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MLOps Coffee Sessions #100 with Matthijs Brouns, MLOps Critiques co-hosted by David Aponte. // Abstract MLOps is too tool-driven, don't let FOMO drive you to pick the latest feature/model/evaluation/ store but pay closer attention to what you actually need to release more safely and reliably. // Bio Matthijs is a Machine Learning Engineer, active in Amsterdam, The Netherlands. His current work involves training MLEs at Xccelerated.io. This means Matthijs divides his time between building new training materials and exercises, giving live trainings, and acting as a sparring partner for the Xccelerators at their partner firms, as well as doing some consulting work on the side. Matthijs spent a fair amount of time contributing to their open scientific computing ecosystem through various means. He maintains open source packages (scikit-lego, seers) as well as co-chairs the PyData Amsterdam conference and meetup. // MLOps Jobs board  https://mlops.pallet.xyz/jobs // Related Links https://www.youtube.com/watch?v=appLxcMLT9Y https://www.youtube.com/watch?v=Z1Al4I4Os_A --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/ Connect with Matthijs on LinkedIn: https://www.linkedin.com/in/mbrouns/ Timestamps: [00:00] Introduction to Matthijs Brouns [00:28] Takeaways [03:09] Best of Slack Newsletter [03:38] AI MLFlow [04:43] Nanny ML [05:08] Best confinement buy over the last 2 years [06:35] Matthijs' day-to-day [08:24] What's hot right now?   [09:36] ML space, orchestration, deployment [10:21] Scaling [13:20] Low-risk releases [15:27] Scale Limitations or Fundamental in API [16:33] MLOps maturity to a certain point [18:57] Interdisciplinary leverage need [21:11] PyScript   [22:41] Next pipeline tools   [24:02] General pattern to build your own tools [30:25] Technology recommendation to a chaotic space [33:46] Structured data vs tabular data   [35:52] Big barriers in production [37:57] Standardization [39:20] Automation tension between the engineering side and data science side [41:50] Low-hanging fruit [42:30] Human check [43:43] Rapid fire questions [48:30] PyData Meetups

MLOps Coffee Sessions #99 with Ronen Dar and Gijsbert Janssen van Doorn, Getting the Most Out of your AI Infrastructure co-hosted by Vishnu Rachakonda.   // Abstract Run:AI is building a cloud-based platform for building with AI. In this talk, we hear all about why this need exists, how this works, and what value it creates. // Bio Ronen Dar Run:AI Co-founder and CTO Ronen was previously a research scientist at Bell Labs and has worked at Apple and Intel in multiple R&D roles. As CTO, Ronen manages research and product roadmap for Run:AI, a startup he co-founded in 2018. Ronen is the co-author of many patents in the fields of storage, coding, and compression. Ronen received his B.S., M.S., and Ph.D. degrees from Tel Aviv University. Gijsbert Janssen van Doorn Gijsbert is Director of Technical Product Marketing at Run:AI. He is a passionate advocate for technology that will shape the future of how organizations run AI. Gijsbert comes from a technical engineering background, with six years in multiple roles at Zerto, a Cloud Data Management and Protection vendor. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // Related Links The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers by Ben Horowitz ebook:  https://www.scribd.com/book/211302755/The-Hard-Thing-About-Hard-Things-Building-a-Business-When-There-Are-No-Easy-Answers?utm_medium=cpc&utm_source=google_search&utm_campaign=3Q_Google_DSA_NB_RoW&utm_term=&utm_device=c&gclid=Cj0KCQjw1ZeUBhDyARIsAOzAqQLnUzXlgFT1PjU_M6jGqRZmwLbcK-mbfKQI4XrZJBRwgUs4x5j2hQ4aAmt1EALw_wcB --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Ronen on LinkedIn: https://www.linkedin.com/in/ronen-dar/ Connect with Gijsbert on LinkedIn: https://www.linkedin.com/in/gijsbertjvd/ Timestamps: [00:00] Introduction to Ronen Dar & Gijsbert Janssen van Doorn [01:25] Takeaways [04:24] Thank you Run:AI for sponsoring this episode! [05:13] Run:AI products and components [09:27] Companies coming to Run:AI and problems they solve [13:30] Why is this problem hard? [18:56] Run:AI's Vision [22:12] Run-on-the-mill workload [25:36] Engineering challenges and requirements building Run:AI   [32:47] Process of solving problems on the same page [35:45] Power to give data scientists [37:38] Avoiding horror stories that might cost a lot of money [44:23] Running multiple models on a single GPU [47:17] Never scale down to zero [48:28] So many ML Start-ups in Israel [53:00] Vision for the future at GPUs and how will Kubernetes advance [55:55] Future of AI accelerators [57:03] Lightning round [1:02:26] Wrap up

