Agentic Conversations (formally mlops.community)

Coffee Sessions #26 with Vishnu Rachakonda of Tesseract Health, Daniel Galinkin of iFood, Matias Dominguez of Rappi & Simarpal Khaira of Intuit, Feature Store Master Class.

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//Bio
Vishnu Rachakonda
Machine Learning Engineer at Tesseract Health. Coffee sessions co-host, but this time his role is one of the all-star guest speakers.

Daniel Galinkin
One of the co-founders of Hekima, one of the first companies in Brazil to work with big data and data science, with over 10 years of experience in the field. At Hekima, Daniel was among the people responsible for dealing with infrastructure and scalability challenges. After iFood acquired Hekima, he became the ML Platform Tech Lead for iFood.

Matias Dominguez  
A 29-year-old living in Buenos Aires, with past 4.5 years working on fraud prevention.  Previously at MercadoLibre and other random, smaller consulting shops.

Simarpal Khaira
Simarpal is the product manager driving product strategy for Feature Management and Machine Learning tools at Intuit. Prior to Intuit, he was at Ayasdi, a machine learning startup, leading product efforts for machine learning solutions in the financial services space. Before that, he worked at Adobe as a product manager for Audience Manager, a data management platform for digital marketing.

--------------- ✌️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

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Daniel on LinkedIn: https://www.linkedin.com/in/danielgalinkin/
Connect with Matias on LinkedIn: https://www.linkedin.com/in/mndominguez/
Connect with Simarpal on LinkedIn: https://www.linkedin.com/in/simarpal-khaira-6318959/ 

Timestamps:
[00:00] Introduction to guest speakers.
[00:33] Vishnu Rachakonda Background
[01:00] Guest speakers' Background
[03:13] Are Feature Stores for everyone?
[04:02] Guest speakers' Feature Store background
[17:09] How do you go about gathering requirements for a Feature Store and customizing it?
[17:34] Guest speakers' process for Feature Store
[31:14] What solution are we actually trying to build?
[36:42] How do you ensure consistency in your transformation logic and in your process for generating features?
[43:39] In terms of versioning that transformation logic and knowledge that goes into creating Feature Stores and allowing them to be reusable and consistent, how are you going to grapple with that?
[48:06] How do you bake in best practices into the services that you offer?
[49:34] "It's too possible for you to do something wrong. You have to specify that wrong thing. That makes it harder to do that wrong thing." Daniel
[51:54] "It starts with changing the mindset. Making people get the habit of what the value is here. Then you are producing features for consumers because tomorrow you could become a consumer. Write it in a way that you want to consume somebody's feature." Simar
[56:51] "As part of that process, it should come with everyone's best practices to actually improve all features," Matias

MLOps community meetup #47! Last Wednesday, we talked to Adrià Romero, Founder and Instructor at ProductizeML.

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// Abstract:
In this talk, we tackled:  
- Motivations and mission behind ProductizeML.
- Common friction points and miscommunication between technical and management/product teams, and how to bridge these gaps.  
- How to define ML product roadmaps (and more importantly, how to get them signed off by all your team).
- Best practices when managing the end-to-end ML lifecycle.

// Takeaways:
- Self-study guide that reviews the end-to-end ML lifecycle, starting with some ML theory, data access and management, MLOps, and how to wrap up all these pieces in a viable but still lovable product.  
- Free and collaborative self-study guide built by professionals with experience in different stages of the ML lifecycle.

// Bio:
Adrià is an AI, ML, and product enthusiast with more than 4 years of professional experience on his mission to empower society with data and AI-driven solutions.
Born and raised in the beautiful and sunny Barcelona, he began his journey in the AI field as an applied researcher at the Florida Atlantic University, where he published some of the first deep learning works in the healthcare sector. Attracted by the idea of deploying these ideas to the real world, he then joined Triage, a healthcare startup building healthcare solutions powered by AI, such as a smartphone app able to detect over 500 skin diseases from a picture. During this time, he has given multiple talks at conferences, hospitals, and institutions such as Novartis and Google. Previously, he interned at Huawei, Schneider Electric, and the Insight Center for Data Analytics.
Early this year, he started crafting ProductizeML, An Instruction and Interactive Guide for Teams Building Machine Learning Products, where he and a team of AI & product specialists carefully prepared content to assist in the end-to-end ML lifecycle.

