MLOps Coffee Sessions #103 with Corey Zumar, MLOps Podcast on Making MLflow co-hosted by Mihail Eric. // Abstract Because MLOps is a broad ecosystem of rapidly evolving tools and techniques, it creates several requirements and challenges for platform developers:   - To serve the needs of many practitioners and organizations, it's important for MLOps platforms to support a variety of tools in the ecosystem. This necessitates extra scrutiny when designing APIs, as well as rigorous testing strategies to ensure compatibility.   - Extensibility to new tools and frameworks is a must, but it's important not to sacrifice maintainability. MLflow Plugins (https://www.mlflow.org/docs/latest/plugins.html) is a great example of striking this balance.   - Open source is a great space for MLOps platforms to flourish. MLflow's growth has been heavily aided by: 1. meaningful feedback from a community of ML practitioners with a wide range of use cases and workflows & 2. collaboration with industry experts from a variety of organizations to co-develop APIs that are becoming standards in the MLOps space. // Bio Corey Zumar is a software engineer at Databricks, where he’s spent the last four years working on machine learning infrastructure and APIs for the machine learning lifecycle, including model management and production deployment. Corey is an active developer of MLflow. He holds a master’s degree in computer science from UC Berkeley. // MLOps Jobs board   https://mlops.pallet.xyz/jobs MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/ Connect with Corey on LinkedIn: https://www.linkedin.com/in/corey-zumar/ Timestamps: [00:00] Origin story of MLFlow [02:12] Spark as a big player [03:12] Key insights [04:42] Core abstractions and principles on MLFlow's success [07:08] Product development with open-source [09:29] Fine line between competing principles [11:53] Shameless way to pursue collaboration [12:24] Right go-to-market open-source [16:27] Vanity metrics [18:57] First gate of MLOps drug [22:11] Project fundamentals [24:29] Through the pillars [26:14] Best in breed or one tool to rule them all [29:16] MLOps space mature with the MLOps tool [30:49] Ultimate vision for MLFlow [33:56] Alignment of end-users and business values [38:11] Adding a project abstraction separate from the current ML project [42:03] Implementing bigger bets in certain directions [44:54] Log in features to experiment page [45:46] Challenge when operationalizing MLFlow in their stack [48:34] What would you work on if it weren't MLFlow? [49:52] Something to put on top of MLFlow [51:42] Proxy metric [52:39] Feature Stores and MLFlow [54:33] Lightning round [57:36] Wrap up

MLOps Coffee Sessions #102 with Yash Sheth, Fixing Your ML Data Blindspots co-hosted by Adam Sroka.   // Abstract Improving your dataset quality is absolutely critical for effective ML. Finding errors in your datasets is generally a slow, iterative, and painstaking process.     Data scientists should be proactively fixing their model’s blindspots by improving their training data. In this talk, Yash discusses how Galileo helps data scientists identify, fix, and track data across the entire ML workflow.   // Bio Co-founder and VP of Engineering. Prior to starting Galileo, Yash spent the last decade working on Automatic Speech Recognition (ASR) at Google, leading their core speech recognition platform team, that powers speech-to-text across 20+ products at Google in over 80 languages along with thousands of businesses through their Cloud Speech API.   // MLOps Jobs board   https://mlops.pallet.xyz/jobs MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.rungalileo.io/ Trade-Off: Why Some Things Catch On, and Others book by Kevin Maney: https://www.amazon.com/Trade-Off-Some-Things-Catch-Others/dp/0385525958 --------------- ✌️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 Yash on LinkedIn: https://www.linkedin.com/in/yash-sheth-72111216/ Timestamps: [00:00] Introduction to Yash Sheth [02:53] Takeaways [04:35] Why unstructured data? [06:59] Fitting in the workflow [10:56] Digging into the different pains [18:23] Vision around the democratization of machine learning [24:31] Unstructured data problem [25:49] Galileo handling unified tools [27:21] Calculus for ML [28:45] Gatekeep [29:49] Synthetic data in the unstructured data world of Galileo [33:10] Tips for data scientists that have unstructured data but with a small data set [35:00] Benefits of users from Galileo [37:15] Business case for dummies [42:36] War stories [44:49] Rapid fire questions [50:55] Wrap up

