Agentic Conversations (formally mlops.community)

Latest Episodes:

ML Unicorn Start-up Investor Tells-IT-All // George Mathew // MLOps Coffee Sessions #126 Oct 04, 2022

Show notes

MLOps Coffee Sessions #126 with George Mathew, ML Unicorn Start-up Investor, Tells-IT-All.

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

// Abstract
What's so enticing about enterprise software? It's incredible to see George's idea and vision to invest in generationally enduring companies.
Let's look at the way George likes to structure deals with companies while he's reviewing them, and let's look at the MLOps ecosystem through the eyes of the investors.

// Bio
George Mathew joins Insight Partners as a Managing Director focused on venture stage investments in AI, ML, Analytics, and Data companies as they are establishing product/market Fit.

He brings 20+ years of experience developing high-growth technology startups, including most recently being CEO of Kespry. Prior to Kespry, George was President & COO of Alteryx, where he scaled the company through its IPO (AYX). Previously, he held senior leadership positions at SAP and Salesforce.com. He has driven company strategy, led product management and development, and built sales and marketing teams.
George holds a Bachelor of Science in Neurobiology from Cornell University and a Master's in Business Administration from Duke University, where he was a Fuqua Scholar.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
https://www.insightpartners.com/

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with George on LinkedIn: https://www.linkedin.com/in/gmathew/

Timestamps:

[00:00] George's preferred coffee

[00:13] Introduction and background to George

[03:02] Prompting infrastructure

[06:43] George deploying startups

[11:07] Different areas in the MLOps space

[16:18] MLOps is broad

[18:46] Factors which George is interested in

[23:02] What's fascinating in Enterprise Software

[27:17] George's transition story

[29:46] Company stage to invest for George

[31:51] Evaluating KPIs for deals

[39:22] The deals that George had

[41:58] George's senses

[43:59] Liquidation preferences

[45:50] The duration from the first meeting to the term sheet

[48:43] Meeting sources

[50:03] Wrap up

Databricks Model Serving V2 // Rafael Pierre // Coffee Sessions #125 Sep 30, 2022

Show notes

MLOps Coffee Sessions #125 with Rafael Pierre, Deploying Real-time ML Models in Minutes with Databricks Model Serving V2, co-hosted by Ryan Russon.

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

// Abstract
From our experience helping customers in the Data and AI field, we learned that the most challenging part of Machine Learning is deploying it. Putting models into production is complex and requires additional pieces of infrastructure as well as specialized people to take care of it - this is especially true if we are talking about real-time REST APIs for serving ML models.
With Databricks Model Serving V2, we introduce the idea of Serverless REST endpoints to the platform. This allows teams to easily deploy their ML models in a production-grade platform with a few mouse clicks (or lines of code 😀).

// Bio
Rafael has worked for 15 years in data-intensive fields within finance in multiple roles: software engineering, product management, data engineering, data science, and machine learning engineering.
At Databricks, Rafael has fun bringing all these topics together as a Solutions Architect to help our customers become more and more data-driven.

// MLOps Jobs board
jobs.mlops.community

MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
https://mlopshowto.com
Airflow Summit 2022:
https://youtu.be/JsYEOdRBgREING
Data Engineering Meetup:
https://www.youtube.com/watch?v=gJoxX1rRZJI
MLOps World Virtual Summit NYC 2022:
https://drive.google.com/file/d/1EXsqmLfrPAsV9i6h6pGfJxVjMO9y6u9a/view?usp=sharing

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Ryan on LinkedIn: https://www.linkedin.com/in/ryanrusson/
Connect with Rafael on LinkedIn: https://www.linkedin.com/in/rafaelpierre

Timestamps:

[00:00] Rafael's preferred coffee

[00:14] Intro and background of Ryan

[01:18] Takeaways

[04:15] Rafael's first job and first touch in data

[06:35] IOT with no cloud

[08:00] Rafa's personal experience with Kubernetes

[09:16] Hardest thing to grasp when starting

[10:49] ML in Kubernetes

[13:32] Why is Kubernetes a good platform for ML?

