Skip to content

DataDelver

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

Delve 3: Fun Linux Utilities

Banner

"People rarely succeed unless they have fun in what they are doing" - Dale Carnegie

*nix the Workhorse of MLOps

Welcome to 2024 data delvers! I hope you had a wonderful holiday season! As we enter into the new year I'd like wanted to take some time to talk about things that make my day to day as a developer fun! As I hope to get into in future delves, for many reasons I prefer a nix (Unix or Linux) based environment for doing development. Many people use MacOS as their nix environment of choice, however my preferred method of achieving this in recent years has been the Windows Subsystem for Linux (WSL). It is super easy to set up on any modern windows machine, integrates very nicely with my IDE of choice, Visual Studio Code, and avoids many of the dangers associated with partitioning your hard drive to dual-boot your machine with multiple operating systems. As a bonus, when working with cloud providers you are almost always deploying your model on a Linux server, so mirroring that same environment on your dev machine makes everything that much smoother.

Banner

"If I have seen further than others, it is by standing upon the shoulders of giants." - Isaac Newton

My Go To List of Machine Learning & Data Science Resources

I have often been asked what resources I recommend for those looking to get into machine learning, whether you want to be a data scientist or ml engineer. In this delve I'll cover my go to list of resources I continue to rely on whenever I need to refresh my own knowledge or delve deeper into a specific subject matter.

Delve 1: The (Hidden) Danger of Notebooks in Production

Banner

"Coming together is a beginning. Keeping together is progress. Working together is success." - Henry Ford

When Good Intentions go Awry

At many points in my career I have come across the topic of deploying code related to machine learning models in the form of Jupyter Notebooks. Often, the push towards this idea comes from a place of good intentions, of speeding up the the model deployment process or enabling better access to and understanding of the production environment by data scientists. However, despite the good intentions, this approach has in my experience created an environment of quite negative effect for the engineering teams asked to maintain these systems. In this delve, I will share my own personal experiences on working with notebooks in production systems, some of the ways I have observed them creating unnecessary friction between data scientists and ML engineers, and reflect how I think notebooks can be used as part of a healthy production system.

  • in Meta
  • 2 min read

Delve 0: Hello Labyrinth (World)!

Banner

It seemed so daunting, "I need to make this model work!", "Running this in a notebook isn't good enough, we need to drive live site traffic against this!", "All of this data is bad!".

Entering the Labyrinth

Welcome to my blog data delver! I'm so glad you found your way here! If you're like me, when you first started out with data science and machine learning, you may have been feeling overwhelmed. With so many different concepts to learn it may have seemed as if there was an insurmountable labyrinth of information ahead of you, with no clear path towards mastery and practical application. Fear not! For you have found a resource which shall aid you in your own quest to navigate the maze.