215 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
    • 5.0 • 8 Ratings

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.

    Real-time Machine Learning: Features and Inference // Sasha Ovsankin and Rupesh Gupta // MLOps Podcast #134

    Real-time Machine Learning: Features and Inference // Sasha Ovsankin and Rupesh Gupta // MLOps Podcast #134

    MLOps Coffee Sessions #134 with Sasha Ovsankin and Rupesh Gupta, Real-time Machine Learning: Features and Inference co-hosted by Skylar Payne.

    // Abstract
    Moving from batch/offline Machine Learning to more interactive "near" real-time requires knowledge, team, planning, and effort. We discuss what it means to do real-time inference and near-real-time features when to do this move, what tools to use, and what steps to take.

    // Bio
    Sasha Ovsankin Sasha is currently a Tech Lead of Machine Learning Model Serving infrastructure at LinkedIn, worked also on Feathr Feature Store, Real-Time Feature pipelines, designed metric platforms at LinkedIn and Uber, and was co-founder in two startups. Sasha is passionate about AI, Software Craftsmanship, improvisational music, and many more things.

    Rupesh Gupta
    Rupesh is a Sr. Staff Engineer in the AI team at LinkedIn. He has 10 years of experience in search and recommender systems.

    // 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 Skylar on LinkedIn: https://www.linkedin.com/in/skylar-payne-766a1988/
    Connect with Sasha on LinkedIn: https://www.linkedin.com/in/sashao/
    Connect with Rupesh on LinkedIn: https://www.linkedin.com/in/guptarupesh

    • 53 min
    Real-time Machine Learning with Chip Huyen // Chip Huyen // MLOps Coffee Sessions #133

    Real-time Machine Learning with Chip Huyen // Chip Huyen // MLOps Coffee Sessions #133

    MLOps Coffee Sessions #133 {Podcast BTS} with Chip Huyen, Real-time Machine Learning with Chip Huyen co-hosted by Vishnu Rachakonda.

    // Abstract
    Forcing functions and how you can supercharge your learning by putting yourself into a situation where you know you either have a responsibility to others to learn or accountability on you so you have to learn.  

    It's not that hard when you think about streaming machine learning. It's not that big of a mental barrier to cross. It is simple in theory but maybe it's more complicated in practice and that's exactly where Chip's perspective is.

    // Bio
    Chip Huyen is a co-founder of Claypot AI, a platform for real-time machine learning. Previously, she was with Snorkel AI and NVIDIA. She teaches CS 329S: Machine Learning Systems Design at Stanford. She’s the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI.

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

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

    // Related Links
    Landing page: https://claypot.ai
    Designing Machine Learning Systems book:
    https://www.amazon.com/Designing-Machine-Learning-Systems-Production-Ready/dp/1098107969

    --------------- ✌️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 Chip on LinkedIn: https://www.linkedin.com/in/chiphuyen/

    • 58 min
    What is Data / ML Like on League? // Ian Schweer // MLOps Coffee Sessions #132

    What is Data / ML Like on League? // Ian Schweer // MLOps Coffee Sessions #132

    MLOps Coffee Sessions #132 {Podcast BTS} with Ian Schweer, What is Data / ML Like on League? co-hosted by Skylar Payne.  

    // Abstract
    If you're not an avid gamer yourself, you have never really seen how ML might be used in the gaming space. It's so interesting to see the things that are different like full stories of players' games from start to finish.  

    // Bio
    On the surface, Ian is an excellent developer who gets things done. Underneath, he is much more. Ian is a reliable and trustworthy teammate who demonstrates an exceptional ownership mentality.  

    Here's a fair share of Ian's job history:
    2014 - UCI (With Skylar!)
    2015 - Adobe Primetime (SWE)
    2017 - Adobe Product and Customer Analytics (SWE)
    2019 - Doordash Data Infra (SWE) Current - Riot Games on League  

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

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

    // Related Links Landing page:
    https://www.riotgames.com/en

    --------------- ✌️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 Skylar on LinkedIn: https://www.linkedin.com/in/skylar-payne-766a1988/
    Connect with Ian on LinkedIn: https://www.linkedin.com/in/ianschweer/

