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MLOps community meetup #54! Last Wednesday, we talked to Laszlo Sragner, Founder, Hypergolic.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter// Abstract:How my experience in quant finance and software engineering influenced how we ran ML at a London Fintech Startup. How to solve business problems with incremental ML? What's the difference between academic and industrial ML?// Bio:Laszlo worked as a quant researcher at multiple investment managers and as a DS at the world's largest mobile gaming company. As Head of Data Science at Arkera, he drove the company's data strategy, delivering solutions to Tier 1 investment banks and hedge funds. He currently runs Hypergolic (hypergolic.co.uk), an ML Consulting company helping startups and enterprises bring the maximum out of their data and ML operations.// TakeawaysContinuous evaluation and monitoring are indistinguishable in a well-set-up product team. Separation of concerns (SE, ML, DevOps, MLOps) is very important for smooth operation, and low-friction team coordination/communication is key.To be able to iterate business features into models, you need a modeling framework that can express these, which is usually a DL package.DS-es are well motivated to go more technical because they see the rewards of it. All well-run (from the DS perspective) startups in my experience do the same.// Related LinksFree eBook about MLPM: https://machinelearningproductmanual.com/Lightweight MLOps Python package: https://hypergol.ml/Blog: laszlo.substack.com----------- Connect With Us ✌️-------------   Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/register  Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Laszlo on LinkedIn: https://www.linkedin.com/in/laszlosragner/Timestamps:[00:00] Introduction to Laszlo Sranger[02:15] Laszlo's Background[09:18] Being a Quant, then influenced what you were doing with the Investment Banks?[12:24] Do you think this can be applied in different use cases or specific to what you are doing?[14:41] Do you have any thoughts of a potentially highly opinionated person?[16:54] Product management in Machine Learning[24:59] You have to be at a large company, or you have to have a large team? [26:38] What are your thoughts on MLOps products helping with product management for ML? Is it an overreach or scope creep?[32:00] In the messy world of startups, due to the high cost of an MVP for NLP, is RegEx, which means to incorporate user feedback, it's incorporated by tweaking RegEx?[33:04] Do the ensemble recent models more than older models? If so, what is the decay rate of weights for older models?[35:40] Since the iterative management model is generic enough for most ML projects, which component of it can be easily generalized, and what tools are built for version control?[36:38] Topic Extraction: What type of model do you train for that task?[52:55] Thoughts on Notebooks[53:34] "I don't hate notebooks. Let's be clear about that. I put it this way: notebooks are whiteboards. You don't want your whiteboards to be your output because they're a sketch of your solution. You want the purest solution."

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletterThis is a deep dive into the most recent MLOps Engineering Labs from the point of view of Team 3.  // Diagram Link:  https://github.com/dmangonakis/mlops-lab-example-yelp  --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/register  Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Laszlo on LinkedIn https://www.linkedin.com/in/laszlosragner/Connect with Artem on LinkedIn: https://www.linkedin.com/in/artem-yushkovsky/Connect with Paulo on LinkedIn: https://www.linkedin.com/in/paulo-maia-410874119/Connect with Dimi on LinkedIn:Timestamps:[00:00] Engineering Labs Recap Team Three[01:12] Laszlo Sranger Background[02:05] Artem Background[04:45] Dimi Background[06:31] Paulo Background[08:51] Initial Product Ideas Overview[09:12] Decent Product Using Yelp Dataset[10:32] Backend Facade Streamlit Overview[13:52] Questioning Bad Practices[14:11] Demo Works But Limited[15:12] Walking Through Streamlit Code[15:16] Decoupled Frontend Backend Architecture[16:54] Managerial Considerations[19:00] Working Outside Comfort Zones[20:36] Key Takeaways From Lab[20:42] MLflow Architecture Insights[22:21] Additional Considerations[22:31] MLflow End-to-End Monitoring[24:50] Explainability Tools and Complexity[26:29] Real-World Issues[26:36] Avoid Unnecessary Bells and Whistles[28:33] Difficulties in Process[30:25] Engineering Mistakes Reflection[31:17] Artifact Logging Challenges[32:00] Identifying Non-Ideal Aspects[33:21] PyTorch Limitations[34:52] Managing Dependencies[35:08] Avoid Using Notebooks[36:27] Consistent Scripts And Environments[37:08] Replicable Docker Processes[37:42] Future MLflow Use[38:23] MLflow Improvement Over Time[40:34] Kubernetes Knowledge Requirements[41:25] Kubernetes Provides Great Output[46:03] Current Status Limitations[46:53] Limited Production Control[47:40] Kubernetes Knowledge For Data Scientists[48:14] Machine Learning Cultural Movement[50:55] Jack Of All Trades[51:32] Productized ML Requires Engineering[56:27] Final Lab Reflections[57:11] Cloud Credits For Next Lab