----------- 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  

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Adria on LinkedIn: https://www.linkedin.com/in/adriaromero/
Related links:
https://en.wikipedia.org/wiki/ImageNet
https://twitter.com/productizeML https://course.productize.ml/
https://github.com/ProductizeML/gitbook
https://adria756514.typeform.com/to/V4BDqjYA - Newsletter Signup
https://www.buymeacoffee.com/

Timestamps:
[00:00] Introduction to Adrià Romero  
[00:32] How did you get into tech?
[02:16] ImagiNet Project (Visual Recognition Challenge)
[06:49] Visual Recognition with Skin Lesions
[07:05] Fundamental vs Applied Research (Academia experience)
[08:44] Motivation for technology
[14:55] Transition to ProductizeML
[19:09] ProductizeML Context
[23:50] What was it that made you think that Education is probably more powerful?
[24:21] ProductizeML Objective
[26:55] Ethics: Do you want to put that in there later?
[30:12] ProductizeML Content Format and Tools
[34:07] ProductizeML Catalog
[39:28] ProductizeML Audience Target
[42:54] "Buy me a coffee" platform
[48:29] Do you ever foresee the educational being more vertical-specific?

The revolution of Federated Learning - And we're back with another episode of the podcast When Machine Learning meets Privacy! For the episode #8 we've invited Ramen Dutta, a member of our community and founder of TensoAI.

// Abstract:

In this episode,  Ramen explain us the concept behind Federated Learning, all the amazing benefits and it's applications in different industries, particularly in agriculture. It's all about not centralizing the data, sound awkward? Just listen to the episode.

//Other links to check on Ramen:

https://www.linkedin.com/in/tensoai/

https://www.tensoai.com/

https://twitter.com/tensoAI

//Final thoughts

Feel free to drop some questions into our slack channel (https://go.mlops.community/slack) 

Watch some of the other podcast episodes and old meetups on the channel: https://www.youtube.com/channel/UCG6qpjVnBTTT8wLGBygANOQ

----------- 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

Connect with Fabiana on LinkedIn: https://www.linkedin.com/in/fabiana-clemente/

Connect with Ramen on LinkedIn: https://www.linkedin.com/in/tensoai/

Coffee Sessions #25 with Marian Ignev of CloudStrap.io & SashiDo.io, Most Underrated MLOps Topics.

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//Bio
Marian is a passionate entrepreneur, backend dude & visionary.
These are the three main things described to Marian very well:
Marian's everyday routines include making things happen and motivating people to work hard and learn all the time because I think success is a marathon, not just a sprint!
Marian loves to communicate with bright, creative minds who want to change things.  
His favorite professional topics are backend stuff, ops, infra, IoT, AI, Startups, and Entrepreneurship.  
In his free time, he loves to cook for my family.

--------------- ✌️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

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

Timestamps:
[00:00] Introduction to Marian Ignev
[01:57] Marian's background
[10:06] Who do you need and what should they be doing?
[18:05] How are you solving problems at your company?
[27:22] What are your thoughts around ML tooling? Why hasn't that happened yet, and why will it change?
[33:16] You can't actually figure out what the main focus of ML tooling services is.  
[34:14] "Start small and start simple to focus only on a small problem that you can bring more than the others."
[37:14] How are you making the case for how standardization is to occur in your initial MLOps?
[38:08] "If you're making a mistake somewhere, do it everywhere because it will be very easy to find and replace it after that."   
[41:50] How do you model monitoring?
[47:19] How would you recommend people get started?
[49:00] Ecosystem of Machine Learning in Eastern Europe

Other links you can check on Marian:
https://www.sashido.io/
https://www.cloudstrap.io/
https://twitter.com/mignevm.ignev.net/blog/  (Blog)
m.ignev.net/  (Personal Website)
fridaycode.net/  (FridayCode)

MLOps community meetup #46! Last Wednesday, we talked to Hendrik Brackmann, Director of Data Science and Analytics at Tide.

