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ML Engineering

Delve 15: Let's Build a Modern ML Microservice Application - Part 8, The Orchestrator Service

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"Only a small fraction of real-world ML systems is composed of the ML code... The required surrounding infrastructure is vast and complex." - Hidden Technical Debt in Machine Learning Systems, Sculley et al.

Machine Learning Services as a System

Greetings data delvers! In part seven of this series we finally deployed a model! For this part we'll examine how to utilize our model as part of a larger microservice ecosystem!

Delve 14: Reflections on a Job Quest

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"Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work." - Steve Jobs

A New Chapter

Hello data delvers! I recently successfully wrapped up a journey to find a new job! Along the way, I had the opportunity to interview at several different companies, experience many different styles of interviews, and explore different types of roles. For this delve, I intend to distill some thoughts about this process and share some lessons learned in the hopes they may be useful to others either looking to break into this field or find their next opportunity within it. If that sounds of interest stick around!

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 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?"

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"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

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"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.