MLOps Coffee Sessions #98 with Brannon Dorsey, Racing the Playhead: Real-time Model Inference in a Video Streaming Environment co-hosted by Vishnu Rachakonda. // Abstract Runway ML is doing an incredibly cool workaround applying machine learning to video editing. Brannon is a software engineer there and he’s here to tell us all about machine learning in video and how Runway maintains their machine learning infrastructure. // Bio Brannon Dorsey is an early employee at Runway, where he leads the Backend team. His team keeps infrastructure and high-performance models running at scale and helps to enable a quick iteration cycle between the research and product teams. Before joining Runway, Brannon worked on the Security Team at Linode. Brannon is also a practicing artist who uses software to explore ideas of digital literacy, agency, and complex systems. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // Related Links Website: https://brannon.online Blog: https://runwayml.com/blog/distributing-work-adventures-queuing-and-autoscaling/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Brannon on LinkedIn: https://www.linkedin.com/in/brannon-dorsey-79b0498a/ Timestamps: [00:00] Introduction to Brannon Dorsey [00:56] Takeaways [05:42] Runway ML [07:00] Replacement for Imovie? [09:07] Machine Learning use cases of Runway ML [10:40] Journey of starting as a model zoo to video editor [14:42] Rotoscoping   [16:23] Intensity of ML models in Runway ML and engineering challenges [19:55] Deriving requirements [23:10] Runway's model perspective [25:25] Why browser hosting? [27:19] Abstracting away hardware [32:04] Kubernetes is your friend [35:29] Statelessness is your friend [38:17] Merge to master quickly [42:57] Brannon's winding history becoming an engineer [46:49] How much do you use Runway? [49:37] Last book read [50:36] Last bug smashed [52:21] MLOps marketing that made eyes rolling [54:11] Bullish on technology that might surprise people [54:39] Spot by netapp [56:42] Implementing Spot by netapp [56:55] How do you want to be remembered? [57:22] Wrap up

MLOps Coffee Sessions #97 with Jacob Tsafatinos, Real-Time Exactly-Once Event Processing with Apache Flink, Kafka, and Pinot co-hosted by Mihail Eric. // Abstract A few years ago Uber set out to create an ads platform for the Uber Eats app that relied heavily on three pillars; Speed, Reliability, and Accuracy. Some of the technical challenges they were faced with included exactly-once semantics in real-time. To accomplish this goal, they created the architecture diagram above with lots of love from Flink, Kafka, Hive, and Pinot. You can dig into the whole paper (https://go.mlops.community/k8gzZd) to see all the reasoning for their design decisions. // Bio Jacob Tsafatinos is a Staff Software Engineer at Elemy. He led the efforts of the Ad Events Processing system at Uber and has previously worked on a range of problems including data ingestion for search and machine learning recommendation pipelines. In his spare time, he can be found playing lead guitar in his band Good Kid. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // Related Links Uber blog https://eng.uber.com/author/jacob-tsafatinos/ https://eng.uber.com/real-time-exactly-once-ad-event-processing/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/ Connect with Jacob on LinkedIn: https://www.linkedin.com/in/jacobtsaf/ Timestamps: [00:00] Introduction to Jacob Tsafatinos [00:40] Takeaways [04:25] Jacob's band [05:29] Lyrics about software engineers or artistic stuff [06:20] Connection of hobby and real-time system [08:43] How to game Spotify Algorithm? [10:00] Data stack for analytics [13:28] Uber blog [16:28] Video mess up [17:04] Considerations and importance of the Uber System [21:22] Challenges encountered through the Uber System journey [26:06] Crucial to building the system [28:13] Not exactly real-time [30:22] Design decisions main questions [34:23] Testament to OSS   [36:58] Real-time processing systems for analytical use cases vs Real-time processing systems for predictive use cases [38:46] Real-time systems necessity [41:04] Potential that opens up new doors [41:40] Runaway or learn it? [46:09] Real-time use case target [49:31] Resource constrained [50:48] ML Oops stories [52:45] Wrap up