[15:06] Is Kubernetes always the right answer?

[19:30] Recap of Ale Solano's background

[20:30] Cultural side of MLOps

[23:07] Ryan's experience

[25:04] Databricks over Kubernetes

[27:20] What to gain from Databricks?

[29:28] Best way to structure and promote code

[32:13] Rationalizing tradeoffs

[34:08] Difference from a mature software org

[35:26] Best way to deal with artifacts

[38:21] RBAC is a must-have

[41:10] Favorite tools for modeling

[42:19] Wrap up

Monitoring Unstructured Data // Aparna Dhinakaran & Jason Lopatecki // Lightning Sessions #2 Sep 27, 2022

Show notes

Lightning Sessions #2 with Aparna Dhinakaran, Co-Founder and Chief Product Officer, and Jason Lopatecki, CEO and Co-Founder of Arize. Lightning Sessions is sponsored by Arize

// Abstract
Monitoring embeddings on unstructured data is not an easy feat, let's be honest. Most of us know what it is but don't understand it one hundred percent.
Thanks to Aparna and Jason of Arize for breaking down embedding so clearly. At the end of this Lightning talk, we get to see a demo of how Arize deals with unstructured data and how you can use Arize to combat that.

// Bio
Aparna Dhinakaran
Aparna is the Co-Founder and Chief Product Officer at Arize AI, a pioneer and early leader in machine learning (ML) observability. A frequent speaker at top conferences and a thought leader in the space, Dhinakaran was recently named to the Forbes 30 Under 30. Before Arize, Dhinakaran was an ML engineer and leader at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michaelangelo.
Aparna has a BA from Berkeley's Electrical Engineering and Computer Science program, where she published research with Berkeley's AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University.

Jason Lopatecki
Jason is the Co-founder and CEO of Arize AI, a machine learning observability company. He is a garage-to-IPO executive with an extensive background in building marketing-leading products and businesses that heavily leverage analytics. Prior to Arize, Jason was co-founder and chief innovation officer at TubeMogul where he scaled the business into a public company and eventual acquisition by Adobe.
Jason has hands-on knowledge of big data architectures, programmatic advertising systems, distributed systems, and machine learning and data processing architectures. In his free time, Jason tinkers with personal machine learning projects as a hobby, with a special interest in unsupervised learning and deep neural networks. He holds an electrical engineering and computer science degree from UC Berkeley.

// MLOps Jobs board

jobs.mlops.community
// Related Links
https://arize.com/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Aparna on LinkedIn: https://www.linkedin.com/in/aparnadhinakaran/
Connect with Jason on LinkedIn: https://www.linkedin.com/in/jason-lopatecki-9509941/

Timestamps:
[00:00] Introduction to the topic
[01:13] Troubleshooting unstructured ML models is difficult
[01:40] Challenges with monitoring unstructured data
[02:10] What data looks like
[03:02] Embeddings are the backbone of unstructured models
[03:28] ML teams need a common tool
[04:06] What are embeddings?
[05:08] The real WHY behind AI
[06:41] ML observability for unstructured data
[07:08] Index and Monitor every Embedding
[08:05] Measuring drift of unstructured data
[08:54] Interactive visualizations
[09:34] Fix underlying data issue
[09:44] Data-centric AI workflow
[10:08] Demo of the product
[12:48] Wrap up

Trustworthy Machine Learning // Kush Varshney // Coffee Sessions #124 Sep 21, 2022

Show notes

MLOps Coffee Sessions #124 with Kush Varshney, Distinguished Research Staff Member and Manager, IBM Research, Trustworthy Machine Learning, co-hosted by Krishnaram Kenthapadi.

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

// Abstract
Trustworthy ML is a way of thinking and something to be worked on and operationalized throughout the entire machine learning development lifecycle, starting from the problem specification phase, which should include diverse stakeholders.

