Agentic Conversations (formally mlops.community)

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We asked what you wanted to hear next in our Coffee sessions, and the vote was in favor of feature stores! Today, the usual suspects, Demetrios Brinkmann and David Aponte, sat down to talk with Jim Dowling, CEO of Logical Clocks, and Venkata Pingali, CEO of Scribble Data, to talk about feature stores, what they are, why we need them, some business implications, and everything in between!   

As always, if you enjoyed the session, let us know or reach out to us in Slack!   

Check out what Jim is doing around Hopsworks and open-sourced feature stores at Logical Clocks: https://www.logicalclocks.com/  

Find out more about the feature stores that Venkata is building at Scribble Data: https://www.scribbledata.io/  

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/  
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/

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As more and more machine learning models are deployed into production, it is imperative that we have better observability tools to monitor, troubleshoot, and explain their decisions. In this talk, Aparna Dhinakaran, Co-Founder, CPO of Arize AI (Berkeley-based startup focused on ML Observability), will discuss the state of the commonly seen ML Production Workflow and its challenges. She will focus on the lack of model observability, its impacts, and how Arize AI can help.  

This talk highlights common challenges seen in models deployed in production, including model drift, data quality issues, distribution changes, outliers, and bias. The talk will also cover best practices to address these challenges and where observability and explainability can help identify model issues before they impact the business. Aparna will be sharing a demo of how the Arize AI platform can help companies validate their models' performance, provide real-time performance monitoring and alerts, and automate troubleshooting of slices of model performance with explainability. The talk will cover best practices in ML Observability and how companies can build more transparency and trust around their models.

Aparna Dhinakaran is Chief Product Officer at Arize AI, a startup focused on ML Observability. She was previously an ML engineer at Uber, Apple, and Tubemogul (acquired by Adobe). During her time at Uber, she built a number of core ML Infrastructure platforms, including Michaelangelo. She has a bachelor's from Berkeley's Electrical Engineering and Computer Science program, where she published research with Berkeley's AI Research group. She is on a leave of absence from the Computer Vision PhD program at Cornell University.

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/
Connect with Aparna on LinkedIn: https://www.linkedin.com/in/aparnadhinakaran/

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In this talk, I demonstrate an example of an ML project development and production workflows which we build on top of our proprietary core - Neu.ro - using a number of open-source and proprietary tools: Jupyter Notebooks, Tensorboard, FileBrowser, PyCharm Professional, Cookiecutter, Git, DVC, Airflow, Seldon, and Grafana. I describe how we integrate each of these tools with Neu.ro, and how we can improve these integrations.

Mariya came to MLOps from a software development background. She started her career as a Java developer at JetBrains in 2011, then gradually moved to developer advocacy for JS-based APIs. In 2019, she joined Neu.ro as a platform developer advocate and then moved to the product management position. She has been obsessed with AI and ML for many years: she finished a bunch of courses, read a lot of books, and even wrote a couple of fiction stories about AI. She believes that proper tooling and decent development and operations practices are an essential success component for ML projects, as well as they are for traditional SD.

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/
Connect with Mariya on LinkedIn: https://www.linkedin.com/in/mariya-davydova/

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It can be tricky to explain MLOps to colleagues and managers who are used to traditional software engineering and DevOps, let alone your gran. We have to answer the 'Isn't that just DevOps?' question clearly, otherwise the challenges of MLOps will continue to be underestimated (potentially by us as well as others). In this session, we dive into what is new about MLOps and why current mainstream DevOps alone does not solve the problems.

Ryan Dawson is an Engineer at Seldon and the author of the article 'Why is DevOps for Machine Learning so Different?' You can find that article at https://hackernoon.com/why-is-devops-for-machine-learning-so-different-384z32f1

||Show Notes||  

LF AI Foundation Interactive Landscape: https://landscape.lfai.foundation/
Seldon Docs: https://docs.seldon.io/en/latest/
An awesome list of references for MLOps: https://github.com/visenger/awesome-mlops
Awesome production machine learning: https://github.com/EthicalML/awesome-production-machine-learning

Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://zoom.us/webinar/register/WN_a_nuYR1xT86TGIB2wp9B1g  

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Ryan on LinkedIn https://www.linkedin.com/in/ryan-dawson-501ab9123/

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Python's most popular data science libraries—pandas, numpy, and scikit-learn—were designed to run on a single computer, and in some cases, using a single processor. Whether this computer is a laptop or a server with 96 cores, your compute and memory are constrained by the size of the biggest computer you have access to.

In this course, you'll learn how to use Dask, a Python library for parallel and distributed computing, to bypass this constraint by scaling our compute and memory across multiple cores. Dask provides integrations with Python libraries like pandas, numpy, and scikit-learn, so you can scale your computations without having to learn completely new libraries or significantly refactoring your code.

Daniel Gerlanc has worked as a data scientist for more than a decade and written software professionally for 15 years. He spent 5 years as a quantitative analyst with two Boston hedge funds before starting Enplus Advisors. At Enplus, he works with clients on data science and custom software development with a particular focus on projects requiring expertise in both areas. He teaches data science and software development at introductory through advanced levels. He has co-authored several open source R packages, published in peer-reviewed journals, and is active in local predictive analytics groups.

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/
Connect with Daniel on LinkedIn: https://www.linkedin.com/in/dgerlanc/

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How To Monitor Machine Learning Stacks - Why Current Monitoring is Unable to Detect Serious Issues and What to Do About It with Lina Weichbrodt.  

