"Containerization is the new virtualization." - James Turnbull
Greetings data delvers! In part eight of this series we deployed our first multi-service system. In this part, we examine more deeply how we are deploying our services with Docker and look for opportunities to make our deployment more optimized and secure.
"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.
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!
"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
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!
"Machine learning models are only as good as their deployment strategy. An unused model is just a fancy equation." - Chat GPT
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!
"Life is like riding a bicycle. To keep your balance, you must keep moving." - Albert Einstein
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?"
"If I have seen further than others, it is by standing upon the shoulders of giants." - Isaac Newton
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.
"Coming together is a beginning. Keeping together is progress. Working together is success." - Henry Ford
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.