MLOps community meetup #53! Last Wednesday, we talked to Krishna Gade, CEO & Co-Founder, Fiddler AI.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter// Abstract:Training and deploying ML models have become relatively fast and cheap, but with the rise of ML use cases, more companies and practitioners face the challenge of building “Responsible AI.” One of the barriers they encounter is increasing transparency across the entire AI lifecycle to not only better understand predictions but also to find problem drivers. In this session with Krishna Gade, we will discuss how to build AI responsibly, share examples from real-world scenarios and AI leaders across industries, and show how Explainable AI is becoming critical to building Responsible AI.// Bio:Krishna is the co-founder and CEO of Fiddler, an Explainable AI Monitoring company that helps address problems regarding bias, fairness, and transparency in AI. Prior to founding Fiddler, Gade led the team that built Facebook’s explainability feature ‘Why am I seeing this?’. He’s an entrepreneur with a technical background, with experience creating scalable platforms and expertise in converting data into intelligence. Having held senior engineering leadership roles at Facebook, Pinterest, Twitter, and Microsoft, he’s seen the effects that bias has on AI and machine learning decision-making processes, and with Fiddler, his goal is to enable enterprises across the globe to solve this problem.----------- Connect With Us ✌️-------------   Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Krishna on LinkedIn: https://www.linkedin.com/in/krishnagade/Timestamps:[00:00] Thank you, Fiddler AI![01:04] Introduction to Krishna Gade[03:19] Krisha's Background[08:33] Everything was fine when you were doing it behind the scenes. But then, when you put it out into the wild, we just lost our "baby." It's no longer under our control.[08:53] "You want to have the assurance of how the system works. Even if it's working fine or if it's not working fine."  [09:37] What else is Explainability? Can you break that down for us?[13:58] "Explainability becomes the cornerstone technology to have in place for you to build Responsible AI in production."[14:48] For those used cases that aren't as high stakes, do you feel it's important? Is it up the food chain?[18:47] Can we dig into that used case real fast?[22:01] If it is a human doing it, there's a lot more room for error? Bias or theories can be introduced and then they don't have a basis in reality?[23:51] Do you need these subject matter experts or someone who is very advanced to be able to set up what the Explainability tool should be looking for at first? Is it that plug and play, and it will know it latches on to the model?[29:36] Does Explainable AI also entail Explainable Data? I see the point where Explainability can help with getting the insights about data after the model has been trained, but should it be handled perhaps more proactively, where you unbias the data before training the model on it?[32:16] As a data scientist, there are situations when the prediction output is expected to support a business decision taken by senior executives. In that case, when the Explainable model gives out a prediction that doesn't align with the stakeholder's expectations, how should one navigate through this tricky situation?[43:49] How is denen gram clustering for data explainability?

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠This is a deep dive into the most recent MLOps Engineering Labs from the point of view of Team 1.// Diagram Link: https://github.com/mlops-labs-team1/engineering.labs#workflow--------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Alexey on LinkedIn: https://www.linkedin.com/in/alexeynaiden/Connect with John on LinkedIn: https://www.linkedin.com/in/johnsavageireland/Connect with Michel on LinkedIn: https://www.linkedin.com/in/michel-vasconcelos-8273008/Connect with Varuna on LinkedIn: https://www.linkedin.com/in/vpjayasiri/Timestamps [00:00] Introduction to Engineering Labs Participants[00:34] What Are Engineering Labs[01:05] Credits to Ivan Nardini[04:24] John Savage Profile[05:13] Prior MLflow Knowledge[05:50] Alexey Naiden Profile[07:26] Varuna Jayasiri Profile[08:28] Michel Vasconcelos Profile[10:07] Process Using PyTorch MLflow[13:39] Implementation Structure and Coding[17:03] Encountering Problems Along the Way[20:26] Overview and First Problem[23:08] Catching Up or Comfortable[24:12] Tool John Called Out[24:41] Homegrown Tool Confirmation[24:51] Engineering Labs Implementation[26:03] Pipeline and Serving Overview[37:26] Pet Project Limitations[38:13] Lego-Like Modular Building Block[40:54] PyTorch or MLflow Troubles[42:44] Torchserve Prompt Challenges[44:27] Considering Better Approaches[49:05] Feedback on Labs Experience[50:20] Michel Wants Future Participation[51:52] Varuna Values Tangible Learning[53:00] John Anchored in MLOps[55:52] Alexey Reaching Checkpoint[56:01] Michel’s Terraform Reproducibility Piece

MLOps community meetup #52! Last Wednesday, we talked to Luke Feeney and Gavin Mendel-Gleason, TerminusDB.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠// 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/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect 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.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠// 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.//TakeawaysAgile methods of adopting AI Ethical processes.----------- Connect With Us ✌️-------------   Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect 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.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠//BioJet 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.//TakeawaysInsight into the role of culture and architecture in MLOps.--------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect 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.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠// 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 BalsoCEO & 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 PienaarCo-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 AronchickProgram 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/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect 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.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠// 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/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect 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.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠//BioCharlie 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.comhttps://cyou.ai/--------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect 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"