// Bio
Kush R. Varshney was born in Syracuse, New York, in 1982. He received a B.S. degree (magna cum laude) in electrical and computer engineering with honors from Cornell University, Ithaca, New York, in 2004. He received the S.M. degree in 2006 and the Ph.D. degree in 2010, both in electrical engineering and computer science at the Massachusetts Institute of Technology (MIT), Cambridge. While at MIT, he was a National Science Foundation Graduate Research Fellow.

Dr. Varshney is a distinguished research staff member and manager with IBM Research at the Thomas J. Watson Research Center, Yorktown Heights, NY, where he leads the machine learning group in the Foundations of Trustworthy AI department. He was a visiting scientist at IBM Research - Africa, Nairobi, Kenya in 2019. He is the founding co-director of the IBM Science for Social Good initiative. He applies data science and predictive analytics to human capital management, healthcare, olfaction, computational creativity, public affairs, international development, and algorithmic fairness, which has led to recognitions such as the 2013 Gerstner Award for Client Excellence for contributions to the WellPoint team and the Extraordinary IBM Research Technical Accomplishment for contributions to workforce innovation and enterprise transformation. He conducts academic research on the theory and methods of trustworthy machine learning. His work has been recognized through best paper awards at the Fusion 2009, SOLI 2013, KDD 2014, and SDM 2015 conferences and the 2019 Computing Community Consortium / Schmidt Futures Computer Science for Social Good White Paper Competition. He self-published a book entitled 'Trustworthy Machine Learning in 2022, available at http://www.trustworthymachinelearning.com. He is a senior member of the IEEE.

// MLOps Jobs board
jobs.mlops.community

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Krishnaram on LinkedIn: https://www.linkedin.com/in/krishnaramkenthapadi
Connect with Kush on LinkedIn: https://www.linkedin.com/in/kushvarshney/

Timestamps:

[00:00] Kush Varshney's introduction

[02:16] Takeaways

[07:56] Talking about TRUST

[12:44] Trustworthy AI

[19:32] Adopting Trustworthy Machine Learning

[22:19] Weapons of Math Destruction book - biases

[30:34] Problem sources

[38:08] Self-publishing a Trustworthy Machine Learning book

[43:36] Changing the Nature of AI Research by Professor Subbarao Kambhampati

[50:08] IBM research support

[52:00] Wrap up

RECOMMENDER SYSTEM: Why They Update Models 100 Times a Day // Gleb Abroskin // MLOps Coffee Sessions #123 Sep 16, 2022

Show notes

MLOps Coffee Sessions #123 with Gleb Abroskin, Machine Learning Engineer at Funcorp, RECOMMENDER SYSTEM: Why They Update Models 100 Times a Day, co-hosted by Jake Noble.

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

// Abstract
FunCorp was a top 10 app store. It was a very popular app that had a ton of downloads and just memes. They need a recommendation system on top of that. Memes are super tricky because they're user-generated and they evolve very quickly. They're going to live and die by the Recommender System in that product.
It's incredible to see FunCorp's maturity. Gleb breaks down the feature store they created and the velocity they have to be able to create a whole new pipeline in a new model and put it into production after only a month!

// Bio
Gleb makes models go brrrrr. He doesn't know what is expected in this field, to be honest, but Gleb has experience in deploying a lot of different ML models for CV, speech recognition, and RecSys in a variety of languages (C++, Python, Kotlin), serving millions of users worldwide.

/ MLOps Jobs board
jobs.mlops.community

MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
Putting a two-layered recommendation system into production -
https://medium.com/@FunCorp/putting-a-two-layered-recommendation-system-into-production-b8caaf61393d
Practical Guide to Create a Two-Layered Recommendation System -
https://medium.com/@FunCorp/practical-guide-to-create-a-two-layered-recommendation-system-5486b42f9f63
Ten Mistakes to Avoid When Creating a Recommendation System -
https://medium.com/@FunCorp/ten-mistakes-to-avoid-when-creating-a-recommendation-system-8268ed60aeba
Applying Domain-Driven Design and Patterns: With Examples in C# and .NET, 1st Edition by Jimmy Nilsson:
https://www.amazon.com/Applying-Domain-Driven-Design-Patterns-Examples/dp/0321268202

