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ML Engineering

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.