Agentic Conversations (formally mlops.community)

MLOps community meetup #52! Last Wednesday, we talked to Luke Feeney and Gavin Mendel-Gleason, TerminusDB.

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// Abstract:
A look at the open-source 'Git for Data' landscape with a focus on how the various tools fit into the pipeline. Following that scene-setting, we will delve into how and why TerminusDB builds a revision control database from the ground up.

// Takeaways
- Understanding the 'git for data' offering and landscape
- See how to technically approach a revision control database implementation
- Dream of a better tomorrow

// Bio:

Luke Feeney - Operations Lead, TerminusDB  

Luke Feeney is Operations Director at TerminusDB. Prior to joining TerminusDB, Luke worked in the Irish Foreign Ministry for a number of years. He served in Ireland’s Permanent Mission to the UN in New York and the Embassies in South Africa and Greece. He was Ireland’s acting Ambassador to Greece for 2016 and 2017. Luke was also the Head of the Government of Ireland’s Brexit Communications Team and the Government Brexit Spokesperson from 2017 to 2018.

Gavin Mendel-Gleason - Chief Technology Officer, TerminusDB  

Dr Gavin Mendel-Gleason is CTO of TerminusDB. He is a former research fellow at Trinity College Dublin in the School of Statistics and Computer Science. His research focuses on databases, logic, and verification in software engineering. His work includes contributing to the Seshat global historical databank, an ambitious project to record and analyze patterns in human history. He is the inventor of the Web Object Query Language and the primary architect of TerminusDB. He is interested in improving the best practices of the software development community and is a strong believer in formal methods and the use of mathematics and logic as disciplines to increase the quality and robustness of software.

----------- 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 Luke on LinkedIn: https://www.linkedin.com/in/luke-feeney/
Connect with Gavin on LinkedIn: https://www.linkedin.com/in/gavinmendelgleason/

Timestamps:
[00:00] MLOps Announcements
[00:17] Slack Community
[00:59] Luke and Gavin's Presentation Style
[01:34] MLOps Community Twitter, LinkedIn, and YouTube
[01:45] Introduction to Luke Feeney and Gavin Mendel-Gleason
[04:35] Luke: You wanted Git for Data?
[05:17] Deep Breath || Is there a Git for Data?
[06:30] What is Git for Data?
[08:55] Four Big Buckets
[28:43] Jupiter Notebook
[30:20] Gavin: Collaboration for Structured Data
[31:28] What about gitdifs with gitlfs?
[31:40] Outline: Motivation, Challenges, Solution
[35:35] Motivation: Why Structured Data?
[36:08] Data is Core
[37:34] Challenges: Data is Still in the Dark Ages
[37:40] Structured or Unstructured, we're doing it wrong
[40:15] Managing Data means Collaborating
[45:09] Discoverability and Schema: Structured data requires a real database - not just GIT.
[46:27] Revision Control
[47:00] Collaboration
[48:38] "Git for data, data is the new oil."
[49:01] Why is merging so difficult?
[49:25] "If you have a schema, you can do much more intelligent things."
[52:36] Machine Learning and Revision Control

MLOps community meetup #51! Last Wednesday, we talked to Pamela Jasper, AI Ethicist, Founder, Jasper Consulting Inc.

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// Abstract:
One of the challenges to the widespread adoption of AI Ethics is not only its integration with MLOps, but the added processes to embed ethical principles will slow and impede Innovation. I will discuss ways in which DS and ML teams can adopt Agile practices for Responsible AI.

// Bio:
Pamela M. Jasper, PMP, is a global financial services technology leader with over 30 years of experience developing front-office capital markets trading and quantitative risk management systems for investment banks and exchanges in NY, Tokyo, London, and Frankfurt. Pamela developed a proprietary Credit Derivative trading system for Deutsche Bank and a quantitative market risk VaR system for Nomura. Pamela is the CEO of Jasper Consulting Inc., a consulting firm through which she provides advisory and audit services for AI Ethics governance. Based on her experience as a software developer, auditor, and model risk program manager, Pamela created an AI Ethics governance framework called FAIR – Framework for AI Risk, which was presented at the NeurIPS 2020 AI conference. Pamela is available as an Advisor, Auditor, and Keynote Speaker on AI Ethics Governance. She is a member of BlackInAI, The Professional Risk Managers Industry Association, Global Association of Risk Managers, and ForHumanity.

