Agentic Conversations (formally mlops.community)

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

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// 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.

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--------------- ✌️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/

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

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.

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// 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.

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// 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

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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

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

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// 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)

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--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
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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

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

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// 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.

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// 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 Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
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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 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

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.

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// Abstract
When David started building Feathr, Feature Stores did not exist. That was not a term floating around at all. This was definitely one of the OG Feature Stores for sure!  
We hear how the LinkedIn team got to this point, having an open source release, and how they used LinkedIn as an incubator to build a great product.

// 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/

// Related Links
https://engineering.linkedin.com/blog/2022/open-sourcing-feathr---linkedin-s-feature-store-for-productive-m
* https://github.com/linkedin/feathr
* https://engineering.linkedin.com/blog/2022/open-sourcing-feathr---linkedin-s-feature-store-for-productive-m
* https://azure.microsoft.com/en-us/blog/feathr-linkedin-s-feature-store-is-now-available-on-azure/

--------------- ✌️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 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!

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.

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

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// 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/

// Related Links
engineering.linkedin.com/hadoop/user-engagement-powered-apache-pig-and-hadoop
* https://doordash.engineering/2020/07/20/enabling-efficient-machine-learning-model-serving/
* https://doordash.engineering/2020/11/19/building-a-gigascale-ml-feature-store-with-redis/
* https://doordash.engineering/2021/03/04/building-a-declarative-real-time-feature-engineering-framework/
* https://doordash.engineering/2021/05/20/monitor-machine-learning-model-drift/
* https://doordash.engineering/2021/01/26/computational-graph-machine-learning-ensemble-model-support/

--------------- ✌️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 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

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

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// 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 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 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

MLOps Coffee Sessions #117 with Chad Sanderson, Head of Product, Data Platform at Convoy, Data Engineering for ML, co-hosted by Josh Wills.

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// Abstract
Data modeling is building relationships between core concepts within your data. The physical data model shows how the relationships manifest in your data environment, but then there's the semantic data model, the way that entity relationship design is extracted away from any data-centric implementation.  
Let's have the good old fun of talking about why data modeling is so important!

// Bio
Chad Sanderson is the Product Lead for Convoy's Data Platform team, which includes the data warehouse, streaming, BI & visualization, experimentation, machine learning, and data discovery.
Chad has built everything from feature stores, experimentation platforms, metrics layers, streaming platforms, analytics tools, data discovery systems, and workflow development platforms. He’s implemented open source, SaaS products (early and late-stage) and has built cutting-edge technology from the ground up. Chad loves the data space, and if you're interested in chatting about it with him, don't hesitate to reach out.

// MLOps Jobs board

jobs.mlops.community  

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

// Related Links
https://odsc.com/speakers/scaling-machine-learning-with-data-mesh/ https://docs.google.com/presentation/d/1rVtltHkRkP_JaGZdkAS3U_SXfr5Gg-RP980FKXh0YNU/edit?usp=sharing
Josh Wills will be teaching a course on Data Engineering for Machine Learning in September here:
https://www.getsphere.com/ml-engineering/data-engineering-for-machine-learning

--------------- ✌️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 Josh on LinkedIn: https://www.linkedin.com/in/josh-wills-13882b/
Connect with Chad on LinkedIn: https://www.linkedin.com/in/chad-sanderson/

Timestamps:
[00:00] Introduction of the new co-host, Josh Wills  
[00:54] Introduction to Chad Sanderson
[01:46] Josh will lead a course on Machine Learning in mid-September
[02:16] Data modeling blog post of Chad
[06:10] Idea of Strategy
[09:40] Modern cloud data warehouses  
[17:01] Layering on contracts
[20:38] Scaling at larger companies
[25:30] Carrot-stick strategy
[34:27] Second and third-order effects
[39:53] Stockholm Syndrome
[41:22] Quality checks at Slack
[45:28] Success in two main ways, according to Chad
[47:35] Completely and utterly different universes
[53:42] Product use case to push semantic events
[56:00] Pattern of analysis of the sequence of events
[57:23] Wrap up

MLOps Coffee Sessions #116 with Shawn Kyzer, Principal Data Engineer at Thoughtworks (Spain), Scaling Machine Learning with Data Mesh, co-hosted by Adam Sroka.

