207 episodes

Weekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations.

MLOps.community Demetrios Brinkmann

    • Technology

Weekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations.

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

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

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

    // 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 how 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 it’s 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 Masters in Business Administration from Duke University, where he was a Fuqua Scholar.

    // MLOps Jobs board  
    https://mlops.pallet.xyz/jobs

    // 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/

    • 51 min
    Databricks Model Serving V2 // Rafael Pierre // Coffee Sessions #125

    Databricks Model Serving V2 // Rafael Pierre // Coffee Sessions #125

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

    // 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  
    https://mlops.pallet.xyz/jobs

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

    • 55 min
    Monitoring Unstructured Data // Aparna Dhinakaran & Jason Lopatecki // Lightning Sessions #2

    Monitoring Unstructured Data // Aparna Dhinakaran & Jason Lopatecki // Lightning Sessions #2

    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 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  
    https://mlops.pallet.xyz/jobs

    // Related Links
    https://arize.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 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] How 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

    • 10 min
    Trustworthy Machine Learning // Kush Varshney // Coffee Sessions #125

    Trustworthy Machine Learning // Kush Varshney // Coffee Sessions #125

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

    // 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 that 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  
    https://mlops.pallet.xyz/jobs

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

    // Related Links


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

    • 58 min
    RECOMMENDER SYSTEM: Why They Update Models 100 Times a Day // Gleb Abroskin // MLOps Coffee Sessions #123

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

    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.

    // Abstract
    FunCorp was a top 10 app store. It was a very popular app that has 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 make 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  
    https://mlops.pallet.xyz/jobs

    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 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 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
    [32:04] Missing piece of the whole Feature Store picture
    [33:54] Throwing ideas turn around time
    [36:37] Decrease time to market
    [37:41] Continuous training pipelines or 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

    • 52 min
    Scaling Similarity Learning at Digits // Hannes Hapke // Coffee Sessions #122

    Scaling Similarity Learning at Digits // Hannes Hapke // Coffee Sessions #122

    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 tooling looks like.   

    Hannes maps out what good data hygiene looks like not only from the machine learning perspective 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-author two machine learning books:
    * Building Machine Learning Pipeline (O'Reilly)
    * NLP in Action (Manning)

    // MLOps Jobs board  
    https://mlops.pallet.xyz/jobs

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

    // Related Links


    --------------- ✌️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 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

    • 57 min

You Might Also Like

Tobias Macey
Sam Charrington
Changelog Media
Jon Krohn and Guests on Machine Learning, A.I., and Data-Career Success
Software Engineering Daily
Michael Kennedy (@mkennedy)