MLOps Coffee Sessions #96 with Sebastián Ramírez, FastAPI for Machine Learning co-hosted by Adam Sroka. // Abstract Fast API almost never happened. Sebastián Ramírez, the creator of FastAPI, tried as hard as possible not to build something new. After many failed attempts at finding what he was looking for he decided to scratch his own itch and build a new product.    The conversation goes over what Fast API is, how Sebastián built it, what the next big problems to tackle in ML are, and how to focus on adding value where you can. // Bio 👋 Sebastián Ramírez is the creator of FastAPI, Typer, and other open-source tools. Currently, Sebastián is a Staff Software Engineer at Forethought while also helping other companies as an external consultant.🤓 // MLOps Jobs board   https://mlops.pallet.xyz/jobs // Related Links Website: https://tiangolo.com/ https://fastapi.tiangolo.com/ https://typer.tiangolo.com/ https://www.forethought.ai/ https://sqlmodel.tiangolo.com/ https://github.com/tiangolo --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Adam on LinkedIn: https://www.linkedin.com/in/aesroka/ Connect with Sebastián on LinkedIn: https://www.linkedin.com/in/tiangolo/ Timestamps: [00:00] Introduction to Sebastián Ramírez [00:44] Takeaways [02:45] Apply () Conference is coming up! [03:38] FastAPI background [05:02] Ramp up reason [06:17] Tipping point [08:11] Surprising ways using FastAPI [10:08] Twist it and break it lessons learned [12:00] Length of comprehension process [15:59] Missing pieces [21:25] Advice to technically capable what to start with [25:19] Making FastAPI better [27:52] What to simplify and why are they cumbersome right now? [30:14] Building FastAPI vs solving the problem [32:42] Next itch to scratch [34:26] Landscape's pathway [38:03] Things that would not change [40:13] Sebastián's change in life since FastAPI [43:09] Sebastián's famous tweet [44:13] Experienced vs inexperienced [46:07] Approach to becoming a tools expert [50:22] Wrap up

MLOps Coffee Sessions #95 with Ciro Greco, MLOps as Tool to Shape Team and Culture. // Abstract Good MLOps practices are a way to operationalize a more “vertical” practice and blur the boundaries between different stages of “production-ready”. Sometimes you have this idea that production-ready means global availability but with ML products that need to be constantly tested against real-world data, we believe production-ready should be a continuum and that the key person that drives that needs to be the data scientist or the ML engineer. // Bio Ciro Greco, VP of AI at Coveo. Ph.D. in Linguistics and Cognitive Neuroscience at Milano-Bicocca. Ciro worked as visiting scholar at MIT and as a post-doctoral fellow at Ghent University. In 2017, Ciro founded Tooso.ai, a San Francisco-based startup specializing in Information Retrieval and Natural Language Processing. Tooso was acquired by Coveo in 2019. Since then Ciro has been helping Coveo with DataOps and MLOps throughout the turbulent road to IPO. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // Related Links Company Website psicologia.unimib.it/03_persone/scheda_personale.php?personId=518    gist.ugent.be/members --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Ciro on LinkedIn: https://www.linkedin.com/in/cirogreco/en Timestamps: [00:00] Introduction to Ciro Greco [02:32] Ciro's bridge to Coveo [07:15] Coveo in a nutshell [11:30] Confronting disorganization and challenges [16:08] Fundamentals of use cases [18:09] Immutable data in the data warehouse [21:36] Data management in Coveo [24:48] Pain for advancement [29:56] Rational process and Stack [32:24] Habits of high-performing ML Engineers [35:46] Sharpening the sword [37:50] Attracting talents vs firing people [42:18] Wrap up