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// Abstract
You can't just get something done by using tools. You need to go much deeper than that, and it is very clear that Data Mesh is the same thing. You have to educate the organization about the movement.  
In this session, Shawn broke down the cultural piece of data mesh and how many parallels there are with the MLOps Movement when it comes to the cultural side of MLOps.

// Bio
Shawn is passionate about harnessing the power of data strategy, engineering, and analytics in order to help businesses uncover new opportunities. As an innovative technologist with over 13 years of experience, Shawn removes technology as a barrier and broadens the art of the possible for business and product leaders. His holistic view of technology and emphasis on developing and motivating strong engineering talent, with a focus on delivering outcomes whilst minimising outputs, is one of the characteristics that sets him apart from the crowd.

Shawn’s deep technical knowledge includes distributed computing, cloud architecture, data science, machine learning, and engineering analytics platforms. He has years of experience working as a consultant practitioner for a variety of prestigious clients, ranging from secret clearance-level government organizations to Fortune 500 companies.

// MLOps Jobs board  
jobs.mlops.community

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

// Related Links
https://odsc.com/speakers/scaling-machine-learning-with-data-mesh/ https://docs.google.com/presentation/d/1rVtltHkRkP_JaGZdkAS3U_SXfr5Gg-RP980FKXh0YNU/edit?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 Adam on LinkedIn: https://www.linkedin.com/in/aesroka/
Connect with Shawn on LinkedIn: https://www.linkedin.com/in/shawn-kyzer-msit-mba-b5b8a4b/

Timestamps:
[00:00] Introduction to Shawn Kyzer
[00:43] Takeaways
[04:00] Data Mesh for ML projects
[11:22] The signal for the exploratory part of a new modeling project
[14:13] Ownership and centralization
[16:20] Lack of technology and some implementations of literature
[17:10] Python stronghold from Microsoft blogs
[23:09] Integration with self-serve data platform
[25:31] Starting a platform team
[30:04] Quick wins
[32:09] Metrics monitoring
[34:18] Metrics break up
[38:32] Limit to capabilities and not worth doing
[41:39] Culture and technology hold
[44:03] Setting the foundation
[46:53] Unforeseen benefits
[52:19] Lightning question

MLOps Coffee Sessions #115 with Flaviu Vadan, Senior Software Engineer at Dyno Therapeutics, How Hera is an Enabler of MLOps Integrations, co-hosted by Vishnu Rachakonda.

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// Abstract
Flaviu talks about the internal ML platform at Dyno Therapeutics called Hera. His team uses Hera as an internal innovation engine to help discover new breakthroughs with machine learning in the biotech healthcare industry.

// Bio
Flaviu is a Senior Software Engineer at Dyno Therapeutics, the leading organization in the design of novel gene therapy vectors with transformative delivery properties for a vast landscape of human diseases. Flaviu comes from a background focused on Bioinformatics, which is a field that combines Computer Science, Mathematics, and Biology. He took stints in academia by working as a research assistant in Computer Science and Bioinformatics labs before joining Dyno Therapeutics to work on machine-guided design of adeno-associated viruses (AAVs).   

At Dyno, Flaviu works on compute and core infrastructure, DevOps, MLOps, and approaches that combine AI/ML to design AAVs in silico. He is also the author and maintainer of Hera, a Python SDK that facilitates access to Argo Workflows by making workflow construction and submission easy and accessible.

// MLOps Jobs board  
jobs.mlops.community

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 Vishu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Flaviu on LinkedIn: https://www.linkedin.com/in/flaviuvadan/

Timestamps:
[00:00] Introduction to Flaviu Vadan
[00:50] Takeaways
[02:06] Share this episode with a friend!
[03:20] What Dyno does
[05:44] CRISPR and Gene Editing
[06:21] Kidney transplants and using pig organs
[07:31] Deciding what genes to put in the body  
[07:48] Role of ML at Dyno
[10:07] Higher dose
[13:41] Process of Machine Learning Deployment and Productionizing at Dyno
[16:22] Proliferation of models
[17:31] Building the internal platform
[19:37] Interaction with data, translation to compute layer, evaluation
[24:21] Venn diagram for MLOps
[27:06] Leveraging Argo Workflows
[30:34] Hera
[35:28] Open sourcing
[38:44] Human power at Dyno
[41:17] Wrap up