    Timestamps:
    [00:00] Ian's preferred coffee
    [02:10] Takeaways
    [05:14] Please hit the like button and leave us a review. Please subscribe also!
    [05:45] Engineering Community Mental Health Awareness
    [07:33] Coping mechanism
    [09:29] Increase in video game playing  
    [11:20] Ian's career progression
    [17:55] Lessons to apply in the Data space
    [24:23] Challenges at Riot
    [34:18] Real-time element
    [39:09] Ian's day-to-day responsibilities
    [43:13] Analysis vs. Production Code Quality
    [48:11] Tools and techniques on the reality of writing production codes
    [55:00] What would you change your career into?
    [57:00] Ian's best practices advise
    [58:28] Ian's favorite video game
    [59:58] Wrap-up

    • 1 hr
    Let's Continue Bundling into the Database // Ethan Rosenthal // MLOps Coffee Sessions #131

    Let's Continue Bundling into the Database // Ethan Rosenthal // MLOps Coffee Sessions #131

    MLOps Coffee Sessions #131 {Podcast BTS} with Ethan Rosenthal, Let's Continue Bundling into the Database co-hosted by Mike Del Balso.

    // Abstract
    The relationship between ML Engineers and Product Managers is something that we don't talk about enough. We've got to get this right. If we don't get this right, either you're not focusing on the business problems in the right way or the Product Managers are not going to understand the tech appropriately to help make the right decisions.

    // Bio
    Ethan works on the Conversations Team at Square leading a team of Artificial Intelligence Engineers. Ethan's team builds applied AI solutions for Square Messages, a messaging hub for Square merchants to communicate with their customers. Prior to Square, Ethan spent time as a freelance data science consultant building machine learning products for a range of companies, from pre-seed startups to Fortune 100 enterprises.   

    Ethan got his start in data science working at two different e-commerce startups, Birchbox and Dia&Co. Before data science, Ethan was an actual scientist and got his Ph.D. in experimental physics from Columbia University.

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

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

    // Related Links
    https://www.ethanrosenthal.com/
    Relevant blog posts:  
    https://www.ethanrosenthal.com/2022/05/10/database-bundling/
    https://www.ethanrosenthal.com/2022/07/19/materialize-ml-monitoring/
    https://www.ethanrosenthal.com/2022/01/18/autoretraining-is-easy/

    --------------- ✌️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 Mike on LinkedIn: https://www.linkedin.com/in/michaeldelbalso/
    Connect with Ethan on LinkedIn: https://www.linkedin.com/in/ethanrosenthal/

    Timestamps:
    [00:00] Ethan's preferred coffee
    [00:10] Introduction to co-host Mike Del Balso
    [00:43] Takeaways
    [04:10] Sign up for our newsletter!
    [04:47] Ethan's team
    [06:49] Ethan's team improvement
    [08:40] Product manager role at Square
    [10:39] Large Language Models
    [12:22] Big questions to figure out
    [15:45] Cost of false-positive
    [18:20] Company Vocabulary
    [20:11] MLOps concerns and challenges around Large Language Models
    [23:42] Data learning management
    [27:36] Leveling trade-offs
    [30:25] Ethan's Database Bundling blog
    [34:32] Feature Stores
    [38:24] Streaming databases
    [41:57] Best of both worlds trade-off highlight
    [43:51] Rosenthal data
    [46:40] Ethan's freelancing
    [47:46] Risk-reward trade-off
    [49:17] Ethan as a professor
    [51:14] Wrap up

    • 51 min
    MLOps for Ad Platforms // Andrew Yates // MLOps Coffee Sessions #130

    MLOps for Ad Platforms // Andrew Yates // MLOps Coffee Sessions #130

    MLOps Coffee Sessions #130 {Podcast BTS} with Andrew Yates, Adversarial MLOps on Other People's Money: Lessons Learned from Ad Tech co-hosted by Abi Aryan.

    // Abstract
    Design ML to be components in a larger system with stable interfaces is not tracible to monitor the entire stack as a black box. You need intermediate ground-truth signals. Ads are designed in this way.

    You can go from profitable to non-profitable real quick with ads. This will determine whether your company is around a year or two. You play with money and sometimes you play a lot of it so make sure that it's correct.

    // Bio
    Andrew Yates formerly led ads ranking, auction, and marketplace engineering and research teams at Facebook and Pinterest. He specializes in designing billion-dollar content marketplaces that maximize long-term revenue while protecting both seller and user experiences. Andrew has published over a dozen patents in online advertising optimization.