MLOps Coffee Sessions #94 with Mark Freeman, Traversing the Data Maturity Spectrum: A Startup Perspective. // Abstract A lot of companies talk about having ML and being data-driven, but few are there currently and doing it well. If anything, many companies are on the cusp of implementing ML rather than being ML mature.   As a startup, what decisions are we making today to drive data maturity and set us up for success when we further implement ML in the near future. What business cases are we making for leadership buy-in to invest in data infrastructure as compared to product development while we identify product-market-fit. // Bio Mark is a community health advocate turned data scientist interested in the intersection of social impact, business, and technology. His life’s mission is to improve the well-being of as many people as possible through data—especially among those marginalized. Mark received his M.S. from the Stanford School of Medicine where he was trained in clinical research, experimental design, and statistics with an emphasis on observational studies. In addition, Mark is also certified in Entrepreneurship and Innovation from the Stanford Graduate School of Business. He is currently a senior data scientist at Humu where he builds data tools that drive behavior change to make work better. His core responsibilities center around 1) building data products that reach Humu's end users, 2) providing product analytics for the product team, and 3) building data infrastructure and driving data maturity. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // Related Links Website: humu.com The Informed Company: How to Build Modern Agile Data Stacks that Drive Winning Insights book: https://www.amazon.com/Informed-Company-Cloud-Based-Explore-Understand/dp/1119748003 Fundamentals of Data Engineering book by Joe Reis and Matt Housley: https://www.oreilly.com/library/view/fundamentals-of-data/9781098108298/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Mark on LinkedIn: https://www.linkedin.com/in/mafreeman2/ Timestamps: [00:00] Introduction to Mark Freeman [01:43] Grab your own Apron merch @ https://mlops.community/! [03:41] LinkedIn stardom! [04:40] Followers or connections? [05:31] Leveraging essential information platform [08:56] Investment in time spent on creating and working on a social platform [12:16] Put yourself out there for people to find you [16:33] Data maturity is a spectrum that takes time to traverse [23:43] Maturity of path [28:43] Fundamentals for data products [33:05] Foundational data capabilities [37:32] Value of metrics [41:48] writing reused code timeframe vs working with stakeholders timeframe [44:11] Wrap up   [45:14] Look for Meetups near you!

MLOps Coffee Sessions #93 with Krishnaram Kenthapadi, Model Monitoring in Practice: Top Trends co-hosted by Mihail Eric // Abstract We first motivate the need for ML model monitoring, as part of a broader AI model governance and responsible AI framework, and provide a roadmap for thinking about model monitoring in practice. We then present findings and insights on model monitoring in practice based on interviews with various ML practitioners spanning domains such as financial services, healthcare, hiring, online retail, computational advertising, and conversational assistants. // Bio Krishnaram Kenthapadi is the Chief Scientist of Fiddler AI, an enterprise startup building a responsible AI and ML monitoring platform. Previously, he was a Principal Scientist at Amazon AWS AI, where he led the fairness, explainability, privacy, and model understanding initiatives in the Amazon AI platform. Prior to joining Amazon, he led similar efforts at the LinkedIn AI team and served as LinkedIn’s representative on Microsoft’s AI and Ethics in Engineering and Research (AETHER) Advisory Board. Previously, he was a Researcher at Microsoft Research Silicon Valley Lab. Krishnaram received his Ph.D. in Computer Science from Stanford University in 2006. He serves regularly on the program committees of KDD, WWW, WSDM, and related conferences, and co-chaired the 2014 ACM Symposium on Computing for Development. His work has been recognized through awards at NAACL, WWW, SODA, CIKM, ICML AutoML workshop, and Microsoft’s AI/ML conference (MLADS). He has published 50+ papers, with 4500+ citations and filed 150+ patents (70 granted). He has presented tutorials on privacy, fairness, explainable AI, and responsible AI at forums such as KDD ’18 ’19, WSDM ’19, WWW ’19 ’20 '21, FAccT ’20 '21, AAAI ’20 '21, and ICML '21. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // Related Links Website: https://cs.stanford.edu/people/kngk/ https://sites.google.com/view/ResponsibleAITutorial https://sites.google.com/view/explainable-ai-tutorial https://sites.google.com/view/fairness-tutorial https://sites.google.com/view/privacy-tutorial --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/ Connect with Krishnaram on LinkedIn: https://www.linkedin.com/in/krishnaramkenthapadi Timestamps: [00:00] Introduction to Krishnaram Kenthapadi [02:22] Takeaways [04:55] Thank you Fiddler AI for sponsoring this episode! [05:15] Struggles in Explainable AI [06:16] Attacking the problem of difficult models and architectures in Explainability [08:30] Explainable AI prominence [09:56] Importance of password manager and actual security [14:27] Role of Education in Explainable AI systems [18:52] Highly regulated domains in other sectors [21:12] First machine learning wins [23:36] Model monitoring [25:35] Interests in ML monitoring and Explainability [27:27] Future of Explainability in the wide range of ML models [29:57] Non-technical stakeholders' voice [33:54] Advice to ML practitioners to address organizational concerns [38:49] Ethically sourced data set  [42:15] Crowd-sourced labor [43:35] Recommendations to organizations about their minimal explainable product [46:29] Tension in practice [50:09] Wrap up