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Jake on LinkedIn: https://www.linkedin.com/in/jakednoble/
Connect with Gleb on LinkedIn: https://www.linkedin.com/in/gasabr/

Timestamps:
[00:00] Introduction to Gleb Abroskin
[00:50] Takeaways
[05:39] Breakdown of FunCorp teams
[06:47] FunCorp's team ratio
[07:41] FunCorp team provisions
[08:48] Feature Store vision
[10:16] Matrix factorization
[11:51] Fairly modular, fairly thin infrastructure
[12:26] Distinct models with the same feature
[13:08] FunCorp's definition of Feature Store
[15:10] Unified API
[15:55] FunCorp's scaling direction
[17:01] Level up as needed
[17:38] Future of FunCorp's Feature Store
[18:37] Monitoring investment in the space
[19:43] Latency for business metrics
[21:04] Velocity to production
[23:10] 30-day retention struggle
[24:45] Back-end business stability
[27:49] Recommender systems
[30:34] Back-end layer headaches
[37:41] Continuous training pipelines produce an artifact
[39:33] Worst-case scenario
[40:38] Realistic estimation of a new model deployment
[41:42] Recommender Systems' future velocity
[43:07] A/B Testing launch - no launch decision
[46:32] Lightning question
[47:08] Wrap up

Scaling Similarity Learning at Digits // Hannes Hapke // Coffee Sessions #122 Sep 09, 2022

Show notes

MLOps Coffee Sessions #122 with Hannes Hapke, Machine Learning Engineer at Digits Financial, Inc., Scaling Similarity Learning at Digits, co-hosted by Vishnu Rachakonda.

// Abstract
Machine Learning in a product is a double-edged sword. It can make a product more useful, but it depends on assumed and strictly defined behavior from users.
Hannes walks through the entirety of their machine learning pipeline, how they implemented it, what the elements are, what the learning looks like, and what the tooling looks like.
Hannes maps out what good data hygiene looks like, not only from the machine learning perspective, but also down to the software engineering, design, and backend engineering, all the way to the data engineering perspectives.

// Bio
Hannes was the first ML engineer at Digits, where he built the MLOPs foundation for their ML team. His interest in production machine learning ranges from building ML pipelines to scaling similarity-based ML to process millions of banking transactions daily.
Prior to Digits, Hannes implemented ML solutions for a number of applications, incl. retail, health care, or ERP companies.

He co-authored two machine learning books:
* Building Machine Learning Pipeline (O'Reilly)
* NLP in Action (Manning)

// MLOps Jobs board
jobs.mlops.community

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Hannes on LinkedIn: https://www.linkedin.com/in/hanneshapke/

Timestamps:
[00:00] Introduction to Hannes Hapke
[01:37] Takeaways
[02:40] Design supercharges machine learning
[05:48] Building Machine Learning Pipeline book
[08:09] Updating the edition
[09:37] Abstract away
[11:52] Approach of crossover
[16:04] Training serving skew
[20:42] Tools using continuous integration and deployment
[25:25] Human in the loop touch point
[27:44] Data backfilling update
[30:06] Work and Products of Digits
[32:26] Digit Boost
[35:30] The first machine learning engineer
[39:55] Structured data in good shape, good data processing perspective, concept-educated teams
[43:33] Digits is hiring!
[43:55] Machine Learning struggles
[47:10] Design decision
[49:49] Data or machine learning literacy
[51:30] Data Hygiene
[52:49] Rapid-fire questions
[54:47] Wrap up

Bringing DevOps Agility to ML// Luis Ceze // Coffee Sessions #121 Sep 06, 2022

Show notes

MLOps Coffee Sessions #121 with Luis Ceze, CEO and Co-founder of OctoML, Bringing DevOps Agility to ML, co-hosted by Mihail Eric.