//Takeaways
Agile methods of adopting AI Ethical processes.

----------- 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 Pamela on LinkedIn: https://www.linkedin.com/in/pamela-michelle-j-a5a3a914/

Timestamps:
[00:00] Introduction to Pamela Jasper
[00:17] Pamela's Background
[05:45] Agile IA/Agile Machine Learning: If they are the right fit for each other?
[07:50] What is agile? Not necessarily in and of itself a hard-coded framework.
[08:05] Agile itself, based on the May 2001 Manifesto, is simply a set of values and principles and teams that make decisions around these values and principles.
[10:17] Proposal of Pamela: Let's do Agile with the underlying Ethics that are involved in the ways that you're creating this machine learning. Is that correct?
[10:28] "What I'm suggesting is that Ethics become baked into almost the mindset of a machine learning engineer, data scientists, and in the machine learning operational process for MLOps."
[14:37] "Not all models are created equal"
[15:59] How would it be, in an Agile way, put into practice in your mind?
[36:38] What are the things that would help bridge the gap between AI Ethics and Agile?
[41:01] It's not that you're trying to bring on the Agile framework to the different pieces of Ethics. It's that what you're bringing into the Agile framework?
[41:21] "We're weaving Ethics into the bedrock of existing Machine Learning practices."
[45:13] How can you really get a diverse team if you're not hiring someone who's there as a diverse person?
[48:59] What would Epics look like if you're baking Ethics?
[52:52] How do you apply Ethics to an ethically questionable domain like gambling?
[54:42] "I think that we can create an AI app for gambling is legal that becomes legal in that construct."
[56:23] Do you think it's possible/desirable to automate any of the ethical considerations in this way?

Coffee Sessions #29 with Jet Basrawi of Satalia, Culture and Architecture in MLOps.

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//Bio
Jet started his career in technology as a game designer but became interested in programming. He found he loved it. It was an endlessly challenging and deeply enjoyable "Flow" activity. It was also nice to be in demand and earn a living.

In the last several years, Jet has been passionate about DevOps as a key strategic practice. About a year ago, he came into the AI world, and it is a great place to be for someone like him. The challenges of MLOps and all the things surrounding AI delivery are a great space to work in.

At about the time Jet got into AI, the MLops community began, and it was a great experience to come on the journey with Demetrios, who was uncovering topics in parallel to him. It was uncanny that each week, Demetrios would run a meetup that dealt with exactly the topics he had been trying to reason about.
Jet is very interested in culture and architecture, and looking forward to exploring this subject in conversation.

//Takeaways
Insight into the role of culture and architecture in MLOps.

--------------- ✌️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 Jet on LinkedIn: https://www.linkedin.com/in/jet-basrawi-4b9ab43/

Timestamps:
[00:00] Introduction to Jet Basrawi
[01:24] Jet's take on MLOps
[02:00] "MLOps - the real Kung fu in the future" Jet
[02:35] Jet's different opinion on "Tooling is the biggest piece in MLOps".  
[04:23] MLOps is a way of life. It's a lifestyle. It's not just tooling.
[05:47] What you refer to as an orthodox perspective on DevOps, and how does that place out in your perspective on MLOps?  
[06:37] Why do you believe that the separate terminology is coming about, and do you believe that this is ultimately harmful to organizations to have this confusion, or do you think things should be simplified?
[09:05] As soon as you go down and you're not looking at the big picture. You go down one level, and they divert completely. Is that your thought, too?
[12:30] How do you go about educating yourself and then figuring out how to articulate MLOps or constituents in your organization?
[16:16] How to do things differently? What are some of your preferred tactics? How to encourage culture change?  
[19:02] "Management is NOT Leadership"
[20:13] Why are people stuck in their agile approach?
[23:57] Someone's trying to pick something up for the 1st time and then put it into production, how dangerous that can be?
[25:53] Accepting failure
[29:11] What are some of your principles that helped you communicate with the developers?
[35:33] "It has to dumb down."
[37:43] Annotation [39:37] "Patterntastic"
[41:24] "MLOps is a people problem."
[43:50] Are Sprints adequate for machine learning?
[47:03] "Software development is a social activity"
[48:03] "We are all juniors in this field."

