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Helping you optimize your workflow by organizing your everyday practices

In my day job as a machine learning engineer (MLE), I juggle quite a few balls. I support several models in production, actively work on many new modeling efforts, participate as an admin for our area’s regular “skills team meeting”, and serve as my MLE team’s scrum master. I’m always the guy to say “yes” to any request and am more than glad to help a teammate stuck on something.

What might surprise you is that I would not consider myself a very busy person.

Don’t get me wrong, I keep busy all the time. (If my manager is reading this, I am not hurting for work! 😂) I just never get to the point where I’m “tear my hair out” busy. If you’re not aware of my background, I’m a bit of an anomaly in the data science world. Prior to becoming a machine learning engineer, I worked in purely business roles. My Master’s degree is in Organizational Leadership, and I hold several leadership / management designations. …


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Image taken by author of iPad with Juno app

Checking out some apps to support your data science learning path on an iPad

I. Love. My iPad. Ever since its launch in 2010, I had to have one. Back then, I was still a poor college student, so I had to save a hefty penny to save up for my first one. And I did not regret getting it, even though you really couldn’t do much on it.

In my opinion, the iPad is unquestionably the best consumption machine. Whether it be watching your favorite show on Netflix or browsing the web, the iPad is the best personal large screen experience that isn’t a full blown laptop. …


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Yes, folks, I am back! I’ve been away from blogging for some time, and item #4 on this list will explain why. As we wind down an undoubtedly difficult year, I thought it would be good to share one final post to focus on some positivity I personally experienced in 2020.

Of course, I do not want to diminish how difficult this year was for many people, including myself. I personally had a very difficult time adjusting to the whole “work from home” thing, and my grandmother also passed away just a few short weeks ago. So I’m not going to be the person who says that 2020 was a great year. …


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MLflow 101

Getting your parameters, metrics, artifacts, and more logged to an MLflow tracking server

Hey there, friends, and welcome back to another post in our series on MLflow. If this is the first post you’ve seen and would like to catch up, be sure to check out the previous posts here:

As always, if you would like to see the code mentioned in this post, please be sure to check out my GitHub repo here.

This latest post is going to build right on top of part 2, so please do check that out if you missed it. Just to quickly recap what we did in that post, we deployed an MLflow tracking server to Kubernetes with Minikube on our local machines. Behind the scenes, the MLflow tracking server is supported by a Postgres metadata store and an AWS S3-like artifact store called Minio. That post was quite meaty, so I’m happy to share this one is much simpler by comparison. …


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Hello there, friends! It’s been a while since I’ve written a more business-oriented post given that I’ve been focused on more data science / machine learning-related stuff. But as I begin to type this, it’s 2:00am on a Thursday, and I woke up randomly inspired to write this post. (Can’t remember what I was dreaming about, but it must have been along these lines!)

One of the areas of life in which I’m most passionate is this idea of persuasion psychology. In my own words, persuasion psychology is the idea that humans are deeply irrational beings and thus behave in ways that are irrational yet oddly predictable. So for example, if you’re in line at Starbucks and the 5 people in front of you “pay it forward” by paying for the coffee of the person behind them, how much more inclined are you to also follow suit? …


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MLflow 101

Creating a point for logging and tracking model artifacts in a single server running on Minikube

10/15/20 Update: In writing my next post in this series, I found several bugs that prevented me from appropriately deploying to Minikube. To that end, I’ve updated a number of things to get you up and going with a WORKING instance! 😃

Welcome back, friends! We’re back with our continued mini-series on MLflow. In case you missed out part one, be sure to check it out here. The first post was a super basic introduction to log basic parameters, metrics, and artifacts with MLflow. That was just having us log those items to a spot on our local machine, which is not an ideal practice. In a company context, you ideally want to have all those things logged to a central, reusable location. That’s we’ll be tackling in today’s post! …


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MLflow 101

Helping you take your first step into the machine learning lifecycle flow with this handy tool

Hello again friends! We’re back here with another quick tip, and because I do attempt to keep these posts quick, this is actually going to be part one in a series of tips related to MLFlow. In the spirit of full transparency, MLFlow is pretty new to me, so I’m going to be learning things alongside you all over the next few weeks. If you’d like to follow along with my code, check out this link to my correlated GitHub repository.

I’m sure the first question on your mind is, what is MLflow? Simply put, MLflow is an open source platform designed to help streamline the machine learning lifecycle process. Again, I’m still learning all it does, but it seems to offer a lot of promising features that I’m excited to explore within future posts. These things range from creating a model registry, easy deployment of models as APIs, and more. I honestly don’t know how long this sub-series will go, but I imagine we’re going to get a lot out of this neat tool! …


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Ensuring your ML-serving API can handle the properly expected performance load when used in production

Hello again, friends! Welcome back to another data science quick tip. Now, when it comes to the full spectrum of data science (discovery to production), this post definitely falls toward the end of the spectrum. In fact, some companies might recognize this as the job of a machine learning engineer rather than a data scientist. As a machine learning engineer myself, I can verify that’s definitely true for my situation.

Still, I‘m sure there are many data scientists out there who are responsible for deployment of their own machine learning models, and this post will hopefully shed some light on how to do easy performance testing with this neat tool called Locust. …


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Helping you to demystify what some people might perceive as a “black box” for your machine learning models

Hello there all! Welcome back again to another data science quick tip. This particular post is most interesting for me not only because this is the most complex subject we’ve tackled to date, but it’s also one that I just spent the last few hours learning myself. And of course, what better way to learn than to figure out how to teach it to the masses?

Before getting into it, I’ve uploaded all the work shown in this post to a singular Jupyter notebook. You can find it at my personal GitHub if you’d like to follow along more closely.

So even though this is a very complex topic behind the scenes, I’m going to intentionally dial it down as much as possible for the widest possible audience. …


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Teaching you two different correct ways to perform one-hot encoding (and one “wrong” way)

Hello, hello everybody! I hope you all are enjoying a nice Labor Day weekend. I personally took Friday off as well to extend my weekend into four days, and I had a lot of great quality time with my daughters these last few days. But you know me, I’m always itching to keep producing in some capacity!

Before we get into the post, please be sure to reference my personal GitHub for all the code we’ll get into below. It’s pretty simple, but if you’d like to follow along in a concise Jupyter notebook, then I’d encourage you to check that out. …

About

David Hundley

Machine learning engineer by day, spiritual explorer by night.

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