Agentic Conversations (formally mlops.community)

MLOps Coffee Sessions #114 with Marc Lindner, Co-Founder, COO, and Amr Mashlah, Head of Data Science of eezylife Inc., Product Enrichment and Recommender Systems, co-hosted by Skylar Payne.

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// Abstract
The difficulties of making multi-modal recommender systems. How can it be easy to know something about a user but very hard to know the same thing about a product and vice versa? For example, you can clearly know that a user wants an intellectual movie, but it is hard to accurately classify a movie as intellectual and fully automated.

// Bio
Marc Lindner has a background in Knowledge Engineering. He's always extremely product-focused with anything to do with Machine Learning.   
Marc built several products working together with companies such as Lithium Technologies, etc., and then co-founded eezy.

Amr Mashlah
Amr is the head of data science at eezy, where he leads the development of their recommender engine. Amr has a master's degree in AI and has been working with startups for 6 years now.

// MLOps Jobs board  

jobs.mlops.community

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

// Related Links
Children of Time book by Adrian Tchaikovsky:  
https://www.amazon.com/Children-Time-Adrian-Tchaikovsky/dp/0316452505

--------------- ✌️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 Marc on LinkedIn: https://www.linkedin.com/in/marc-lindner-883a0883/
Connect with Amr on LinkedIn: https://www.linkedin.com/in/mashlah/

Timestamps:

[00:00] Takeaways

[04:20] Introduction to Marc Lindner and Amr Mashlah

[06:08] eezylife Inc.

[08:43] Integration with different tools

[10:13] Richer data for eezy

[12:58] Challenges with different providers

[15:25] Labeling solutions

[16:00] Maodal.com

[18:18] Handling multi-modal recommendation

[20:11] Figuring out the process of ingesting the data

[23:16] Ontology of sorts

[27:21] Talking in-depth about technical pieces

[28:40] Handling cold start

[31:42] Bad recommendations

[37:24] Curation vs hard-coded rules

[39:53] Social features

[41:57] eezy's vision

[45:12] Suggested recommendations

[46:48] Preferences from inferred interactions

[51:45] Ethical considerations

[54:19] Wrap up

MLOps Coffee Sessions #113 with Leanne Fitzpatrick, Director of Data Science of Financial Times, Building Better Data Teams, co-hosted by Mihail Eric.

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// Abstract
We spent a lot of time talking about data tooling, but we may not have spent as much time talking about data organizations and efficiently running and organizing data teams.   

What about starting with limitations instead of aspirations? Right constraints instead of the north star? In this session, let's learn more about a realistic take on the state of data organizations of today.

// Bio
Leanne is Director of Data Science at the Financial Times and is a passionate data leader with experience building and developing empowered data science and analytics teams in a variety of businesses. Leanne is in her element when developing and implementing strategic, technical, and cultural solutions to get machine learning and data science into the operational ecosystem.
Leanne is an active part of the data and technology community, sharing innovation and insights to encourage best practices, from Manchester, UK, to Austin, TX, and is an Advisory Panel Board Member. Outside of all things data, you can ask Leanne about her golf swing (it’s not good - yet), her passion for American Football (specifically the Cincinnati Bengals), her latest sewing project, and her love for good music, food, and whisky.

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

// Related
Links Children of Time book by Adrian Tchaikovsky:  
https://www.amazon.com/Children-Time-Adrian-Tchaikovsky/dp/0316452505

--------------- ✌️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 Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/
Connect with Leanne on LinkedIn: https://www.linkedin.com/in/leanne-kim-fitzpatrick-29204341/

Timestamps:
[00:00] Introduction to Leanne Fitzpatrick
[04:23] Write us your suggestions!
[05:43] Tri-pawed dog called Seaweed!
[08:43] How to architect data teams
[14:44] Organizational deficiencies
[19:19] Tensions and conflicts for starters
[24:07] Misunderstandings from marketing
[25:59] The Middle Layer
[28:48] Data science work at publications
[31:11] Mystique of going to real-time
[35:29] Third parties with fraud
[37:40] Augmenting data practitioners with third-party tools
[41:00] Principle of reinventing the wheel and avoiding undifferentiated heavy lifting  
[46:29] Different Abstraction Layer recommendations
[48:42] RN Production
[51:56] Will Python eat RN Production away?
[56:05] Julia as a dark horse
[56:39] Future of RN Production
[58:00] Rapid-fire questions

MLOps Coffee Sessions #112 with Xiangrui Meng, Principal Software Engineer of Databricks, MLX: Opinionated ML Pipelines in MLflow, co-hosted by Vishnu Rachakonda.

