Machine learning has become an increasingly important means for organizations to extract value from their data. Many companies start off with successful proofs of value but face problems when scaling their capabilities afterward. By generalizing engineering problems and solving them centrally, scaling becomes much more feasible. Model serving platforms generalize the problem of turning a machine learning model in a value-generating application. Combining a serving platform with cultural shifts such as a shift-left approach enhances efficiency even further. Bertjan is a Senior Data Engineer, with 15 years of experience in the software industry, specializing in data science and engineering for the last 10 years. He built a variety of data products and machine learning platforms. He have worked on both traditional desktop applications as well as cloud native applications in DevOps teams. He's a craftsman with a passion for delivering value through high quality software, aligning stakeholders and coaching junior and medior team members. Axel has a background in data science. While getting his data science master degree, he did software engineering and data science projects for a wide range of customers. This experience taught him that the main complexity of data science projects lies in the software built around the predictive models. After finishing his degree, he joined BigData Republic. Axel currently helps companies bring their data science capabilities to the next level. His main interest lies in tooling that speeds up the development of machine learning applications. Join our slack community: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/ Connect with Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/ Connect with Bertjan on LinkedIn: https://www.linkedin.com/in/bertjanbroeksema/ Connect with Axel on LinkedIn: https://www.linkedin.com/in/axel-goblet-5325327a/

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 Join our slack community: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/ Connect with Elle on LinkedIn: https://www.linkedin.com/in/elle-o-brien-2a4586100/

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 a 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 Join our slack community: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/ Connect with Simon on LinkedIn: https://www.linkedin.com/in/sdarr/ Connect with Byron on LinkedIn: https://www.linkedin.com/in/byronaallen/

Most MLOps discussion traditionally focuses on model deployment, containerization, 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 which 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 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 personalised mentorship for 400+ aspiring data professionals through the #DataWithDanny community. Join our slack community: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register 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 Danny on LinkedIn: https://www.linkedin.com/in/dannykcma/

We asked what you wanted to hear next on 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/   Join our slack community: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw   Follow us on Twitter: @mlopscommunity   Sign up for the next meetup: https://go.mlops.community/register    Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/   Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/

As more and more machine learning models are deployed into production, it is imperative 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 bachelors 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. Join our slack community: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register 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/

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 in 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. Join our slack community: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw 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 Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/ Connect with Mariya on LinkedIn: https://www.linkedin.com/in/mariya-davydova/

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 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://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw 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/

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 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. Join our slack community: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw 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 Cris Sterry on LinkedIn: https://www.linkedin.com/in/chrissterry/ Connect with Daniel on LinkedIn: https://www.linkedin.com/in/dgerlanc/

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 focusses 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 suggests 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.    Join our Slack community: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw   Follow us on Twitter: @mlopscommunity   Sign up for the next meetup:   https://go.mlops.community/register Connect with Demetrios on Linkedin: https://www.linkedin.com/in/dpbrinkm/ Connect with Lina on Linkedin:  https://www.linkedin.com/in/lina-weichbrodt-344a066a/