// Abstract
There's something about this idea where people see a future where you don't need to think about infrastructure. You should just be able to do what you do, and infrastructure happens.
People understand that there is a lot of complexity underneath the hood, and most data scientists or machine learning engineers start deploying things, and shouldn't have to worry about the most efficient way of doing this.

// Bio
Luis Ceze is Co-Founder and CEO of OctoML, which enables businesses to seamlessly deploy ML models to production, making the most out of the hardware. OctoML is backed by Tiger Global, Addition, Amplify Partners, and Madrona Venture Group. Ceze is the Lazowska Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he has taught for 15 years.
Luis co-directs the Systems and Architectures for Machine Learning lab (sampl.ai), which co-authored Apache TVM, a leading open-source ML stack for performance and portability that is used in widely deployed AI applications.

Luis is also co-director of the Molecular Information Systems Lab (misl.bio), which led pioneering research in the intersection of computing and biology for IT applications such as DNA data storage. His research has been featured prominently in the media, including the New York Times, Popular Science, MIT Technology Review, and the Wall Street Journal. Ceze is a Venture Partner at Madrona Venture Group and leads their technical advisory board.

// MLOps Jobs board

jobs.mlops.community

MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
Landing page: https://octoml.ai/
The Boys in the Boat: Nine Americans and Their Epic Quest for Gold at the 1936 Berlin Olympics by Daniel James Brown:
https://www.amazon.com/Boys-Boat-Americans-Berlin-Olympics/dp/0143125478

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/
Connect with Luis on LinkedIn: https://www.linkedin.com/in/luis-ceze-50b2314/

Timestamps:
[00:00] Introduction to Luis Ceze
[06:28] MLOps does not exist
[10:41] Semantics argument
[16:25] Parallel programming standpoint
[18:09] TVM
[22:51] Optimizations
[24:18] TVM in the ecosystem
[27:10] OctoML's further step
[30:42] Value chain
[33:58] Mature players
[35:48] Talking to SREs and Machine Learning Engineers
[36:32] Building OctoML
[40:20] My Octopus Teacher
[42:15] Environmental effects of Sustainable Machine Learning
[44:50] Bridging the gap from OctoML to biological mechanisms
[50:02] Programmability
[57:13] Academia making an impact
[59:40] Rapid-fire questions
[1:03:39] Wrap up

Feathr: LinkedIn's High-performance Feature Store // David Stein // Coffee Sessions #120 Sep 02, 2022

Show notes

MLOps Coffee Sessions #120 with David Stein, Senior Staff Software Engineer at LinkedIn, Feathr: LinkedIn's Enterprise-Grade, High-Performance Feature Store, co-hosted by Skylar Payne.

// Bio
David Stein is a tech lead at LinkedIn, working on machine learning feature infrastructure. He is the original architect of Feathr and continues to contribute to its development as well as to other parts of LinkedIn's machine learning platform.

// MLOps Jobs board

jobs.mlops.community

MLOps Swag/Merch
https://mlops-community.myshopify.com/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Skylar on LinkedIn: https://www.linkedin.com/in/skylar-payne-766a1988/
Connect with David on LinkedIn: https://www.linkedin.com/in/steindavidj/

Timestamps:
[00:00] Introduction to David Stein
[00:30] Takeaways
[06:58] David and Skylar's reunion
[08:34] Feathr's background
[12:04] Lessons learned in building Feathr
[16:35] Scale of Feathr
[20:25] Systems interaction
[24:15] Standardization
[29:20] Importance and better difference of Feathr
[34:30] Feature Stores' evolution and more generally MLOps
[37:57] Challenges in real-time
[40:09] Going real-time, you're not ready!
[42:24] Use cases are strong for real-time unleveraged now
[45:50] Proud about
[47:58] Going back in time
[50:47] Feathr as a separate company?
[52:53] LinkedIn is hiring!

MLOps at DoorDash // Hien Luu and DoorDash Leads // Coffee Sessions #119 Aug 30, 2022

Show notes

SlackMLOps Coffee Sessions #119 with Hien Luu, Sr. Engineering Manager of DoorDash, MLOps at DoorDash: 3 Principles for Building an ML Platform That Will Sustain Hypergrowth, co-hosted by Skylar Payne.

