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DataDelver

Exploring the labyrinth of Data Science, Machine Learning, and MLOps one delve at a time.

Delve 13: Let's Build a Modern ML Microservice Application - Part 7, Model Tracking and APIs with MLFlow

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"Machine learning models are only as good as their deployment strategy. An unused model is just a fancy equation." - Chat GPT

Reset and Rescope

Hello data delvers! In part six of this series we containerized our application, making it portable and easy to deploy. For this part we will take a step back. Introduce machine learning (finally!), and explore how we can begin to incorporate machine learning models into our microservice ecosystem!

Delve 12: Let's Build a Modern ML Microservice Application - Part 6, Containerization

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"This containers revolution is changing the basic act of software consumption. It’s redefining this much more lightweight, portable unit, or atom, that is much easier to manage… It’s a gateway to dynamic management and dynamic systems." - Craig McLuckie

ML Microservices, Deployment through Docker

Hello data delvers! In part five of this series we added automated tests to our application to make it easier to catch bugs. In this part we'll cover packaging and deploying the app!

Delve 11: Let's Build a Modern ML Microservice Application - Part 5, Testing

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"More than the act of testing, the act of designing tests is one of the best bug preventers known." - Boris Beizer

ML Microservices, Keep Calm and Run Your Tests

Hello data delvers! In part four of this series we refactored our application to include a configuration file to make it easy to switch configuration values per development environment. In this part we'll cover a critical element to building scalable systems: Testing.

Delve 10: Let's Build a Modern ML Microservice Application - Part 4, Configuration

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"The measure of intelligence is the ability to change." - Albert Einstein

ML Microservices, The Great Env-scape

Hello data delvers! In part three of this series we refactored our application into three separate layers, allowing us to better separate concerns within our codebase. However, if we examine certain parts of our code we can still observe some brittleness:

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Delve 9: Migrating from Jekyll to Material for MkDocs

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"Good tools make good work." - Unknown

From one Static Site to Another

Greetings data delvers! The sharper-eyed among you may have noticed that the website looks a little bit different now. No you aren't seeing things. I recently completely changed the backend of the site from Jekyll to Material for MkDocs. The process was overall pretty smooth but had some hiccups which I think are worth documenting. However, before we get into that, why the change in the first place?

Delve 7: Let's Build a Modern ML Microservice Application - Part 2, The Data Layer

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"Data is not just the new oil, it's also the new soil." - David McCandless

ML Microservices, the Second

Hello data delvers! In part one of this series we left off creating a basic application that allowed us to search for a work by title in the Metropolitan Museum of Art's collection. We were able set up a basic project structure as well as the tooling we would need to get the project off the ground. In this second part, I'd like to focus on how we can reorganize our code to make it a bit easier to manage as the complexity of our application scales. However, to begin I'd like to take a slight detour and discussing debugging.

Delve 6: Let's Build a Modern ML Microservice Application - Part 1

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"The beginning is the most important part of the work." - Plato

A New Year, A New Start

Hello data delvers! Happy New Year! I hope you all have been well! It's been some time since our last delve (sorry!) but I want to kick off 2025 with a new series of delves I've been wanting to do for some time, building out an ML application using modern microservices principles.

In this multi-part series we'll focus on the tools, techniques, and strategies I use to bring ML applications to life in a maintainable, scalable, and extendable way. If that sounds of interest to you, put on your delving gear and join me as we go deep into the depths of the labyrinth!

Delve 5: The Quest for a Full Screen Raspberry Pi Application

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"All life is problem solving." - Karl Popper

Full Screen Applications on the Raspberry Pi, Why so Hard?

Hello data delvers! Apologies for the lack of updates, life has been busy! For today I have a quick delve on a frustrating problem I had to solve, longer delves are on the way!

Like I'm sure many of you, I greatly enjoy doing side projects on the Raspberry Pi mini computer. If you've read my previous delve, you'll know there are lots of fun utilities you can run on a linux machine. One such application I like to use my Raspberry Pi for is to stream music from web services such as Spotify to my television and then use an audio visualizer like those covered in my previous delve to provide a visual.

Delve 4: The ML Engineer, Coming to an Enterprise Near You

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"Life is like riding a bicycle. To keep your balance, you must keep moving." - Albert Einstein

Who am I?

Hello data delvers! I hope your year is off to a good start! For this delve I wanted to cover a question that I get asked often, especially whenever I meet someone new, the dialog usually goes something like this:

Me: "Hi I'm Chase, nice to meet you!"

Other Person: "Hello Chase, it's nice to meet you too! I'm \<Insert Name Here>. I'm a \<Insert Profession Here>. What do you do for work?

Me: "Oh! I'm a machine learning engineer!"

Other Person: "Oh that's neat... What's a machine learning engineer?"