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// Abstract
MLX is to enable data scientists to stay mostly within their comfort zone, utilizing their expert knowledge while following the best practices in ML development and delivering production-ready ML projects, with little help from production engineers and DevOps.

// Bio
Xiangrui Meng is a Principal Software Engineer at Databricks and an Apache Spark PMC member. His main interests center around simplifying the end-to-end user experience of building machine learning applications, from algorithms to platforms and to operations.

// MLOps Jobs board

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

// Related Links
Good Strategy Bad Strategy: The Difference and Why It Matters, book by Richard Rumelt:
https://www.amazon.com/Good-Strategy-Bad-Difference-Matters/dp/0307886239

--------------- ✌️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 Xiangrui on LinkedIn: https://www.linkedin.com/in/mengxr/

Timestamps:
[00:00] Introduction to Xiangrui Meng
[00:39] Takeaways
[02:09] Xiangrui's background
[03:38] What kept Xiangrui in Databricks
[07:33] What needs to be done to get there
[09:20] Machine Learning passion of Xiangrui
[11:52] Changes in building that keep you fresh for the future
[14:35] Evolution core challenges to real-time and use cases in real-time
[17:33] DevOps + DataOps + ModelOps = MLOps
[19:21] MLFlow Support
[21:37] Notebooks to production debates  
[25:42] Companies tackling Notebooks for production
[27:40] MLOoops stories
[31:03] Opinionated MLOps productionizing in a good way
[40:23] Xiangrui's MLOps Vision
[44:47] Lightning round
[48:45] Wrap up

MLOps Coffee Sessions #111 with Samuel Partee, Principal Applied AI Engineer of Redis, More than a Cache: Turning Redis into a Composable, ML Data Platform, co-hosted by Mihail Eric. This episode is sponsored by Redis.

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// Abstract
Pushing forward the Redis platform to be more than just the web-serving cache that we've known it to be up to now. It seems like a natural progression for the platform. We see how they're evolving to be this AI-focused, AI native serving platform that does vector similarity, feature storage, and provides those kinds of functionalities.

// Bio
A Principal Applied AI Engineer at Redis, Sam helps guide the development and direction of Redis as an online feature store and vector database.   
Sam's background is in high-performance computing, including ML-related topics such as distributed training, hyperparameter optimization, and scalable inference.

// MLOps Jobs board  
jobs.mlops.community

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

// Related Links
https://partee.io
Redis VSS demo: https://github.com/Spartee/redis-vector-search
Redis Stack: https://redis.io/docs/stack/
Github - https://github.com/Spartee  
OSS org Sam co-founded at HPE/Cray - https://github.com/CrayLabs
This paper last year was some of the best research and collaborations Sam has been a part of. The Paper is published here: https://www.sciencedirect.com/science/article/pii/S1877750322001065?via%3Dihub
Do you really need an extra database for vectors? https://databricks.com/dataaisummit/session/emerging-data-architectures-approaches-real-time-ai-using-redis
Blink: The Power of Thinking Without Thinking by Malcolm Gladwell,  Barry Fox,  Irina Henegar (Translator): https://www.goodreads.com/book/show/40102.Blink

--------------- ✌️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 Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/
Connect with Sam on LinkedIn: www.linkedin.com/in/sam-partee-b04a1710a

Timestamps:
[00:00] Introduction to Samuel Partee
[00:24] Takeaways
[02:46] Updates on the Community
[05:17] Start of Redis
[08:10] Vision for Vector Search
[11:05] Changing the narrative, going from the "Cache" for all servers and web endpoints
[14:35] Clear value prop on demos
[20:17] Vector Database
[26:26] Features with benefits
[28:41] AWS Spend
[30:39] Vector Database upsell model and bureaucratic convenience  
[32:08] Distributed training hyperparameter optimization and scalable inference
[35:03] Core infrastructural advancement
[36:55] Tools movement to help
[39:00] Using Machine Learning at scale in numerical simulations with SmartSim: An application to ocean climate modeling (published paper)

[42:52] Future applications of tech to get excited about
[44:20] Lightning round
[47:48] Wrap up

MLOps Coffee Sessions #110 with David Bayliss, Chief Data Scientist of LexisNexis Risk Solutions, Just Fetch the Data and then... co-hosted by Vishnu Rachakonda.