Monitoring usually focuses on the “four golden signals”: latency, errors, traffic, and saturation. Machine learning services can suffer from special types of problems that are hard to detect with these signals. The talk will introduce these problems with practical examples and suggest additional metrics that can be used to detect them. 

A case study demonstrates how these new metrics work for the recommendation stacks at Zalando, one of Europe’s largest fashion retailers.  

Lina has 8+ years of industry experience in developing scalable machine learning models and bringing them into production. She currently works as the Machine Learning Lead Engineer in the data science group of the German online bank DKB. She previously worked at Zalando, one of Europe’s biggest online fashion retailers, where she developed real-time, deep learning personalization models for more than 32M users.   

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Lina on LinkedIn: https://www.linkedin.com/in/lina-weichbrodt-344a066a/

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How to become a better data scientist: the definitive guide with Alexey Grigorev

We all know what we need to do to be good data scientists: know machine learning, be able to program, and be fluent in SQL and Python. That’s enough to do our job quite well. But what does it take to be a better data scientist?

The best way to grow as a data scientist is to step out of direct responsibilities and try on the hats of a product manager as well as a DevOps engineer. In particular, we should:
- be pragmatic and product-oriented
- communicate more
- get into infrastructure

After listening to this talk, you will know how exactly we should do it.

Alexey lives in Berlin with his wife and son. He’s a software engineer with a focus on machine learning. He works at OLX Group as a Lead Data Scientist. Alexey is a Kaggle master, and he wrote a couple of books. One of them is “Mastering Java for Data Science,” and now he’s working on another one — “Machine Learning Bookcamp”.

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Sign up for the next meetup: https://zoom.us/webinar/register/WN_a_nuYR1xT86TGIB2wp9B1g

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/
Connect with Alexey on LinkedIn: https://www.linkedin.com/in/agrigorev/

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Companies are increasingly investing in Machine Learning (ML) to deliver new customer experiences and re-invent business processes. Unfortunately,  the majority of operational  ML  projects never make it to production. The most significant blocker is the lack of infrastructure and tooling required to build production-ready data for ML.  

Kevin Stumpf has a long history of building data infrastructure for ML, first for Uber Michelangelo, and most recently as co-founder of Tecton. Kevin will share his insights on the challenges of getting ML features to production. We’ll discuss the role of the feature store in bringing DevOps-like efficiency to building ML features. Kevin will also provide an overview of Tecton, which aims to bring an enterprise-grade feature store to every company.

Kevin co-founded Tecton, where he leads a world-class engineering team that is building a next-generation feature store for operational Machine Learning. Kevin and his co-founders built deep expertise in operational ML platforms while at Uber, where they created the Michelangelo platform that enabled Uber to scale from 0 to 1000's of ML-driven applications in just a few years. Prior to Uber, Kevin founded Dispatcher with the vision to build the Uber for long-haul trucking. Kevin holds an MBA from Stanford University and a Bachelor's Degree in Computer and Management Sciences from the University of Hagen. Outside of work, Kevin is a passionate long-distance endurance athlete.

Join our Slack community: https://go.mlops.community/slack
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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/
Connect with Cristiano on LinkedIn: https://www.linkedin.com/in/cristiano-breuel/
Connect with Kevin on LinkedIn: https://www.linkedin.com/in/kevinstumpf/

David Aponte and Misha sat down and talked in depth about what the ML tool Paperspace can do.

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Misha Kutsovsky is a Senior Machine Learning Architect at Paperspace, working on the Gradient team. He has expertise in machine learning, deep learning, distributed training, and MLOps. Previously, he was on Microsoft's Windows Active Defense team, building fileless malware detection software and tooling machine learning systems for Microsoft DevOps & Data Scientist teams. He holds B.S. and M.S. degrees in Electrical & Computer Engineering from Carnegie Mellon University.

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/
Connect with Cristiano on LinkedIn: https://www.linkedin.com/in/cristiano-breuel/
Connect with Paperspace on LinkedIn: https://www.linkedin.com/company/paperspace/

Running a Fintech on Machine Learning

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For this meetup, we sat down with Caique Lima and Cristiano Breuel, Machine Learning Engineers at the Brasilian Fintech Nubank.   

Nubank is a Fintech providing credit and banking services to more than 20 million customers. Data science has been one of the company's pillars since the beginning, and many of its critical decisions in production are made with ML, in areas such as Credit, Fraud, and Customer Service. We discussed how they develop, deploy, and monitor ML models, and also talked about how they built those in-house solutions over the years. Today, they use MLOps to support a team of more than 70 Data Scientists/ Machine Learning Engineers.  

Caique is a Machine Learning Engineer at Nubank, developing software to scale decision-making, which goes from model development to monitoring. Always trying to bring good practices from Software development to Data teams.  Cristiano, a Machine Learning Engineer at Nubank, works to improve the efficiency and quality of ML development. Previously ML/Data Engineer at Google, specializing in MLOps, and Software Engineer at IBM.  

Join our Slack community: https://go.mlops.community/slack
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Sign up for the next meetup: https://zoom.us/webinar/register/WN_a_nuYR1xT86TGIB2wp9B1g  

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/
Connect with Cristiano on LinkedIn:  https://www.linkedin.com/in/cristiano-breuel/
Connect with Caique on LinkedIn: https://www.linkedin.com/in/caiquelima/