//Show Notes
https://www.youtube.com/watch?v=J1WpAJRt3rg Charlie You
https://youtu.be/J36xHc05z-M Manoj https://www.youtube.com/watch?v=vH7UFZZdja8&t=5s Lak design patterns https://www.youtube.com/watch?v=9g4deV1uNZo&t=1s flavios talk
https://continuousdelivery.com/implementing/culture/ westrum culture  
https://www.youtube.com/watch?v=Y4H8dW7Ium8&feature=youtu.be&t=109 Jez Humble

MLOps community meetup #50! Last Wednesday, we talked to Michael Del Balso, Willem Pienaar, and David Aronchick.

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// Abstract:
The MLOps tooling landscape is confusing. There’s a complicated patchwork of products and open-source software that each cover some subset of the infrastructure requirements to get ML to production. In this session, we’ll focus on the two most important platforms: model management platforms and feature stores. Model management platforms such as Kubeflow help you get models to production quickly and reliably. Feature stores help you easily build, use, and deploy features. Together, they cover requirements to get models and data to production - the two most important components of any ML project.  

In this panel discussion, we’ll be joined by David Aronchick (Co-Founder of Kubeflow), Mike Del Balso (Co-Founder of Tecton), and Willem Pienaar (Creator of Feast). These experts will share their perspective on the challenges of Operational ML and how to build the ideal infrastructure stack for MLOps.

// Bio:
Michael Del Balso
CEO & Co-founder, Tecton  
Mike is the co-founder of Tecton, where he is focused on building next-generation data infrastructure for Operational ML. Before Tecton, Mike was the PM lead for the Uber Michelangelo ML platform. He was also a product manager at Google, where he managed the core ML systems that power Google’s Search Ads business. Previous to that, he worked on Google Maps. He holds a BSc in Electrical and Computer Engineering summa cum laude from the University of Toronto.

Willem Pienaar
Co-creator, Feast  
Willem is currently a tech lead at Tecton, where he leads the development of Feast, an open-source feature store for machine learning. Previously, he led the ML platform team at Gojek, the Southeast Asian decacorn, which supports a wide variety of models and handles over 100 million orders every month. His main focus areas are building data and ML platforms, allowing organizations to scale machine learning and drive decision-making. In a previous life, Willem founded and sold a networking startup.

David Aronchick
Program Manager, Azure Innovations  
David leads work in the Azure Innovation Office on Machine Learning. This means he spends most of my time helping humans to convince machines to be smarter. He is only moderately successful at this.  
Previously, he led product management for Kubernetes on behalf of Google, launched Google Kubernetes Engine, and co-founded the Kubeflow project. He has also worked at Microsoft, Amazon, and Chef and co-founded three startups.  

----------- 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 Michael on LinkedIn: https://www.linkedin.com/in/michaeldelbalso/
Connect with Willem on LinkedIn: https://www.linkedin.com/in/michaeldelbalso/
Connect with David on LinkedIn: https://www.linkedin.com/in/aronchick/

[00:00] Introduction to Michael, Willem, and David
[02:01] Favorite quarantine purchase question
[05:40] Discussion on Kubeflow (David)
[09:50] Vision of reusable components
[12:40] Non-component aspects of the platform
[17:05] Feature stores
[19:18] Standardization and community agreement
[19:59] “That’s not a standard” – David
[23:50] Mistakes when setting small standards
[27:43] One tool to rule all?
[28:31] MLOps evolving quickly – Mike
[31:16] Willem on one-tool challenge
[35:34] Using production-ready tools early
[45:37] Lessons from failed products

MLOps community meetup #49! Last Wednesday, we talked to Lak Lakshmanan, Data Analytics and AI Solutions, Google Cloud.