MLOps Coffee Sessions #101 with Piero Molino, Declarative Machine Learning Systems: Big Tech Level ML Without a Big Tech Team co-hosted by Vishnu Rachakonda. // Abstract Declarative Machine Learning Systems are the next step in the evolution of Machine Learning infrastructure. With such systems, organizations can marry the flexibility of low-level APIs with the simplicity of AutoML. Companies adopting such systems can increase the speed of machine learning development, reaching the quality and scalability that only big tech companies could achieve until now, without the need for a team of several thousand people. Predibase is the turnkey solution for adopting declarative ML systems at an enterprise scale. // Bio Piero Molino is CEO and co-founder of Predibase, a company redefining ML tooling. Most recently, he has been Staff Research Scientist at Stanford University working on Machine Learning systems and algorithms in Prof. Chris Ré's' Hazy group. Piero completed a Ph.D. in Question Answering at the University of Bari, Italy. Founded QuestionCube, a startup that built a framework for semantic search and QA. Worked for Yahoo Labs in Barcelona on learning to rank, IBM Watson in New York on natural language processing with deep learning, and then joined Geometric Intelligence, where he worked on grounded language understanding. After Uber acquired Geometric Intelligence, Piero became one of the founding members of Uber AI Labs. At Uber, he worked on research topics including Dialogue Systems, Language Generation, Graph Representation Learning, Computer Vision, Reinforcement Learning, and Meta-Learning. He also worked on several deployed systems like COTA, an ML and NLP model for Customer Support, Dialogue Systems for driver's hands-free dispatch, the Uber Eats Recommender System with graph learning and collusion detection. He is the author of Ludwig, a Linux-Foundation-backed open source declarative deep learning framework. // MLOps Jobs board   https://mlops.pallet.xyz/jobs MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: http://w4nderlu.st http://ludwig.ai https://medium.com/ludwig-ai Declarative Machine Learning Systems paper By Piero Molino, Christopher Ré: https://cacm.acm.org/magazines/2022/1/257445-declarative-machine-learning-systems/fulltext Slip of the Keyboard by Sir Terry Pratchett: https://www.terrypratchettbooks.com/books/a-slip-of-the-keyboard/ The Listening Society book series by Hanzi Freinacht: https://www.amazon.com/Listening-Society-Metamodern-Politics-Guides-ebook/dp/B074MKQ4LR --------------- ✌️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 Piero on LinkedIn: https://www.linkedin.com/in/pieromolino/?locale=en_US

Lightning Sessions #1 with Peeyush Agarwal, Scaling Real-time Machine Learning at Chime. // Abstract In this Lighting Talk, Peeyush Agarwal explains 2 key pieces of the ML infrastructure at Chime. Peeyush goes into detail about the current feature store design and feature monitoring process along with the ML monitoring setup. This Lighting Talk is brought to you by arize.com reach out to them for all of your ML monitoring needs. // Bio Peeyush Agarwal is the Lead Software Engineer, ML Platform at Chime. He leads the team which enables data science all the way from exploration, model development, and training to orchestrating batch and real-time models in shadow and production. Earlier, Peeyush was a founding engineer in Chime's DSML team and worked on both building models and getting them into production. Before Chime, Peeyush was a software engineer at Google where he developed unsupervised ML models that run on Google's data across search, Chrome, YouTube, and other properties to identify intent and use it for personalized ads and recommendations. At Google, he also worked on ML-powered Adaptive Brightness and Adaptive Battery which were launched into Android. Prior to joining Google, Peeyush was an entrepreneur who founded a customer engagement platform that counted Aurelia, Reebok, W, and Red Chief among its clients. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // Related Links arize.com --------------- ✌️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 Peeyush on LinkedIn: https://www.linkedin.com/in/apeeyush/ Timestamps: [00:00] Introduction to Peeyush Agarwal [01:08] Agenda [01:27] What Chime is and what Chime do [01:44] Chime's products [02:27] Data Science and Machine Learning at Chime [08:06] Chime's first real-time model [08:09] Preventing fraud on Pay Friends [11:01] Feature Store: Unblock real-time capability   [12:40] Preventing fraud on Pay Friends: Monitoring [13:35] Preventing fraud on Pay Friends: Instrumentation [14:36] Monitoring: 4 diverse ways to triage [15:27] Examples of Metrics: Feature and Model Metrics [16:39] Scaling Real-time ML at Chime [17:09] Scaling Real-time ML: Monitoring and Alerting [18:28] Scaling Real-time ML: Build tools [20:13] Scaling Real-time ML: Infrastructure Orchestration [21:36] Scaling Real-time ML: Lessons

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