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David & Elle talk about how one of the staples of DevOps, the practice of continuous integration, can work for machine learning. Continuous integration is a tried-and-true method for speeding up development cycles and rapidly releasing software- an area where data science and ML could use some help. Making continuous integration work for ML has been challenging in the past, and we chat about new open-source tools and approaches in the Git ecosystem for leveling up development processes with big models and datasets.

|| Highlights ||
What is continuous integration, and why should ML/data science teams know about it?
Why ML projects tend to fall short of DevOps best practices, like frequent check-ins and testing
How we're dealing with obstacles to get continuous integration working for ML
Also, some fun chat about how data science roles are changing and how MLOps skills fit into the data science toolkit!

The DevOps Handbook: https://amzn.to/2XH7tIT

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Connect with Elle on LinkedIn: https://www.linkedin.com/in/elle-o-brien-2a4586100/

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Airflow is a renowned tool for data engineering. It helps with orchestrating ETL workloads, and it's well-regarded amongst machine learning engineers as well. So, how does Airflow work, and how is it applied to MLOps?

In this episode, Demetrios and David are joined by Simon Darr, a Managing Consultant at Servian, with many years of experience using Airflow, along with Byron Allen, a Senior Consultant at Servian, specializing in ML. The group discusses how Airflow works, its pros and cons for MLOps, and how it is used in practice, along with a short demo.

|| Links Referenced in the Show ||
Maxime Beauchemin on Medium https://medium.com/@maximebeauchemin
The Rise of the Data Engineer: https://www.freecodecamp.org/news/the-rise-of-the-data-engineer-91be18f1e603/
Using Airflow with Kubernetes at Benevolent AI: https://www.benevolent.com/engineering-blog/using-airflow-with-kubernetes-at-benevolentai

|| Sponsored Content ||
Servian is a global data consultancy, providing advisory and delivery for data engineering and ML/AI projects. Accelerate ML is their framework to streamline and maximize the impact of ML workflows on an organization. As a part of that framework, they have a free tool used to help clients understand ML maturity. Check out the framework here, along with the ML maturity assessment.

Accelerate ML framework: https://www.servian.com/accelerate-ml/
ML Maturity Assessment: https://forms.gle/4ZN9htWjSUsSBkfd7

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Connect with Byron on LinkedIn: https://www.linkedin.com/in/byronaallen/

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Most MLOps discussion traditionally focuses on model deployment, containerization, and model serving - but where do the inputs come from, and where do the outputs get used? In this session, we demystify parts of the data science process used to create the all-important target variable and design machine learning experiments.

We discuss some probability and statistical concepts that are useful for MLOps professionals. Knowledge of these concepts may assist practitioners working closely with data scientists or those who aspire to build complex experimentation frameworks.

Danny is a recovering data scientist who has moved over to the dark side of ML engineering in the past 2 years. He has spent multiple years deploying ML models and designing customer experiments in the retail and banking sectors. Danny's passion is to guide businesses and individuals on their AI & machine learning journey. He believes a clear understanding of data strategy and applied machine learning will be a key differentiator in this brave new world. He currently provides personalized mentorship for 400+ aspiring data professionals through the #DataWithDanny 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 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 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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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

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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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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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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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