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// Abstract:
Design patterns are formalized best practices to solve common problems when designing a software system. As machine learning moves from being a research discipline to a software one, it is useful to catalog tried-and-proven methods to help engineers tackle frequently occurring problems that crop up during the ML process. In this talk, I will cover five patterns (Workflow Pipelines, Transform, Multimodal Input, Feature Store, Cascade) that are useful in the context of adding flexibility, resilience, and reproducibility to ML in production. For data scientists and ML engineers, these patterns provide a way to apply hard-won knowledge from hundreds of ML experts to your own projects.
Anyone designing infrastructure for machine learning will have to be able to provide easy ways for the data engineers, data scientists, and ML engineers to implement these and other design patterns.
// Bio:
Lak is the Director for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud's data analytics and machine learning products. He founded Google's Advanced Solutions Lab ML Immersion program and is the author of three O'Reilly books and several Coursera courses. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA.
----------- 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 Lak on LinkedIn: https://www.linkedin.com/in/valliappalakshmanan/
Timestamps:
[00:00] TWIML Con Debate announcement to be hosted by Demetrios on Friday
[00:19] Should data scientists know about Kubernetes? Is it just one machine learning tool to rule them all? Or is it going to be the "best-in-class" tool?
[00:35] Strong opinion of Lak about "Should data scientists know about Kubernetes?"
[05:50] Lak's background in tech
[08:07] Which ones did you write in the book? Is the airport scenario yours?
[09:25] Did you write ML Maturity Level from Google?
[12:34] How do you know when to bring on perplexity for the sake of making things easier?
[16:06] What are some of the best practices that you've seen being used in tooling?  
[20:09] How did you come up with writing the book?
[20:59] How did we decide that these are the patterns that we need to put in the book?
[24:14] Why did I get the "audacity" to think that this is something that is worth doing?
[31:29] What would be in your mind some of the hierarchy of design patterns?
[38:05] Are there patterns out there that are yet to be discovered? How do you balance the exploitable vs the explorable ML patterns?
[42:08] ModelOps vs MLOps
[43:08] Do you feel that a DevOps engineer is better suited to make the transition into becoming a Machine Learning engineer?
[46:07] Fundamental Machine Design Patterns vs Software Development Design Patterns
[49:23] When you're working with the companies at Google, did you give them a toolchain and a better infrastructure, or was there more to it? Did they have to rethink their corporate culture because DevOps is often mistaken as just a pure toolchain?

Coffee Sessions #28 with Charlie You of Workday, Lessons learned from hosting the Machine Learning Engineered podcast.

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//Bio
Charlie You is a Machine Learning Engineer at Workday and the host of ML Engineered, a long-form interview podcast aiming to help listeners bring AI out of the lab and into products that people love. He holds a B.S. in Computer Science from Rensselaer Polytechnic Institute and previously worked for AWS AI.
Charlie is currently working as a Machine Learning Engineer at Workday. He hosts the ML Engineered podcast, learning from the best practitioners in the world.  
Check Charlie's podcast and website here:
mlengineered.com
https://cyou.ai/

--------------- ✌️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 Charlie on LinkedIn: https://linkedin.com/in/charlieyou/

Timestamps:
[00:00] Introduction to Charlie You
[01:50] Charlie's background in Machine Learning and inspiration to create a podcast
[06:20] What's your experience been so far as a machine learning engineer and trying to put models into production, and trying to get things out that have business value?
[07:08] "I started the podcast because as I started working, I had the tingling that machine learning engineering is harder than most people thought, and like way harder than I personally thought."
[08:20] What's an example of that where you target someone in your podcast, you keep that learning, and you want an extra meeting the next day and say, "Hey, actually I'm starting one of the world's experts on this topic and this is what they said"?   
[10:06] In a world of tons of traditional software engineering assets and the process you put in place, how have they adopted what they're doing to the machine learning realm?   
[19:00] About your podcast, what are some 2-3 most consistent trends that you've been seeing?
[21:08] Instead of splintering so much as a machine learning monitoring infrastructure specialist, are you going to departmentalize it in the future?
[27:22] Is there such a thing as an MLOps engineer right now?
[28:50] "We haven't seen a very vocal, very opinionated project manager in machine learning yet." - Todd Underwood
[30:18] "Similarly with tooling, we haven't seen the emergence of the tools that encode those best practices." Charlie
[31:42] "The day that you don't have to be a subject matter expert in machine learning to feel confident and deploy machine learning products, is the day that you will see the real product leadership in machine learning." Vishnu
[34:12] Security and Ethics
[34:41] "Data Privacy and Security is always at the top of any consideration for infrastructure." Charlie
[35:44] That's driven by legal requirements? How do you solve this problem?
[37:27] How do we make sure that if that blows up, you're not left with nothing?  
[42:28] In your conversations, have you seen people who go with a cloud provider?
[43:25] Enterprises have much different incentives than startups do.
[45:48] What are some use cases where companies need to service their entire needs?
[45:48] What are some use cases where companies need to service their entire needs?
[49:18] What are some takeaways that you had in terms of how you think about your career, what experiences you want to build as this MLOps-based engineering is moving so fast?  
[56:08] "Your edge is never in the algorithm"

