MLOps Coffee Sessions #62 with Joel Grus, MLOps from Scratch.

// Abstract
In this talk, Joel Grus of “I don’t like notebooks” fame shares with us his 2021 perspective on notebooks, where he thinks MLOps is now, and what his hot takes in the data space are now.

// Bio
Joel Grus is a Principal Engineer at Capital Group, where he leads a team that builds search, data, and machine learning products for the investment group. He is the author of the bestselling O'Reilly book *Data Science from Scratch*, the not-bestselling self-published book *Ten Essays on Fizz Buzz*, and the controversial JupyterCon talk "I Don't Like Notebooks." He recently moved to Texas after living in Seattle for a very long time.

// Relevant Links
Data Science from Scratch book: https://www.oreilly.com/library/view/data-science-from/9781491901410/
Data Science from Scratch, 2nd Edition book: https://www.oreilly.com/library/view/data-science-from/9781492041122/
Ten Essays on Fizz Buzz: Meditations on Python, mathematics, science, engineering, and design book: https://www.amazon.com/Ten-Essays-Fizz-Buzz-Meditations/dp/0982481829 or https://leanpub.com/fizzbuzz/
I Don't Like Notebooks talk: https://www.youtube.com/watch?v=7jiPeIFXb6U
I Don't Like Notebooks - #JupyterCon 2018 slides: https://docs.google.com/presentation/d/1n2RlMdmv1p25Xy5thJUhkKGvjtV-dkAIsUXP-AL4ffI/edit#slide=id.g362da58057_0_658
Fizz Buzz in Tensorflow: https://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/

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Timestamps:
[00:00] Introduction to Joel Grus
[01:32] Joel's background in tech
[07:47] Joel's I Don't Like Notebooks talk on Jupyter Con
[13:42] Better tooling around notebooks  
[16:48] Hex
[17:20] Step function evolution
[20:41] Kinds of professionals required in Joel's organization to practice MLOps
[23:08] Evaluation process
[25:51] Sagemaker bring your own algorithm
[27:30] Flexibility of models
[31:55] Hot takes on data science world
[34:19] Current Overall Maturity of MLOps
[37:23] Kinds of problem in NLP and search
[39:52] Finding ways to put structures
[40:50] Probabilistic nature of machine learning systems
[43:10] Data scientists coping up on writing production code
[46:33] Invaluability of code review
[47:22] Common repo structure
[47:57] Reviewing codes
[49:15] Code pals
[50:36] Readability and function
[52:23] Leverage code review
[53:10] Remote work