MLOps Coffee Sessions #92 with Pete Soderling, Building the World's First Data Engineering Conference. // Abstract Keep things centered around community building and what he looks for in teams. Folks that are building their community around their tool, what advice do you have for that? What's worth turning into a company? // Bio Pete Soderling is the founder of Data Council and the Data Community Fund. As a former software engineer, repeat founder, and investor in more than 40 data-oriented startups, Pete’s lifetime goal is to help 1,000 engineers start successful companies. Most importantly, Pete is a community builder — from his earliest days of working with the data engineering community starting in 2013, he has witnessed the unique power of specialized networks to bring inspiration, knowledge, and support to technical professionals. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // Related Links Website: datacouncil.ai Youtube channel: https://www.youtube.com/c/DataCouncil --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Pete on LinkedIn: https://www.linkedin.com/in/petesoder/ Timestamps: [00:00] Introduction to Pete Soderling [04:35] Top takeaways from the World's First Data Engineering Conference [05:41] Buzz around the conference [06:37] Intro to Data Council [09:20] Pete's mission statement with investing [11:19] Evaluating gaps in the market and who should solve those [14:45] One company Peter regrets not investing in [16:41] Repeating the same mistake [20:07] Recommendations to engineers to become entrepreneurs [23:30] Questions to consider before investing [27:37] Things to do and avoid in open-source projects [31:03] Something popular you disagree [35:29] Code as an artifact [39:16] Hypothetical fundraising   [40:53] Wrap up

MLOps Coffee Sessions #91 with Joseph Haaga, The Shipyard: Lessons Learned While Building an ML Platform / Automating Adherence. // Abstract Joseph Haaga and the Interos team walk us through their design decisions in building an internal data platform. Joseph talks about why their use case wasn't a fit for off the self solutions, what their internal tool snitch does, and how they use git as a model registry.   Shipyard blogpost series: https://medium.com/interos-engineering. // Bio Joseph leads the ML Platform team at Interos, the operational resilience company. He was introduced to ML Ops while working as a Senior Data Engineer and has spent the past year building a platform for experimentation and serving. He lives in Washington, DC, with his dog Cheese. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // Related Links Website: https://joehaaga.xyz Medium: https://medium.com/interos-engineering Shipyard blogpost series: https://medium.com/interos-engineering --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Joseph on LinkedIn: https://www.linkedin.com/in/joseph-haaga/ Timestamps: [00:00] Introduction to Joseph Haaga [02:07] Please subscribe, follow, like, rate, review our Spotify and Youtube channels [02:31] New! Best of Slack Weekly Newsletter [03:03] Interos [04:33] Global supply chain [05:45] Machine Learning use cases of Interos [06:17] Forecasting and optimization of routes [07:14] Build, buy, open-source decision making [10:06] Experiences with Kubeflow [11:05] Creating standards and rules when creating the platform   [13:29] Snitches [14:10] Inter-team discussions when processes fall apart [16:56] Examples of the development process on the feedback of ML engineers and data scientists [20:35] Preserving flexibility when introducing new models and formats [21:37] Organizational structure of Interos [23:40] Surface area for product [24:46] Use of Git Ops to manage boarding pass [28:04] Cultural emphasis [30:02] Naming conventions [32:28] Benefit of a clean slate [33:16] One-size-fits-all choice [37:34] Wrap up