// Abstract
Machine Learning plays a big part at DoorDash in terms of what they do on a daily basis. It powers many of their core infrastructures.
When it comes to DoorDash's business, they have to be leveraging machine learning, and it is such a huge piece of the business that it is critical.

// Bio
Hien Luu is an Engineering Manager at DoorDash, leading the Machine Learning platform team at DoorDash. He is particularly passionate about the intersection between Artificial Intelligence and Big Data. He is the author of the Beginning Apache Spark 3 book. He has given presentations at various conferences like Data+AI Summit, MLOps World, Deep Learning Summit, and apply() conference.

// MLOps Jobs board

jobs.mlops.community

MLOps Swag/Merch
https://mlops-community.myshopify.com/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Skylar on LinkedIn: https://www.linkedin.com/in/skylar-payne-766a1988/
Connect with Hien on LinkedIn: https://www.linkedin.com/in/hienluu/

Timestamps:
[00:00] Introduction of the DoorDash team
[01:58] Overview of DoorDash
[03:32] DoorDash's platform
[13:23] Experimenting and testing new models
[15:15] Experience transferring
[17:16] Effective engagement with customers
[24:15] Team sizes
[25:37] Metrics
[33:25] App for users
[34:04] Using Databricks and Snowflake together
[37:49] Supporting power users
[40:17] Advice and experiences
[43:53] Wrap up

ML Platforms, Where to Start? // Olalekan Elesin // Coffee Sessions #118 Aug 26, 2022

Show notes

MLOps Coffee Sessions #118 with Olalekan Elesin, Director of Data Platform & Data Architect at HRS Product Solutions GmbH, co-hosted by Vishnu Rachkonda.

// Abstract
You don't have infinite resources? Call out your main metrics! Focus on the most impactful things that you could do for your data scientists. Olalekan joined us to talk about his experience previously building a machine learning platform at Scaleout24.
From our standpoint, this is the best demonstration and explanation of the role of technical product management in ML that we have on the podcast so far!

// Bio
Olalekan Elesin is a technologist with a successful track record of delivering data-driven technology solutions that leverage analytics, machine learning, and artificial intelligence. He combines experience working across 2 continents and 5 different market segments, ranging from telecommunications, e-commerce, online marketplaces, and current business travel.

Olalekan built the AI Platform 1.0 at Scout24 and currently leads multiple data teams at HRS Group. He is an AWS Machine Learning Community Hero in his spare time.

// MLOps Jobs board

jobs.mlops.community

MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
What Customers Want: Using Outcome-Driven Innovation to Create Breakthrough Products and Services book by Anthony Ulwick: https://www.amazon.com/What-Customers-Want-Outcome-Driven-Breakthrough/dp/0071408673
Empowered: Ordinary People, Extraordinary Products by Marty Cagan:
https://www.amazon.com/EMPOWERED-Ordinary-Extraordinary-Products-Silicon/dp/111969129X
How to Avoid a Climate Disaster: The Solutions We Have and the Breakthroughs We Need by Bill Gates:
https://www.amazon.com/How-Avoid-Climate-Disaster-Breakthroughs/dp/059321577X

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Olalekan on LinkedIn: https://www.linkedin.com/in/elesinolalekan/

Timestamps:
[00:00] Introduction to Olalekan Elesin
[00:42] Takeaways
[02:52] Situation at Scaleout24
[07:53] Data landscape engineer and architect
[11:27] Depiction of events
[13:53] Platform approach investment
[15:59] Exceptional need or opportunity to the most intense need
[17:41] Long-tail pieces
[22:01] Metrics
[24:15] Nitty-gritty product works
[26:00] Educating people on metrics
[30:02] Upskilling fundamentals of the product discipline
[34:05] Investing in AWS
[37:53] Best-of-breed tools
[44:34] Continuous development for AutoML
[47:26] Rapid-fire questions
[52:19] Wrap up

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