Coffee Sessions #27 with Noah Gift of Pragmatic AI Labs, Practical MLOps 

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// A “Gift” from Above

This week, Demetrios and Vishnu got to spend time with inimitable Noah Gift. Noah is a data science educator who teaches at Duke, Northwestern, and many other universities, as well as a technical leader through his company, Pragmatic AI Labs, and past companies.

His bio alone would take up this section of the newsletter, so we invite you to check it out here, as well as the rest of his educational content. Read on for some of our takeaways.

// HOW is as important as WHAT

In our conversation, Noah eloquently pointed out the numerous challenges of bringing ML into production, especially for making sure it's used positively. It’s not enough to train great models; it’s important to make sure they impact the world positively as their productionized. How models are used is as important as what the model is.

Noah specifically commented on externalities and how it’s incumbent on all MLOps practitioners to understand the externalities created by their models.

// Just get certified

As an educator, Noah has seen front and center how deficits in ML/DS education at the university level have led to the “cowboy” data scientist who doesn’t fit into an effective technical organizational structure. In his courses, Noah emphasizes getting started with off-the-shelf models and understanding how existing software systems are engineered before committing to building ML systems.

Furthermore, Noah suggested getting certifications as a useful way of upskilling for anyone looking to increase their knowledge base in MLOps, especially by cloud providers.

// Tech Stack Risk

Finally, as many of you do, we debated the relative merits of the major cloud providers (AWS, Azure, and GCP) with Noah. With his vast experience, Noah made a great point about how adopting extremely new tools can sometimes go wrong. In the past, Noah adopted Erlang as a language used in the development of a product. However, as the language never quite took off (in his experience), it became a struggle to hire the right talent to get things done.

Readers, as you go about designing and building the MLOps stack, does any part of the process sound like Noah’s experience with Erlang? Tools or frameworks where downstream adoption may end up fractured? We’d love to hear more!

Definitely check out Noah’s podcast with us for more awesome nuggets on MLOps. Thanks to Noah for taking the time! 

https://noahgift.com/

Noah Gift

Machine Learning, Data Science, Cloud & AI Lecturer

His most recent books are:

Pragmatic A.I.: An introduction to Cloud-Based Machine Learning (Pearson, 2018)
Python for DevOps (O’Reilly, 2020). 
Cloud Computing for Data Analysis, 2020
Practical MLOps (O'Reilly, 2021 est.)

--------------- ✌️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 Noah on LinkedIn: https://www.linkedin.com/in/noahgift/

Timestamps

[00:00] Introduction to Noah Gift  
[03:28] Pragmatic when it comes to MLOps
[32:45] The worst excuse that you can give somebody is that "I just do this stuff that's hard, intellectually, but departed makes it work. That's your job."  
[33:34] "In the Master's Degree, we don't do anything that gets you a job."
[46:33] MLOps vs Cloud Provider
[51:35] GO vs Erlang

MLOps community meetup #48! Last Wednesday, we talked to Manoj  Agarwal, Software Architect at Salesforce.

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// Abstract:
Serving machine learning models is a scalability challenge at many companies. Most applications require a small number of machine learning models (often < 100) to serve predictions. On the other hand, cloud platforms that support model serving, though they support hundreds of thousands of models, provision separate hardware for different customers. Salesforce has a unique challenge that only very few companies deal with: Salesforce needs to run hundreds of thousands of models sharing the underlying infrastructure for multiple tenants for cost-effectiveness.

