Luigi in Production // MLOps Coffee Sessions #18 // Luigi Patruno ML in Production

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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Episode source