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MLOps.community

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

technology

All Episodes

00:57:06

Data Mesh - The Data Quality Control Mechanism for...

MLOps.community ·

2022/01/28

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

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00:51:13

Build a Culture of ML Testing and Model Quality //...

MLOps.community ·

2022/01/25

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

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00:57:09

Towards Observability for ML Pipelines // Shreya...

MLOps.community ·

2022/01/21

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 -- a

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00:51:14

Scaling Biotech // Jesse Johnson // MLOps Coffee...

MLOps.community ·

2022/01/19

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 Data

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00:52:53

On Structuring an ML Platform 1 Pizza Team //Breno...

MLOps.community ·

2022/01/07

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

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00:51:31

2021 MLOps Year in Review // Vishnu Rachakonda and...

MLOps.community ·

2022/01/03

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

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00:40:04

Setting up an ML Platform on GCP: Lessons Learned //...

MLOps.community ·

2021/12/28

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 ML

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00:36:26

2022 Predictions for MLOps and the Industry // Reah...

MLOps.community ·

2021/12/23

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

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00:52:36

Building for Small Data Science Teams // James Lamb...

MLOps.community ·

2021/12/20

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

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01:05:49

Wikimedia MLOps // Chris Albon // Coffee Sessions #68

MLOps.community ·

2021/12/13

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

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00:47:58

ML Stepping Stones: Challenges & Opportunities for...

MLOps.community ·

2021/12/09

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

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01:04:43

Machine Learning at Reasonable Scale // Jacopo...

MLOps.community ·

2021/12/08

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

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00:52:26

The Future of Data Science Platforms is Accessibility...

MLOps.community ·

2021/11/30

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

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00:55:42

Impact of SWE in ML Projects // Laszlo Sragner and...

MLOps.community ·

2021/11/29

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/

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00:58:01

The Future of AI and ML in Process Automation //...

MLOps.community ·

2021/11/23

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

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00:53:10

PyTorch: Bridging AI Research and Production //...

MLOps.community ·

2021/11/16

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 manage

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00:55:51

I Don't Like Jupyter Notebooks // Joel Grus // Coffee...

MLOps.community ·

2021/11/09

MLOps Coffee Sessions #62 with Joel Grus, MLOps from Scratch. // Abstract In this talk, Joel Grus of “I don’t like notebooks” fame shares with us his 2021 perspective on notebooks, where he thinks MLOps is now, and what his hot takes in the data space are now. // Bio Joel Grus is a Principal Engineer at Capital Group, where he leads a team that builds search, data, and machine learning products for the investment group. He is the author of the bestselling O'Reilly book *Data Science from Scratch*,

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00:40:35

ML Tests // Svet Penkov // Coffee Sessions #61

MLOps.community ·

2021/11/02

MLOps Coffee Sessions #60 with Svet Penkov, ML Tests. // Abstract How confident do you feel when you deploy a new model? Does improving an ML model feel like a game of "whack-a-mole"? ML is taking over all sorts of industries and yet ML testing tools are virtually non-existent. Drawing parallels from software engineering and electronic circuit board design to the aviation and semiconductor industries, the need for principled quality assurance (QA) step in the MLOps pipeline is long overdue. Let's

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00:51:40

Linkedin Job Recommendations // Alexandre Patry //...

MLOps.community ·

2021/10/25

Coffee Sessions #60 with Alexandre Patry, Path to Productivity in Job Search and Job Recommendation AI at LinkedIn. // Abstract A year ago, LinkedIn job search and recommendation AI teams were at the end of a growth cycle. They were fighting many fires at once: a high number of user complaints, engineers spending a significant amount of their time keeping our machine learning pipelines running, online infrastructure that wasn't supporting their growth, and challenges ramping new models to

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01:11:00

Data Selection for Data-Centric AI: Data Quality Over...

MLOps.community ·

2021/10/11

Coffee Sessions #59 with Cody Coleman, Data Quality Over Quantity or Data Selection for Data-Centric AI. // Abstract Big data has been critical to many of the successes inML, but it brings its own problems. Working with massive datasets is cumbersome and expensive, especially with unstructured data like images, videos, and speech. Careful data selection can mitigate the pains of big data by focusing computational and labeling resources on the most valuable examples. Cody Coleman, a recent Ph.D

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514 results

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