm based in the Bay Area. SambaNova was founded by a number of Oracle and Sun Micro Systems alumni. We speak with Choy on two fundamental questions:  How will business models fundamentally change with respect to new AI hardware capabilities? How can business leaders think about their AI hardware needs?  SambaNova is one of many firms that's going to be advertising at the Kisaco Research AI Hardware Summit in Beijing June 4th and 5th.

How Business Leaders Should Think About AI Hardware

This is a re-release of an episode that originally aired on April 1, 2018

If you've done image recognition or computer vision tasks with a neural network, you've probably used a convolutional neural net. This episode is all about the architecture and implementation details of convolutional networks, and the tricks that make them so good at image tasks.

Convolutional Neural Networks

A message from Ben around algorithmic bias, and how our models are sometimes reflections of ourselves.

An interstitial word from Ben

As protests sweep across the United States in the wake of the killing of George Floyd by a Minneapolis police officer, we take a moment to dig into one of the ways that data science perpetuates and amplifies racism in the American criminal justice system. COMPAS is an algorithm that claims to give a prediction about the likelihood of an offender to re-offend if released, based on the attributes of the individual, and guess what: it shows disparities in the predictions for black and white offenders that would nudge judges toward giving harsher sentences to black individuals. 

We dig into this algorithm a little more deeply, unpacking how different metrics give different pictures into the “fairness” of the predictions and what is causing its racially disparate output (to wit: race is explicitly not an input to the algorithm, and yet the algorithm gives outputs that correlate with race—what gives?) Unfortunately it’s not an open-and-shut case of a tuning parameter being off, or the wrong metric being used: instead the biases in the justice system itself are being captured in the algorithm outputs, in such a way that a self-fulfilling prophecy of harsher treatment for black defendants is all but guaranteed. Like many other things this week, this episode left us thinking about bigger, systemic issues, and why it’s proven so hard for years to fix what’s broken.

Racism, the criminal justice system, and data science

This episode features Zach Drake, a working data scientist and PhD candidate in the Criminology, Law and Society program at George Mason University. Zach specializes in bringing data science methods to studies of criminal behavior, and got in touch after our last episode (about racially complicated recidivism algorithms). Our conversation covers a wide range of topics—common misconceptions around race and crime statistics, how methodologically-driven criminology scholars think about building crime prediction models, and how to think about policy changes when we don’t have a complete understanding of cause and effect in criminology. For the many of us currently re-thinking race and criminal justice, but wanting to be data-driven about it, this conversation with Zach is a must-listen.

Criminology and Data Science

This is a re-release of an episode that first ran on January 29, 2017.

This  week: everybody's favorite WWII-era classifier metric!  But it's not  just for winning wars, it's a fantastic go-to metric for all your  classifier quality needs.

Rock the ROC Curve

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.

https://hdsr.mitpress.mit.edu/pub/xsrt4zs2/release/2

The Data Science Open Source Ecosystem

This is a re-release of an episode that originally ran in October 2019.

If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. You also wouldn’t be YouTube, which found themselves with this problem (gigantic data needs and several very different use cases of what they needed to do with that data) and went a different way: they built one analytics data system to serve them all. Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this system, the complexity they had to introduce to service all four uses simultaneously, and the impressive engineering that has to go into building something that “just works.”

Procella: YouTube's super-system for analytics data storage

A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to our audience about their work. This week we’re excited to bring on Todd Hendricks, Bay Area data scientist and a volunteer who reached out to tell us about his studies with the Stanford Open Policing dataset.

A Data Science Take on Open Policing Data

The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that classifies liver cancer, and asks the question—are patients now getting better care, and achieving better outcomes, with these algorithms in the mix? The answer isn’t no, exactly, but it’s not a resounding yes, because these algorithms interact with a very complex system (the healthcare system) and other shortcomings of that system are proving hard to automate away. Getting a faster diagnosis from an image might not be an improvement if the image is now harder to capture (because of strict data quality requirements associated with the algorithm that wouldn’t stop a human doing the same job). Likewise, an algorithm getting a prediction mostly correct might not be an overall benefit if it introduces more dramatic failures when the prediction happens to be wrong. For every data scientist whose work is deployed into some kind of product, and is being used to solve real-world problems, these papers underscore how important and difficult it is to consider all the context around those problems.

A Reality Check on AI-Driven Medical Assistants

All good things must come to an end, including this podcast. This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you!), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years.

It’s been a ride, and a real pleasure and privilege to talk to you each week. Thanks, best wishes, and good night!

—Katie and Ben

Machine Learning: The High Interest Credit Card of Technical Debt

Linear Digressions

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