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// Abstract:
Tide is a U.K.-based FinTech startup with offices in London, Sofia, and Hyderabad. It is one of the first and the largest business banking platforms in the UK, with over 150,000 SME members. As of 2019, one of Tide’s main focuses is to be data-driven. This resulted in the formation of a Data Science and Analytics Team with Hendrik Brackmann at its head. Let's witness Hendrik's personal anecdotes in this episode!
// Bio:
After studying probability theory at the University of Oxford, Hendrik joined MarketFinance, an SME lender, in order to develop their risk models. Following multiple years of learning, he joined Finiata, a Polish and German lender, in order to build out their data science function. Not only did he succeed in improving the risk metrics of the company, but he also learnt to manage a different department as interim Head of Marketing.
Hendrik's job as Director of Data Science and Analytics at business bank Tide is to oversee data engineering, data science, insights and analytics, and data governance functions of Tide.
// Final thoughts
Please feel free to drop some questions you may have beforehand into our Slack channel
(https://go.mlops.community/slack)
Watch some old meetups on our YouTube channel:
https://www.youtube.com/channel/UCG6qpjVnBTTT8wLGBygANOQ
----------- 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
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Hendrik on LinkedIn: https://www.linkedin.com/in/hendrik-brackmann-b2b5477a/
Timestamps:
[00:00] Introduction to Hendrik Brackmann
[01:54] Hendrik's background in tech
[03:22] First Phase of the three epic journeys of Hendrik
[08:05] Were there some hiccups you were running into as you're trying to make things better?  
[10:50] Any other learnings that you got from that job that you want to pass along to us?
[11:50] You were doing all batches at that point, right?
[12:35] Phase 2: of Hendrik's epic journey
[15:11] Did you eventually cut down at the time that it took?
[15:50] Breakdown of Transformation terminologies and their importance
[19:03] What are some things that you would never do again?
[20:32] How did you see things more clearly? [22:30] Phase 3: Moving on to Tide
[24:46] Have you only worked with teams with one programming language?
[30:47] Did you try to open-source solutions, or did you just go right out to buy it?
[33:12] What is real-time for you? How much latency is there? How much time do you need?
[37:18] At what stage did you realize to get the feature store?  
[40:09] What would you recommend from a maturity standpoint to get a feature store?
[41:20] Can you summarize some of the greatest problems that the feature stores solve for you?   
[42:22] What problems does a feature store introduce, if any?
[44:39] Where do the model and the feature start from the perspective of a system in engineering?
[49:15] You need good data management in feature stores
[50:21] Have you ever used or built any feature stores that explicitly handle units and do dimensional analysis on derived features?
[54:46] What kind of models do you have up at the moment, and how do you test and monitor, and deploy the models?

Coffee Sessions #24 with Sara Robinson of Google, Machine Learning Design Patterns, co-hosted by Vishnu Rachakonda.

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//Bio
Sara is a Developer Advocate for Google Cloud, focusing on machine learning. She inspires developers and data scientists to integrate ML into their applications through demos, online content, and events. Before Google, she was a Developer Advocate on the Firebase team. Sara has a Bachelor’s degree from Brandeis University. When she’s not writing code, she can be found on a spin bike or eating frosting.

--------------- ✌️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

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Sara on LinkedIn: https://www.linkedin.com/in/sara-robinson-40377924/

Timestamps:
[00:00] Introduction to Sara Robinson  
[01:38] Sara's Background in Tech
[04:54] What were some things that jumped out at you right away with Machine Learning that are different?
[07:44] Sara's Transition to the Machine Learning realm.
[08:36] What is the role of a Developer Advocate?
[11:41] Compared to traditional software developer advocacy, what stands out to you as being different, unique, perhaps more fun about working in the Machine Learning realm as a Developer Advocate?
[13:40] "No one person has it right."
[15:27] Given how new this space is, how did you go about writing a book? What led you to write this book (Machine Learning Design Patterns)?  [19:00] Process of deciding to write the book
[21:46] What is it that made the focus of these design patterns?
[25:07] Who's the reader that you think who's gonna have this book on their shelf as a reference?
[26:42] How would you advise readers to go about reconciling these domain-based needs and the design patterns that you may suggest or identify? [31:20] Can you tell us about a time that some of the design patterns, as you're learning with your co-authors, have been useful to you?
[36:50] Workflow Pipeline breakdown in the book
[42:23] How do you think about that level of maturity in terms of thinking about the design patterns?
[46:06] How do I communicate in a design pattern? What if there is resistance to formalization or implementational structure because it might prevent creativity or reiteration?
[49:32] Pre-bill and custom components of Pipeline Frameworks
[51:28] How do we know to do the next step or stay in Feature Store patterns? [56:07] Are we going to see the convergence of tools and frameworks soon?

