MLOps Coffee Sessions #104 with the creator of Apache Airflow and Apache Superset Maxime Beauchemin, Future of BI co-hosted by Vishnu Rachakonda. // Abstract // Bio Maxime Beauchemin is the founder and CEO of Preset. Original creator of Apache Superset. Max has worked at the leading edge of data and analytics his entire career, helping shape the discipline in influential roles at data-dependent companies like Yahoo!, Lyft, Airbnb, Facebook, and Ubisoft. // MLOps Jobs board https://m...
Jun 24, 2022•52 min•Season 1Ep. 104
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 str...
Jun 17, 2022•1 hr 5 min•Season 1Ep. 103
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 th...
Jun 10, 2022•52 min•Season 1Ep. 102
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 s...
Jun 03, 2022•59 min•Season 1Ep. 101
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 a...
May 27, 2022•24 min•Season 1Ep. 1
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 tra...
May 27, 2022•50 min•Season 1Ep. 100
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, R...
May 20, 2022•1 hr 4 min•Season 1Ep. 99
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 B...
May 12, 2022•58 min•Season 1Ep. 98
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 f...
May 05, 2022•54 min•Season 1Ep. 97
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...
May 02, 2022•53 min•Season 1Ep. 96
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...
Apr 25, 2022•43 min•Season 1Ep. 95
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 w...
Apr 21, 2022•46 min•Season 1Ep. 94
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, healthcar...
Apr 14, 2022•52 min•Season 1Ep. 93
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 star...
Apr 11, 2022•42 min•Season 1Ep. 92
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 ...
Apr 07, 2022•40 min•Season 1Ep. 91
MLOps Coffee Sessions #90 with Valerio Velardo, Bringing Audio ML Models into Production. // Abstract The majority of audio/music tech companies that employ ML still don’t use MLOps regularly. In these companies, you rarely find audio ML pipelines which take care of the whole ML lifecycle in a reliable and scalable manner. Audio ML probably pays the price of being a small sub-discipline of ML. It’s dwarfed by ML applications in image processing and NLP. In audio ML, novelties tend to travel slow...
Apr 04, 2022•51 min•Season 1Ep. 90
MLOps Coffee Sessions #89 with Gabriel Straub, A Journey in Scaling AI. // Abstract Gabriel talks to us about the difficulties of scaling ML products across an organization. He speaks about differences in profiles of data consumers and data producers, and the challenges of educating engineers so they have greater insights into the effects that their changes to the system may have. // Bio Gabriel joined Ocado Technology in 2020 as Chief Data Officer, bringing over 10 years of experience in...
Mar 31, 2022•53 min•Season 1Ep. 89
MLOps Coffee Sessions #88 with Javier Andres Mansilla, ML Platform Tradeoffs and Wondering Why to Use Them. // Abstract Javier runs ML Platform at Mercado Libre. We’re here with Javier because he’s going to tell us about what the ML platform at Mercado Libre looks like granularly, talk about its purpose, lessons, wins, and future improvements, and share with us some of the most challenging use cases they’ve had to engineer around. // Bio During the last 3 years building the internal ML platform ...
Mar 28, 2022•54 min•Season 1Ep. 88
MLOps Coffee Sessions #87 with Kyle Morris, Don't Listen Unless You Are Going to Do ML in Production. // Abstract Companies wanting to leverage ML specializes in model quality (architecture, training method, dataset), but face the same set of undifferentiated work they need to productionize the model. They must find machines to deploy their model on, set it up behind an API, make the inferences fast, cheap, reliable by optimizing hardware, load-balancing, autoscaling, clustering launches per reg...
Mar 17, 2022•52 min•Season 1Ep. 87
MLOps Coffee Sessions #86 with Julien Bisconti, Building ML/Data Platform on Top of Kubernetes. // Abstract When building a platform, a good start would be to define the goals and features of that platform, knowing it will evolve. Kubernetes is established as the de facto standard for scalable platforms but it is not a fully-fledged data platform. Do ML engineers have to learn and use Kubernetes directly? They probably shouldn't. So it is up to the data engineering team to p...
Mar 12, 2022•48 min•Season 1Ep. 86
MLOps Coffee Sessions #85 with Emmanuel Ameisen, Continuous Deployment of Critical ML Applications. // Abstract Finding an ML model that solves a business problem can feel like winning the lottery, but it can also be a curse. Once a model is embedded at the core of an application and used by real users, the real work begins. That's when you need to make sure that it works for everyone, that it keeps working every day, and that it can improve as time goes on. Just like building a model is all abo...
