Machine Learning and MLOps - What's the Fundamentals of Machine Learning?

Coffee Sessions #27 with Noah Gift of Pragmatic AI Labs, Practical MLOps 

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// A “Gift” from Above

This week, Demetrios and Vishnu got to spend time with inimitable Noah Gift. Noah is a data science educator who teaches at Duke, Northwestern, and many other universities, as well as a technical leader through his company, Pragmatic AI Labs, and past companies.

His bio alone would take up this section of the newsletter, so we invite you to check it out here, as well as the rest of his educational content. Read on for some of our takeaways.

// HOW is as important as WHAT

In our conversation, Noah eloquently pointed out the numerous challenges of bringing ML into production, especially for making sure it's used positively. It’s not enough to train great models; it’s important to make sure they impact the world positively as their productionized. How models are used is as important as what the model is.

Noah specifically commented on externalities and how it’s incumbent on all MLOps practitioners to understand the externalities created by their models.

// Just get certified

As an educator, Noah has seen front and center how deficits in ML/DS education at the university level have led to the “cowboy” data scientist who doesn’t fit into an effective technical organizational structure. In his courses, Noah emphasizes getting started with off-the-shelf models and understanding how existing software systems are engineered before committing to building ML systems.

Furthermore, Noah suggested getting certifications as a useful way of upskilling for anyone looking to increase their knowledge base in MLOps, especially by cloud providers.

// Tech Stack Risk

Finally, as many of you do, we debated the relative merits of the major cloud providers (AWS, Azure, and GCP) with Noah. With his vast experience, Noah made a great point about how adopting extremely new tools can sometimes go wrong. In the past, Noah adopted Erlang as a language used in the development of a product. However, as the language never quite took off (in his experience), it became a struggle to hire the right talent to get things done.

Readers, as you go about designing and building the MLOps stack, does any part of the process sound like Noah’s experience with Erlang? Tools or frameworks where downstream adoption may end up fractured? We’d love to hear more!

Definitely check out Noah’s podcast with us for more awesome nuggets on MLOps. Thanks to Noah for taking the time! 

https://noahgift.com/

Noah Gift

Machine Learning, Data Science, Cloud & AI Lecturer

His most recent books are:

Pragmatic A.I.: An introduction to Cloud-Based Machine Learning (Pearson, 2018)
Python for DevOps (O’Reilly, 2020). 
Cloud Computing for Data Analysis, 2020
Practical MLOps (O'Reilly, 2021 est.)

--------------- ✌️Connect With Us ✌️ -------------

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Noah on LinkedIn: https://www.linkedin.com/in/noahgift/

Timestamps

[00:00] Introduction to Noah Gift  
[03:28] Pragmatic when it comes to MLOps
[32:45] The worst excuse that you can give somebody is that "I just do this stuff that's hard, intellectually, but departed makes it work. That's your job."  
[33:34] "In the Master's Degree, we don't do anything that gets you a job."
[46:33] MLOps vs Cloud Provider
[51:35] GO vs Erlang