How to become a better data scientist: the definite guide with Alexey Grigorev We all know what we need to do to be good data scientists: know machine learning, be able to program, 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”. 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 Alexey on LinkedIn: https://www.linkedin.com/in/agrigorev/

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://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 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. 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. 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 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   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 talk 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, this 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://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 Cristiano on LinkedIn:  https://www.linkedin.com/in/cristiano-breuel/ Connect with Caique on LinkedIn: https://www.linkedin.com/in/caiquelima/

DataOps and Data version Control   MLOps.community meetup #19 with the Founder and creator of DVC.org Dmitry Petrov.  Data versioning and data management are core components of MLOps and any end-to-end AI platform. What challenges are related to data versioning and how to overcome these? What are the benefits of using Git and data codification as a foundation of data versioning? And how open data versioning tools can enable an open MLOps ecosystem instead of closed end-to-end ML platforms.   DVC and other tools: Basic modeling scenarios Automation of modeling Model deployments: to server or docker. DVC as a model registry. CI/CD for ML   Dmitry is a creator of open-source tool Data Version Control - DVC.org - or Git for data. He is a former data scientist at Microsoft with Ph.D. in Computer Science. Now Dmitry is working on tools for machine learning and data versioning as a Co-Founder and CEO of Iterative.AI in San Francisco, CA.   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 Dmitry on LinkedIn: https://www.linkedin.com/in/dmitryleopetrov/

Exploring the world of model serving in machine learning, discussing serverless concepts, API endpoints, streaming and batch data, with a sprinkle of coffee vs tea banter. They touch on real-time prediction scenarios, optimizing model serving using Kubeflow, and challenges of deploying models in production. Delve into the practical applications of Kubeflow, model training with the Iris dataset, building custom model services, and planning in-depth MLOps discussions with audience engagement.

MLOps.community meetup #17 a deep dive into the open source ML framework Hermoine built on top of MLflow with Neylson Crepalde   Key takeaways for attendees: MLOps problems are dealt with tools but also with processes Open-source framework Hermione can help in a lot of parts of the operations process   Abstract: In Neylson's experience with Machine Learning projects, he has encountered a series of challenges regarding agile processes to build and deploy ML models in a professional cooperative environment that fosters teamwork. While on this journey, Neylson and his team developed some of our their own solutions for these challenges.  Out of this was the open-source project Hermoine born . Hermoine is a collection of solutions for these specific MLOps problems that were packaged into a library, an ML project structure framework called Hermione.   In this meetup we talk about these challenges, what they did to overcome them and how Hermione helped address these different issues along the way. We will also do a demo on how to build an ML project with Hermione.   Check out Hermoine here: https://github.com/a3data/hermione   Neylson Crepalde is a partner and MLOps Tech Lead at A3Data. He holds a PhD in Economic Sociology, a masters in Sociology of Culture, an MBA in Cultural Management and a bachelor degree in Music/Conducting. He is professor of Machine Learning and Head of Data Science Department at Izabela Hendrix Methodist Technological University. His main research interests are Machine Learning processes, Politics and Deliberation, Economic Sociology and Sociology of Education. In his PhD he has worked with Multilevel Social Network Analysis and Exponential Random Graph Models to understand the social construction of quality in an orchestras’ market.   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 Neyslon on LinkedIn: https://www.linkedin.com/in/neylsoncrepalde/

John Spindler, CEO of Capital Enterprise, shares insights on evaluating machine learning startups and trends in MLOps. He discusses missed opportunities, challenges in quantum computing, and the role of humans in ML. The impact of Amazon and investing in computer vision in agriculture are also explored.

Human In The Loop Machine Learning and how to scale it with Robert Munro.  This conversation centered around the components of Human-in-the-Loop Machine Learning systems and the challenges when scaling them. Most machine learning applications learn from human examples. For example, autonomous vehicles know what a pedestrian looks like because people have spent 1000s of hours labeling “pedestrians” in videos; your smart device understands you because people have spent 1000s of hours labeling the intent of speech recordings; and machine translation services work because they are trained on 1000s of sentences that have been manually translated between languages. If you have a machine learning system that is learning from human-feedback in real-time, then there are many components to support and scale, from the machine learning models to the human interfaces and the processes for quality control.    Robert Munro is an expert in combining Human and Machine Intelligence, working with Machine Learning approaches to Text, Speech, Image and Video Processing. Robert has founded several AI companies, building some of the top teams in Artificial Intelligence. He has worked in many diverse environments, from Sierra Leone, Haiti and the Amazon, to London, Sydney and Silicon Valley, in organizations ranging from startups to the United Nations. He has shipped Machine Learning Products at startups and at/with Amazon, Google, IBM & Microsoft.   Robert has published more than 50 papers on Artificial Intelligence and is a regular speaker about technology in an increasingly connected world. He has a PhD from Stanford University. Robert is the author of Human-in-the-Loop Machine Learning (Manning Publications, 2020)   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 Robert on LinkedIn: https://www.linkedin.com/in/robertjmunro/   Robert's book on Human in the loop Machine Learning:https://www.manning.com/books/human-in-the-loop-machine-learning Blog Post "Active Learning with Pytorch": https://medium.com/pytorch/https-medium-com-robert-munro-active-learning-with-pytorch-2f3ee8ebec

Byron Allen, Senior Consultant at Servian, compares MLflow and Kubeflow, highlighting their functionalities, pros/cons, and the challenges of integrating different ML tools. They discuss managing dependencies in MLOps, the responsibility of an ML engineer in setting up MLflow, and determining the maturity level of MLOps products. The episode also explores the feasibility of using MLflow on Cloud Run and emphasizes the importance of understanding your use case in MLOps.