🔥 The Most Epic MLOps Newsletter To Date 🔥

Background: About 4 months ago we teamed up with community member David Scharbach to get the word out about the MLOPs world conference that he and his team were organizing. I'm pleased to announce that the good folks at TMLS are holding another event on November 18 and 19 around machine learning!

See here how this interactive machine learning conference works, and how it will benefit your career. The goal is that each pass helps you understand business strategy, as well as technical applications for machine learning.

Topics covered;

You'll have access to 15 bonus workshops (ranging from technical to non-technical) and various breakout sessions with experts who will present use-cases and answer audience Q+A. (Docker, Amazon, NASA, Shopify, Google AI, etc). Come learn cutting-edge applications and as well, how to put more of your models into production!

We will also be raffling off 3 tickets this week in slack so keep an eye on the #general channel to participate. Not one for raffles? Just wanna get the tickets? Here is a link for that.

*It should be noted that these are affiliate links, therefore the community gets a kickback when you buy the ticket. The first order of business with the money we make from this will be to create swag for all of us.

David and I have finally managed to speak with none other than Mr. ML in Production Luigi Patruno himself! We bounced all over the place cause he has so much experience and wisdom we wanted to try and get the most out of it! We sourced the questions to ask him from you all in slack and boy oh boy were there some good ones. Sadly we didnt get through all of them but the good news is Luigi said he enjoyed the interview and would come back for a part 2! 💥

So what questions did we ask?

Who does he learn from? Favorite resources?
Any companies that stand out in terms of MLOps excellence?
Dos and don’t of MLOps
What is his process of identifying use cases that are suitable for machine learning as a solution? How do they proceed methodically?
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?
How has he seen ML in production evolve over the last few years and where does he think it's headed next?

My Favorite quote from him was around automation and it's something that David and I have been discussing at length in the series around the google paper continuous delivery for ML. Luigi said:

"Do it manually first til you feel confident that you can automate it, then automate so you avoid unwanted human errors"

Check out our whole conversation on podcast or on video. Let us know what we should ask as follow up questions for our next session.

Ravi was a great sport even after I managed to steal his thunder at the beginning of the meetup and show my favorite slide from the Netflix engineering blog post that introduces Metaflow!

If there was one thing that stuck out at me about Metaflow is the commitment to reproducibility and the version everything mentality!

Metaflow is only a pip install away, you can have a play with it and check out some updated tutorials here. For our full conversation have a listen here or watch the video here.

More Good News: I'll be talking with another one of Ravi's teammates on the Netflix engineering team Savin Goyal on December 9th. If you have any questions for him please let me know!

Data privacy and PII are hot tops on our minds these days especially if you are working in highly regulated sectors. Today I get the pleasure of announcing a new collaboration and a first within the community! We prepared a series on Machine Learning and Data Privacy, roping in guests who are experts in their fields to talk about everything from Ethics and Privacy to Synthetic data and ways to keep your data secure.

The first episode is now out, and will leave you wondering how much impact privacy will have on Machine Learning and visa versa! And no, I have not lost my beard or suddenly become prettier!

This is also the first time the community has collaborated with a company to bring you a sponsored series. I'd love to hear your feedback and feelings on it. Speaking of sponsored, we should probably mention who was doing the sponsoring!

This series is brought to you by YData. YData offers a dataset experimentation platform with synthetic data generation that makes the process of building datasets take a fraction of the time and cost that they used to.

For our most recent Medium post, community member David Hershey wrote an incredible piece around the difficulties of going from ML research to production. This article stems from the original conversation we had around the topic while he was on the weekly meetup and we decided to expand on the idea in a longer blogpost.

We wanted to examine deep learning efforts that keep getting stuck after a proof of concept phase and why that could be. In the article David breaks down how you may need to revisit the process you have in place for developing models, making sure:

You start prototyping with a plan for production
You manage your experiments and artifacts in a way that you can use in prod
Keep diligent track of your data preprocessing -- you'll need in in prod.

David also breaks down tools that are out there to make it easier to build and maintain a consistent ML process to make the transition to production smoother.

We have got a very special meetup for you all this week! None other than Mark Craddock will talk to us about The global platform he helped to create at the UN.

Stats on the platform: Streams 600,000,000+ records / day. The Strategy was developed using Wardley Maps and the Platform Design Toolkit.

Bio: Mark contributed to the Cloud First policy for the UK Public sector and was one of the founding architects for the UK Governments G-Cloud programme. Mark developed the initial CloudStore which enabled the UK Public Sector to procure cloud services from over 2,500 suppliers. The UK Public Sector has now purchased over
£6.3Bn of cloud services, with £3.6Bn from Small to Medium Enterprises in the UK.

Mark lead the development of the United Nations Global Platform. A multi-cloud platform for capacity building within the national statistics offices in the use of Big Data and its integration with administrative sources, geospatial information, traditional survey and census data.

Meetup Details: As always we will be meeting on Wednesday (aka tomorrow) at 5pm GMT / 9am PST. some have asked me about calendar invites for the recurring event, follow these links so you can add to google cal, outlook and Yahoo cal