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

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// Abstract
Composing data to extract features can be a significant problem. Key factors are the data size, compliance restrictions, and real-time data. Ethics (and law) can drive extremely complex audit requirements. In the cloud, you can do anything - at a price.

// Bio
One of the creators of the world's first big data platform (HPCC), David has been tackling big data problems for two decades. A mathematician, compiler writer, and data sponge with more than five dozen patents spanning platforms, linking, and search.

Most inventors think outside the box; David can't even remember where the box is. He leads the team that creates their core Data Science methods used by hundreds of data scientists.

// MLOps Jobs board  

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

// Related Links
Interesting insight in this post. It would be cool to learn from David about his view on things
https://www.google.com/url?q=https://www.linkedin.com/posts/david-bayliss-426556a_datascience-platform-portability-activity-6913448643303759872-2dqq?utm_source%3Dlinkedin_share%26utm_medium%3Dmember_desktop_web&sa=D&source=calendar&ust=1649078059106132&usg=AOvVaw26wAevExeEfW_AdZSA8UhF

--------------- ✌️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 David on LinkedIn: https://www.linkedin.com/in/david-bayliss-426556a/

Timestamps:
[00:00] Introduction to David Bayliss
[01:03] Takeaways
[04:56] LexisNexis and David's role
[07:15] Evolution of LexisNexis in 20 years with so many use cases
[08:51] Role of David in structuring data for working with data change
[14:32] Data management and data access
[17:45] Unique challenges of scale, use case, and diversity at LexisNexis
[24:47] Tardis Iron Box
[30:05] Iron Box translation
[32:56] JVM for data science
[34:24] Iron Box meaning
[36:52] Metadata with PII
[39:08] Detrimental privacy / Hairy Kneecap Theory
[40:57] Speeding things up and Anonymized linking
[46:47] What kept David working at LexisNexis?
[50:30] Wrap up

MLOps Coffee Sessions #110 with David Bayliss, Chief Data Scientist of LexisNexis Risk Solutions, Just Fetch the Data and then... co-hosted by Vishnu Rachakonda.

MLOps Coffee Sessions #109 with Ketan Umare, Co-founder and CEO of Union.ai, Why You Need More Than Airflow, co-hosted by George Pearse.

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

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// Abstract
Airflow is a beloved tool by data engineers and Machine Learning Engineers alike. But when doing ML, what are the shortcomings, and why is an orchestration tool like that not always the best developer experience? In this episode, we break down what some key drivers are for using an ML-specific orchestration tool.

// Bio
Ketan Umare is the CEO and co-founder at Union.ai. Previously, he had multiple Senior roles at Lyft, Oracle, and Amazon, ranging from Cloud, distributed storage, Mapping (map-making), and machine-learning systems. He is passionate about building software that makes engineers' lives easier and provides simplified access to large-scale systems. Besides software, he is a proud father and husband, and enjoys traveling and outdoor activities.

// MLOps Jobs board  
jobs.mlops.community

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

// Related Links
Zero to One: Notes on Startups, or How to Build the Future, Hardcover by Peter Thiel  and Blake Masters:
https://www.amazon.com/Zero-One-Notes-Startups-Future/dp/0804139296

--------------- ✌️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/george-pearse-b7a76a157/?originalSubdomain=uk
Connect with Ketan on LinkedIn: https://www.linkedin.com/in/ketanumare/

Timestamps:

[00:00] Introduction to Ketan Umare

[01:08] Takeaways

[04:03] Changes with Ketan after 2 years

[10:06] Diametrically opposed software and data infrastructure

[13:06] Airflow shortcomings

[21:14] Core abstractions

[24:56] Machine Learning specific packages

[28:25] Core target users

[30:50] Airflow foundational level

[33:46] Better visualization of data science pipelines

[36:39] Flyte for RL Deployment

[38:36] Role of signals in the system

[40:59] Everything in Flyte is run through an API

[42:20] Manual intervention in the system

[45:11] Union vs Flyte

[50:16] Security precautions

[51:56] Role of Union cloud

[56:20] Distinctiveness of Flyte pipelines

[01:01:11] Ketan in his current company

[01:02:25] Learning from Airflow

[01:04:58] Lightning round with Ketan

[01:09:59] Wrap up

MLOps Coffee Sessions #108 with Byron Allen, AI & ML Practice Lead at Contino, ML Flow vs Kubeflow 2022 co-hosted by George Pearse.

