Coffee Sessions #18  with Luigi Patruno of ML in Production, a Centralized Repository of Best Practices
Summary
Luigi Patruno and ML in production
MLOps workflow: Knowledge sharing and best practices
Objective: learn!
Links:
ML in production: https://mlinproduction.com/
Why you start MLinProduction: https://mlinproduction.com/why-i-started-mlinproduction/
Luigi Patruno: a man whose goal is to help data scientists, ML engineers, and AI product managers, build and operate machine learning systems in production.
Luigi shares with us why he started ML in Production - A lot irrelevant content; a lot of clickbait with low standards of quality.
He had an Entrepreneurial itch and The solution was to start a weekly newsletter. From there he started creating Blog posts and now teamed up with Sam Charrington of TWIML to create courses on SagMaker ML. 
Applied ML
Best practices
Reading google and microsoft papers
Analyzing the tools that are out there ie sagemaker and how to the see the world?
Aimed at making you more effective and efficient at your job
Community questions
Taking some time to answer some community questions!
Who do you learn from? Favorite resources?
Self-taught, papers, talks
Construct the systems
Uber michelangelo

----------------- ? Rought notes ? ----------------
Any companies that stand out to you in terms of MLOps excellence?
Google, Amazon, Stichfix: they've had to solve hard problems
Serving ads
Personalization at scale
Vertical problems: within their vertices
Motivated by real challenges
DropBox
Great articles
A great machine learning company
Tools
Sagemaker
Has a course on sagemaker
Nice lessons baked into the system
Dos and don’t of MLOps
DO LOG!
Monitor
Automate - manual analysis leads to problems
Do it manually first til you feel confident that you can automate it
Tag, version
Store your training, val, and test sets!

What is his process of identifying use cases that are suitable for machine learning as a solution? How do they proceed methodically?
Start with business goal
Potential number of users that the solution can benefit
The ability to build a predictive model
Performance x impact = score
Rank problems by this
How developed are the datasets?
What part of the ML in Production process do people underestimate the most? What are the low hanging fruits that many people don’t take advantage of?
Generate actual value without needing to build the most complex model possible
In industry, performance is only one part of the equation
How has he seen ML in production evolve over the last few years and where does he think it's headed next?
More and more tools!
Industry-specific tool taking advantage of ML
Problem is you must have industry knowledge 

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