Resources referenced in this episode:
https://www.oreilly.com/library/view/machine-learning-design/9781098115777/
https://www.amazon.com/Machine-Learning-Design-Patterns-Preparation/dp/1098115783 https://books.google.com.ph/books/about/Machine_Learning_Design_Patterns.html?id=djwDEAAAQBAJ&redir_esc=y
https://amzn.to/38tM22C
https://sararobinson.dev/2020/11/17/writing-a-technical-book.html

Coffee Sessions #23 with Todd Underwood of Google, Follow-ups from OPML Talks on ML Pipeline Reliability co-hosted by Vishnu Rachakonda.

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//Bio
Todd is a Director at Google and leads Machine Learning for the Site Reliability Engineering Director. He is also Site Lead for Google’s Pittsburgh office. ML SRE teams build and scale internal and external ML services and are critical to almost every Product Area at Google.
Before working at Google, Todd held a variety of roles at Renesys.  He was in charge of operations, security, and peering for Renesys’s Internet intelligence services that are now part of Oracle's Cloud service. He also did product work for some early social products that Renesys worked on. Before that, Todd was Chief Technology Officer of Oso Grande, an independent Internet service provider (AS2901) in New Mexico.

--------------- ✌️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

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

Timestamps:
[00:00] Intro to Todd Underwood
[02:04] Todd's background
[08:54] What's kind of vision do you "paint"?
[14:54] Playing a little bit "devil's advocate." Do you think that's even possible?
[19:36] "Start serving to make sure of having the possibility to get it out." How do you feel about that?
[23:56] What advice could you give to other people who want to bring in ML professionals into their companies to make ML useful for them? [29:53] Is it useful to use these new models?  
[32:25] Do you feel like there would be a point where there would be a standard procedure?
[35:50] How machine learning breaks
[40:44] As an engineering leader, what's your advice to other engineering leaders in terms of how to make that reflection on your team's needs and failures...?  
[48:42] It's the design that you're looking at as the problem, not the person.
[56:27] Do we think that people sold a bunch of stuff, and now we are left with the results?     
[1:00:46] Recommendations on readings, things to do to better hone our craft.
[1:03:35] The more you explore, the more you realize, what's going on? Where can I learn from?
[1:05:00] Since you are in the mode of predicting things and philosophical background, where are you seeing the industry going in the next 5 years as we create it?

Resources referenced in this episode:
https://www.youtube.com/watch?v=Nl6AmAL3i08&feature=emb_title&ab_channel=USENIX
https://www.youtube.com/watch?v=hBMHohkRgAA&ab_channel=USENIX
https://youtu.be/0sAyemr6lzQ https://youtu.be/EyLGKmPAZLY
https://www.usenix.org/conference/opml20/presentation/papasian
https://www.usenix.org/system/files/login/articles/02_underwood.pdf
https://storage.googleapis.com/pub-tools-public-publication-data/pdf/da63c5f4432525bcaedcebeb50a98a9b7791bbd2.pdf

MLOps community meetup #45! Last Wednesday, we talked to Joe Reis, CEO/Co-Founder of Ternary Data.

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// Abstract:
The fact is that most companies are barely doing BI, let alone AI. Joe discussed ways for companies to build a solid data foundation so they can succeed with machine learning. This meetup covers the continuum from cloud data warehousing to MLOps.
// Bio:
Joe is a Data Engineer and Architect, Recovering Data Scientist, 20 years in the data game.  Joe enjoys helping companies make sense of their culture, processes, and architecture so they can go from dreaming to doing. He’s certified in both AWS and Google Cloud. When not working, you can find Joe at one of the two groups he co-founded—The Utah Data Engineering Meetup and SLC Python. Joe also sits on the board of Utah Python, a non-profit dedicated to advocating Python in Utah.