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

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// Abstract

The amazing Byron Allen talks to us about why MLflow and Kubeflow are not playing the same game!  
ML flow vs Kubeflow is more like comparing apples to oranges, or as he likes to make the analogy, they are both cheese, but one is an all-rounder and the other a high-class delicacy. This can be quite deceiving when analyzing the two. We do a deep dive into the functionalities of both and the pros/cons they have to offer.

// Bio

Byron wears several hats. AI & ML practice lead, solutions architect, ML engineer, data engineer, data scientist, Google Cloud Authorized Trainer, and scrum master. He has a track record of successfully advising on and delivering data science platforms and projects. Byron has a mix of technical capability, business acumen, and communication skills that make me an effective leader, team player, and technology advocate.   

See Byron write at https://medium.com/@byron.allen

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MLOps Swag/Merch
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// 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 George on LinkedIn: https://www.linkedin.com/in/george-pearse-b7a76a157/?originalSubdomain=uk
Connect with Byron on LinkedIn: https://www.linkedin.com/in/byronaallen/

Timestamps:
[00:00] Introduction to Byron Allen
[01:10] Introduction to the new co-host, George Pearse
[01:41] ML Flow vs Kubeflow
[05:40] George's take on ML Flow and Kubeflow
[07:28] Writing in YAML
[09:47] Developer experience
[13:38] Changes in ML Flow and Kubeflow
[17:58] Messing around ML Flow Serving
[20:00] A taste of Kubeflow through K-Serve
[23:18] Managed service of Kubeflow
[25:15] How George used Kubeflow
[27:45] Getting the Managed Service
[31:30] Getting Authentication
[32:41] ML Flow docs vs Kubeflow docs
[36:59] Kubeflow community incentives
[42:25] MLOps Search term
[42:52] Organizational problem
[43:50] Final thoughts on ML Flow and Kubeflow
[49:19] Bonus [49:35] Entity-Centric Modeling
[52:11] Semantic Layer options
[57:27] Semantic Layer with Machine Learning
[58:40] Satellite Infra Images demo
[1:00:49] Motivation to move away from SQL
[1:03:00] Managing SQL
[1:05:24] Wrap up

MLOps Coffee Sessions #107 with Ryan Russon, Manager, MLOps and Data Science of Maven Wave Partners, Why and When to Use Kubeflow for MLOps, co-hosted by Mihail Eric.  

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

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// Abstract
Kubeflow is an excellent platform if your team is already leveraging Kubernetes, and it allows for a truly collaborative experience.
Let’s take a deep dive into the pros and cons of using Kubeflow in your MLOps.  

// Bio
From serving as an officer in the US Navy to Consulting for some of America's largest corporations, Ryan has found his passion in the enablement of Data Science workloads for companies and teams.     
Having spent years as a data scientist, Ryan understands the types of challenges that DS teams face in scaling, tracking, and efficiently running their workloads.  

// MLOps Jobs board  
jobs.mlops.community

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

// Related Links
https://www.mavenwave.com/
https://go.mlops.community/hFApDb

--------------- ✌️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 Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/
Connect with Ryan on LinkedIn: https://www.linkedin.com/in/ryanrusson/

Timestamps:
[00:00] Introduction to Ryan Russon
[01:13] Takeaways
[04:17] Bullish on KubeFlow!
[06:23] KubeFlow in ML tooling
[11:47] Kubeflow is having its velocity
[14:16] To Kubeflow or not to Kubeflow
[18:25] KubeFlow ecosystem maturity
[20:51] Alternatively, starting from scratch?
[23:11] Argo workflow vs KubeFlow pipelines
[25:08] KubeFlow as an end-state for citizen data scientists
[28:24] End-to-end workflow key players  
[31:17] K-serve
[33:41] KubeFlow on orchestrators
[36:24] Natural transition to KubeFlow maturity
[41:33] "Don't forget about the engineer cost."
[42:21] KubeFlow to other "Flow brothers" trade-offs
[46:12] Biggest MLOps challenge
[49:52] Best practices around file structure
[52:15] KubeFlow changes over the years and what to expect moving forward
[55:52] Best-of-breed vision
[57:54] Wrap up

MLOps Coffee Sessions #107 with Ryan Russon, Manager, MLOps and Data Science of Maven Wave Partners, Why and When to Use Kubeflow for MLOps co-hosted by Mihail Eric.