    // 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 Abi on LinkedIn: https://www.linkedin.com/in/abiaryan/
    Connect with Andrew on LinkedIn: https://www.linkedin.com/in/andrew-yates-0217a985/

    • 44 min
    Voice and Language Tech // Catherin Breslin // Coffee Sessions #129

    Voice and Language Tech // Catherin Breslin // Coffee Sessions #129

    MLOps Coffee Sessions #129 {Podcast BTS} with Catherin Breslin, Voice and Language Tech co-hosted by Adam Sroka.

    // Abstract
    Back in the day, Speech Recognition was its own thing. It's a very different flavor of Data Science. You could not use a lot of the tools. It wouldn't cross over to this type of machine learning.

    Now, with the advancements, Speech Recognition and Machine learning are coming in together. It's interesting to hear right from someone with a Ph.D. level working with some of the biggest companies in the world doing it. The fact that something like Alexa is lots of models back to back and just fathom the complexity of that is quite cool!

    // Bio
    Catherine is a machine learning scientist and consultant based in Cambridge UK, and the founder of Kingfisher Labs consulting. Since completing her Ph.D. at the University of Cambridge in 2008, Catherine has commercial and academic experience in automatic speech recognition, natural language understanding, and human-computer dialogue systems, having previously worked at Cambridge University, Toshiba Research, Amazon Alexa, and Cobalt Speech. Catherine has been excited by the application of research to real-world problems involving speech and language at scale.

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

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

    // Related Links
    www.catherinebreslin.co.uk
    https://catherinebreslin.medium.com/
    MLOps Community Newsletter: https://airtable.com/shrx9X19pGTWa7U3YTwitter: https://twitter.com/catherinebuk

    --------------- ✌️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 Adam on LinkedIn: https://www.linkedin.com/in/aesroka
    Connect with Catherine on LinkedIn: https://www.linkedin.com/in/catherine-breslin-0592423a/

    Timestamps:
    [00:00] Catherine's preferred coffee
    [01:50] Takeaways
    [03:59] Introduction to Catherine Breslin
    [05:04] Subscribe to our newsletter!
    [06:25] Catherine's background
    [08:13] Speech Recognition trajectory
    [09:36] Challenges around technologies and tools
    [11:34] Reflective trend
    [13:02] Developer experiences hiccups
    [15:09] Speech Recognition use case backup
    [16:56] Toshiba research
    [17:48] Transition from a research lab to working in the industry
    [20:01] Unit test of Speech Recognition
    [20:56] Alexa
    [22:33] Maturity process of Speech Recognition
    [26:48] Speech Recognition unrecognizing challenges
    [30:38] Mechanical Terk
    [33:00] Social media listening
    [34:05] Pipeline models and speed of Speech Recognition
    [36:48] Development of Speech Recognition excited about
    [37:23] Data from people for the Speech Recognition system vs Scowering news vs watching Youtube for a long time
    [40:00] Disappearing Languages
    [41:30] Future of an online practice partner
    [43:17] Speech-to-speech translation
    [44:04] Interesting ways to use unfamiliar models to achieve a result
    [45:40] Meeting transcriptions
    [48:37] First toy problem of a new Speech Recognition learner
    [51:37] Kingfisher Labs' problems to tackle
    [52:18] Off-the-shelf solution
    [53:38] Translation layer
    [54:15] Connect with Catherine on Twitter and LinkedIn for available jobs
    [54:43] Wrap up

    • 49 min

Customer Reviews

5.0 out of 5
8 Ratings

8 Ratings

goalieagk ,

Interesting discussions covering a rapidly developing field

I’m a senior ML engineer who deals with a lot of MLOps related items since we don’t have a dedicated role for that on our team. This show (and the Slack community) have been great resources for inspiration and staying up-to-date on the constant evolution of tools and best practices. It’s very useful to hear from other practitioners as we all try to navigate this landscape together

bmorphism ,

This podcast is art! Grazie ragazzə 🎉

Long time listener, glad the show is going strong. 🦾

More generative art with ANNs in production, please! Looking forward to applying insights from Valerio Velardo episode in my genart collaboration for DEF CON AI Village

uber_gadgetz ,

Your Guide to Successful MLOps!

Great interviews that tease out everything you need to know regarding the MLOps tooling, best practices, and engineering culture required to execute successful machine learning operations!

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