// Other links to check on Joe:
https://www.youtube.com/channel/UC3H60XHMp6BrUzR5eUZDyZg
https://josephreis.com/
https://www.ternarydata.com/
https://www.linkedin.com/pulse/what-recovering-data-scientist-joe-reis/
https://www.linkedin.com/pulse/should-you-get-tech-certification-depends-joe-reis/

----------- 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  

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Joe on LinkedIn: https://www.linkedin.com/in/josephreis/  

Timestamps:
[00:23] How did you get into tech? What brought you onto the journey into data?
[04:50] You got into the auto ML and you decided to branch out and do your own thing? How did that happen?
[08:18] What is it with BI and then making that jump to ML?
[11:00] How have you seen Machine Learning fall flat with trying to shoehorn Machine Learning on top of the already weak foundation of BI?
[13:45] Let's imagine we're doing BI fairly well, and now we want to jump to Machine Learning. Do we have to go out and reinvent the whole stack, or can we shoehorn it on?
[15:36] How do you move from BI to ML?
[18:24] What do you mean by real-time?  
[20:35] Managed Services in DevOps
[23:30] The maturity isn't there yet
[26:03] Where would you draw the line between BI and AI?
[30:45] What are the things is Machine Learning is an overkill for?
[33:43] Are you thinking about what data sets to collect and how different those vary?
[35:18] "Software Engineering and Data Engineering are basically going to merge into one."
[38:27] What do you usually recommend moving from BI to AI?
[40:45] What is "strong data foundation" in your eyes?
[42:47] "MLFlow to gateway drug." What's your take on it?
[46:25] In this pandemic, how easy is it for you to pivot to a new provider?
[49:10] Vision of companies starts coming together on different parts of the stack in the Machine Learning tools.

Coffee Sessions #22 with Carl Steinbach of LinkedIn, Deep in the Heart of Data.

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//Bio
Carl is a Senior Staff Software Engineer and currently the Tech Lead for LinkedIn's Grid Development Team. He is a contributor to Emerging Architectures for Modern Data Infrastructure

//Other links referenced by Carl:
https://rise.cs.berkeley.edu/wp-content/uploads/2017/03/CIDR17.pdf
https://www.youtube.com/watch?v=-xIai_FvcSk&ab_channel=WePayEngineering
https://softwareengineeringdaily.com/2019/10/23/linkedin-data-platform-with-carl-steinbach/
https://www.slideshare.net/linkedin/carl-steinbach-open-source
https://dreamsongs.com/RiseOfWorseIsBetter.html
https://engineering.linkedin.com/blog/2017/03/a-checkup-with-dr--elephant--one-year-later
https://engineering.linkedin.com/
https://engineering.linkedin.com/blog/2018/11/using-translatable-portable-UDFs
https://a16z.com/2020/10/15/the-emerging-architectures-for-modern-data-infrastructure/

--------------- ✌️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

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Carl on LinkedIn: https://www.linkedin.com/in/carlsteinbach/

Timestamps:
[00:00] Introduction to Carl Steinbach
[00:44] Carl's background
[04:51] Breakdown of Transpiler
[10:55] Advantages of Decoupling the Execution Layer
[15:25] Differences between UDF (user-defined function) Functions and Views
[18:45] How do you ensure the reproducibility of these Views?
[23:58] Data structure evolution
[27:55] Are Data Lakes and Data Warehouse fundamentally different things, or are they on a path towards conversion?
[33:37] It's inevitable that people will start doing machine learning on databases
[36:01] Who gets permission on what, especially when it comes to data and how sensitive things can be?
[41:27] Security aspect of data  
[43:40] Does it require a level of obstruction on top of the data of the file system?
[45:48] Why do we go back and go forward, which sets this trend?

ML and Encryption - It's all about secure insights #7! In this episode, we've invited Théo Ryffel, Founder of Arkhn and founding member of the Open-Mined community. 

// Abstract:

In this episode,  Théo introduces us to the concept of encrypted Machine Learning, when and the best practices to have it applied in the development of Machine Learning based solutions, and the challenges of building a community. 

//Other links to check on Théo:

https://twitter.com/theoryffel

https://arkhn.com

https://openmined.org

https://arxiv.org/pdf/1811.04017.pdf

https://arxiv.org/pdf/1905.10214.pdf

//Final thoughts

Feel free to drop some questions into our slack channel (https://go.mlops.community/slack) 

Watch some of the other podcast episodes and old meetups on the channel: https://www.youtube.com/channel/UCG6qpjVnBTTT8wLGBygANOQ

----------- 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

Connect with Fabiana on LinkedIn: https://www.linkedin.com/in/fabiana-clemente/

Connect with Théo on LinkedIn: https://www.linkedin.com/in/theo-ryffel