// Takeaways:
This talk explains that Salesforce hosts hundreds of thousands of models on a multi-tenant infrastructure to support low-latency predictions.

// Bio:
Manoj Agarwal is a Software Architect in the Einstein Platform team at Salesforce. Salesforce Einstein was released back in 2016, integrated with all the major Salesforce clouds. Fast forward to today, and Einstein is delivering 80+ billion predictions across Sales, Service, Marketing & Commerce Clouds per day.

//Relevant Links
https://engineering.salesforce.com/flow-scheduling-for-the-einstein-ml-platform-b11ec4f74f97
https://engineering.salesforce.com/ml-lake-building-salesforces-data-platform-for-machine-learning-228c30e21f16

----------- 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 Manoj on LinkedIn: https://www.linkedin.com/in/agarwalmk/

Timestamps:
[00:00] Happy birthday Manoj!
[00:41] Salesforce blog post about Einstein and ML Infrastructure
[02:55] Intro to Serving Large Number of Models with Low Latency
[03:34] Manoj' background
[04:22] Machine Learning Engineering: 99% engineering + 1% machine learning - Alexey Gregorev on Twitter
[04:37] Salesforce Einstein
[06:42] Machine Learning: Big Picture
[07:05] Feature Engineering [07:30] Model Training
[08:53] Model Serving Requirements
[13:01] Do you standardize on how models are packaged in order to be served, and if so, what standards does Salesforce require and enforce from model packaging?
[14:29] Support Multiple Frameworks  
[16:16] Is it easy to just throw a software library in there?
[27:06] Along with that metadata, can you break down how that goes?  
[28:27] Low Latency
[32:30] Model Sharding with Replication
[33:58] What would you do to speed up the transformation code run before scoring?
[35:55] Model Serving Scaling
[37:06] Noisy Neighbor: Shuffle Sharding
[39:29] If all the Salesforce Models can be categorized into different model types, based on what they provide, what would be some of the big categories be and what's the biggest?
[46:27] Retraining of the Model: Does that deal with your team, or is that distributed out, and your team deals mainly with this kind of engineering, and then another team deals with more machine learning concepts of it?
[50:13] How do you ensure that different models created by different teams for data scientists expose the same data in order to be analyzed?
[52:08] Are you using Kubernetes, or is it another registration engine?

[53:03] How is it ensured that different models expose the same information?

MLOps community meetup #48! Last Wednesday, we talked to Manoj  Agarwal, Software Architect at Salesforce.

//Relevant Links

**Private data, Data Science friendly**

Data Scientists are always eager to get their hands on more data, in particular, if that data has any value that can be extracted. Nevertheless, in real-world situations, data does not exist in the abundance that we thought existed, in other situations, the data might exist, but not possible to share it with different entities due to privacy concerns, which makes the work of data scientists not only hard, but sometimes even impossible.

// Abstract:

In the last episode of this series, we've decided to bring not one, but two guests to tells us how Synthetic data can unlock the use of data for Data Science teams whenever privacy concerns are a reality.  Jean-François Rajotte, Researcher and Resident data Scientist at the University of Columbia and Sumit Mukherjee, Senior Applied Scientist at Microsoft's AI for Good, bring us into more detail their expertise not only, in Synthetic data generation, but in it's mind blowing combination with Federated Learning to take the healthcare sector into the next level of AI adoption.

//Other links to check on Jean-François Rajotte:

https://venturebeat.com/2021/01/20/microsofts-felicia-taps-ai-to-enable-health-providers-to-share-data-anonymously/

https://dsi.ubc.ca/

https://leap-project.github.io/

//Other links to check on Sumit Mukherjee:

www.sumitmukherjee.com (Sumit research)

https://arxiv.org/abs/2101.07235

https://arxiv.org/pdf/2009.05683.pdf

https://github.com/microsoft/privGAN (PrivGan)

//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

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

Connect with Jean-François on LinkedIn: https://www.linkedin.com/in/jfraj/

Connect with Sumit on LinkedIn: https://www.linkedin.com/in/sumitmukherjee2/