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PODCAST

MLOps.community

Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)

technology

All Episodes

00:43:04

MLOps as Tool to Shape Team and Culture // Ciro Greco...

MLOps.community ·

2022/04/25

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

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00:46:03

Traversing the Data Maturity Spectrum: A Startup...

MLOps.community ·

2022/04/21

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

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00:51:36

Model Monitoring in Practice: Top Trends //...

MLOps.community ·

2022/04/14

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,

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00:41:59

Building the World's First Data Engineering...

MLOps.community ·

2022/04/11

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

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00:39:59

The Shipyard: Lessons Learned While Building an ML...

MLOps.community ·

2022/04/07

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

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00:50:45

Bringing Audio ML Models into Production // Valerio...

MLOps.community ·

2022/04/04

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

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00:52:43

A Journey in Scaling AI // Gabriel Straub // MLOps...

MLOps.community ·

2022/03/31

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 leading

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00:53:59

ML Platform Tradeoffs and Wondering Why to Use Them...

MLOps.community ·

2022/03/28

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

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00:51:33

Don't Listen Unless You Are Going to Do ML in...

MLOps.community ·

2022/03/17

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

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00:48:15

Building ML/Data Platform on Top of Kubernetes //...

MLOps.community ·

2022/03/12

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

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00:44:38

Continuous Deployment of Critical ML Applications //...

MLOps.community ·

2022/03/10

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 about

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00:45:34

Lessons from Studying FAANG ML Systems // Ernest Chan...

MLOps.community ·

2022/03/02

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

en-us

00:48:22

Better Use cases for Text Embeddings // Vincent...

MLOps.community ·

2022/02/28

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

en-us

00:49:35

Feature Stores at Shopify and Skyscanner // Matt...

MLOps.community ·

2022/02/23

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:

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00:51:03

Trustworthy Data for Machine Learning // Chad...

MLOps.community ·

2022/02/21

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

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00:46:36

Practitioners Guide to MLOps // Donna Schut and...

MLOps.community ·

2022/02/15

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

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00:48:54

Investing in MLOps // Leigh Marie Braswell and Davis...

MLOps.community ·

2022/02/14

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 the

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00:41:32

The Journey from Data Scientist to MLOps Engineer //...

MLOps.community ·

2022/02/08

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

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00:51:44

Platform Thinking: A Lemonade Case Study // Orr...

MLOps.community ·

2022/02/04

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

en-us

00:49:36

Calibration for ML at Etsy - apply() special // Erica...

MLOps.community ·

2022/01/31

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

en-us

514 results

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