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 leadin...
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 provide the tools and ...
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 Blogs: https://m...
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 that have paid off in...
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
MLOps Coffee Sessions #77 with Scott Hirleman, Data Mesh - The Data Quality Control Mechanism for MLOps? // Abstract Scott covers what is a data mesh at a high level for those not familiar. Data mesh is potentially a great win for ML/MLOps as there is very clear guidance on creating useful, clean, well-documented/described and interoperable data for "unexpected use". So instead of data spelunking being a harrowing task, it can be a very fruitful one. And that one data set that was so awesome? We...
Jan 28, 2022•57 min•Season 1Ep. 77
MLOps Coffee Sessions #76 with Mohamed Elgendy, Build a Culture of ML Testing and Model Quality. // Abstract Machine learning engineers and data scientists spend most of their time testing and validating their models’ performance. But as machine learning products become more integral to our daily lives, the importance of rigorously testing model behavior will only increase. Current ML evaluation techniques are falling short in their attempts to describe the full picture of model performance. Eva...
Jan 25, 2022•51 min•Season 1Ep. 76
MLOps Coffee Sessions #75 with Shreya Shankar, Towards Observability for ML Pipelines. // Abstract Achieving observability in ML pipelines is a mess right now. We are tracking thousands of means, percentiles, and KL divergences of features and outputs in a haphazard attempt to figure out when and how to retrain models. In this session, we break down current unsuccessful approaches and discuss the path towards effectively maintaining ML models in production. Along the way, we introduce mltrace --...
Jan 21, 2022•57 min•Season 1Ep. 75
MLOps Coffee Sessions #74 with Jesse Johnson, Scaling Biotech. // Abstract Scaling a biotech research platform requires managing organization complexity - teams, functions, projects - rather than just the traditional volume, velocity, and variety. By examining the processes and experiments that drive the platform, you can focus your work where it matters the most by finding the ideal balance for each type of experiment along with a number of common trade-offs. // Bio Jesse Johnson is head of Dat...
Jan 19, 2022•51 min•Season 1Ep. 74
MLOps Coffee Sessions #73 with Breno Costa and Matheus Frata, On Structuring an ML Platform 1 Pizza Team. // Abstract Breno and Matheus were part of an organizational change at Neoway in recent years. With the creation of cross-functional and platform teams in order to improve the value stream generated by these. They share their experience in creating a machine learning platform team. The challenges they faced along the way, how they approached using product thinking and the results achieved so...
Jan 07, 2022•53 min•Season 1Ep. 73
MLOps Coffee Sessions #72 with Vishnu Rachakonda and Demetrios Brinkmann, 2021 MLOps Year in Review. // Abstract Vishnu and Demetrios sit down to reflect on some of the biggest news and learnings from 2021 from the biggest funding rounds to best insights. The two finish out the chat by talking about what to expect in 2022. // Bio Demetrios Brinkmann At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups...
Jan 03, 2022•52 min•Season 1Ep. 72
Loblaws is one of Canada’s largest grocery store chains, Mefta's team at Loblaw Digital runs several ML systems such as search, recommendations, inventory, and labor prediction on production. In this conversation, he shares his experience setting up their ML platform on GCP using Vertex AI and open-source tools. The goal of this platform is to help all the data science teams within their organization to take ML projects from EDA to production rapidly while ensuring end-to-end tracking of these M...
Dec 28, 2021•40 min•Season 1Ep. 71
MLOps Coffee Sessions #70 with Reah Miyara, 2022 Predictions for MLOps and the Industry. // Abstract MLOps has moved fast in the last year. What will 2022 be like in the MLOps ecosystem? Raeh from Arize AI comes on to talk to us about what he expects for the new year. Arize is kindly offering 20 free subscriptions to their tool. No marketing BS these are design partners. First come first serve https://arize.com/mlops-signup/ ! // Bio Reah Miyara is a Senior Product Manager at Arize AI, a leading...
Dec 23, 2021•36 min•Season 1Ep. 70
MLOps Coffee Sessions #69 with James Lamb, Building for Small Data Science Teams co-hosted by Adam Sroka. // Abstract In this conversation, James shares some hard-won lessons on how to effectively use technology to create applications powered by machine learning models. James also talks about how making the "right" architecture decisions is as much about org structure and hiring plans as it is about technological features. // Bio James Lamb is a machine learning engineer at SpotHero, a Chicago-b...
Dec 20, 2021•53 min•Season 1Ep. 69
MLOps Coffee Sessions #68 with Chris Albon, Wikimedia MLOps co-hosted by Neal Lathia. // Abstract // Bio Chris spent over a decade applying statistical learning, artificial intelligence, and software engineering to political, social, and humanitarian efforts. He is the Director of Machine Learning at the Wikimedia Foundation. Previously, Chris was the Director of Data Science at Devoted Health, Director of Data Science at the Kenyan startup BRCK, cofounded the AI startup Yonder, created the data...
Dec 13, 2021•1 hr 6 min•Season 1Ep. 68
MLOps Coffee Sessions #67 with John Crousse, ML Stepping Stones: Challenges & Opportunities for Companies co-hosted by Adam Sroka. // Abstract In this coffee session, John shares his observations after working with multiple companies which were in the process of scaling up their ML capabilities. John's observations are mostly around changes in practices, successes, failures, and bottlenecks identified when building ML products and teams from scratch. John shares a few thoughts on building lo...
Dec 09, 2021•48 min•Season 1Ep. 67
MLOps Coffee Sessions #66 with Jacopo Tagliabue, Machine Learning at Reasonable Scale. // Abstract We believe that immature data pipelines are preventing a large portion of industry practitioners from leveraging the latest research on ML: truth is, outside of Big Tech and advanced startups, ML systems are still far from producing the promised ROI. The good news is that times are changing: thanks to a growing ecosystem of tools and shared best practices, even small teams can be incredibly product...
Dec 08, 2021•1 hr 5 min•Season 1Ep. 66
MLOps Coffee Sessions #65 with Skylar Payne, The Future of Data Science Platforms is Accessibility. // Abstract The machine learning and data science space is blowing up -- new tools are popping up every day. While we seem to have every type of "Flow" and "Store" you could imagine, few people really understand how to glue this stuff together. Despite all the tools we have available, we still see companies failing to leverage data science effectively to drive business results. Instead of spending...
Nov 30, 2021•52 min•Season 1Ep. 65
MLOps Reading Group meeting on November 20, 2021 --------------- ✌️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 Blogs: https://mlops.community/
Nov 29, 2021•56 min
MLOps Coffee Sessions #64 with Slater Victoroff, The Future of AI and ML in Process Automation. // Abstract The Unstructured Imperative Recent advances in AI have dramatically advanced the state of the art around unstructured data, especially in the spaces of NLP and computer vision. Despite this, the adoption of unstructured technologies has remained low. Why do you think that is? How have the dynamics changed in the last five years? Multimodal AI Historic AI approaches have generally been cons...
Nov 23, 2021•58 min•Season 1Ep. 64
Dmytro Dzhulgakov, PyTorch: Bridging AI Research and Production. Talking PyTorch is always interesting, as the Facebook ML OSS project is one of the most important parts of the machine learning tooling ecosystem. This week, we talked to Dmytro Dzhulgakov, a tech lead for PyTorch. We started off talking about Dmytro's journey to being an engineer and tech lead at Facebook, and what his role entails. Dmytro has been at Facebook for 10+ years, so he gave some very interesting advice on how to manag...
Nov 16, 2021•53 min•Season 1Ep. 63