Mar 10, 2022•45 min•Season 1Ep. 85
MLOps Coffee Sessions #84 with Ernest Chan, Lessons from Studying FAANG ML Systems. // Abstract Large tech companies invest in ML platforms to accelerate their ML efforts. Become better prepared to solve your own MLOps problems by learning from their technology and design decisions. Tune in to learn about ML platform components, capabilities, and design considerations. // Bio Ernest is a Data Scientist at Duo Security. As part of the core team that built Duo's first ML-powered product, Duo Trust...
Mar 02, 2022•46 min•Season 1Ep. 84
MLOps Coffee Sessions #83 with Vincent Warmerdam, Better Use cases for Text Embeddings. // Abstract Text embeddings are very popular, but there are plenty of reasons to be concerned about their applications. There's algorithmic fairness, compute requirements as well as issues with datasets that they're typically trained on. In this session, Vincent gives an overview of some of these properties while also talking about an underappreciated use-case for the embeddings: labeling! // Bio Vincent D. W...
Feb 28, 2022•48 min•Season 1Ep. 83
MLOps Reading Group meeting on February 11, 2022 Reading Group Session about Feature Stores with Matt Delacour and Mike Moran --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Connect with us on LinkedIn: https://www.linkedin.com/company/mlopscommunity/ Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, Feature Store, Machine Learning Monitoring and Bl...
Feb 23, 2022•50 min
MLOps Community Meetup #93! Two weeks ago, we talked to Chad Sanderson, Trustworthy Data for Machine Learning. //Abstract The most common challenge for ML teams operating at scale is data quality. In this talk, Chad discusses how Convoy invested in a large-scale data quality effort to treat data as an API and provide a data change management surface to enable trustworthy machine learning. // Bio Chad Sanderson is the Product Lead for Convoy's Data Platform team, which includes the data warehouse...
Feb 21, 2022•51 min•Season 1Ep. 93
MLOps Coffee Sessions #82 with Donna Schut and Christos Aniftos, Practitioners Guide to MLOps. // Abstract The "Practitioners Guide to MLOps" introduced excellent frameworks for how to think about the field. Can we talk about how you've seen the advice in that guide applied to real-world systems? Is there additional advice you'd add to that paper based on what you've seen since its publication and with new tools being introduced? Your article about selecting the right capabilities has a lot of g...
Feb 15, 2022•47 min•Season 1Ep. 82
MLOps Coffee Sessions #81 with Davis Treybig and Leigh Marie Braswell, Machine Learning from the Viewpoint of Investors. // Abstract Machine learning is a rapidly evolving space that can be hard to keep track of. Every year, thousands of research papers are published in the space, and hundreds of new companies are built both in applied machine learning as well as in machine learning tooling. In this podcast, we interview two investors who focus heavily on machine learning to get their take on th...
Feb 14, 2022•49 min•Season 1Ep. 81
MLOps Coffee Sessions #80 with Ale Solano, The Journey from Data Scientist to MLOps Engineer. // Abstract After years of failed POCs then all of a sudden one of our models is accepted and will be used in production. The next morning we are part of the main scrum stand-up meeting and a DevOps guy is assisting us. A strange feeling, unknown to us until then, starts growing on the AI team: we are useful! Deploying models to production is challenging, but MLOps is more than that. MLOps is about maki...
Feb 08, 2022•42 min•Season 1Ep. 80
MLOps Coffee Sessions #79 with Orr Shilon, Platform Thinking: A Lemonade Case Study. // Abstract This episode is the epitome of why people listen to our podcast. It’s a complete discussion of the technical, organizational, and cultural challenges of building a high-velocity, machine learning platform that impacts core business outcomes. Orr tells us about the focus on automation and platform thinking that’s uniquely allowed Lemonade’s engineers to make long-term investments t...
Feb 04, 2022•52 min•Season 1Ep. 79
MLOps Coffee Sessions #78 with Erica Greene and Seoyoon Park, Calibration for ML at Etsy - apply() special. // Abstract This is a special conversation about Machine Learning calibration at Etsy. Demetrios sat down with Erica Greene and Seoyoon Park to hear about how they implemented Calibration into the Etsy Machine Learning workflow. The conversation is a pre-chat with these two before their presentation at the apply() conference on February 10th. Register here: applyconf.com // Bio Erica Geen ...
Jan 31, 2022•50 min•Season 1Ep. 78
