Production ML Monitoring: Outliers, Drift, Explainers & Statistical Performance

We are excited to invite Alejandro Saucedo, Engineering Director (Machine Learning), Seldon Technologies, as well as Chief Scientist at the Institute for Ethical AI & Machine Learning, to talk about Production Machine Learning Monitoring. This is one hot topic in the Production ML or MLOp field. Join us. make sure register at Zoom link below

https://us02web.zoom.us/webinar/register/WN_3z-XrGh0Rqa5OezOyGQ7xA

Agenda
Time Zone (PST)

12:00 pm -- 12:05 pm member join zoom
12:05 pm -- 12:06 pm Quick Intro
12:06 pm -- 1:10 pm pm Talk + QA
1:10 pm -- 1:15 pm Closing

Talk: Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance

Description: The lifecycle of a machine learning model only begins once it's in production. In this talk we provide a practical deep dive on best practices, principles, patterns and techniques around production monitoring of machine learning models. We will cover standard microservice monitoring techniques applied into deployed machine learning models, as well as more advanced paradigms to monitor machine learning models through concept drift, outlier detector and explainability. We'll dive into a hands on example, where we will train an image classification machine learning model from scratch, deploy it as a microservice in Kubernetes, and introduce advanced monitoring components as architectural patterns with hands on examples. These monitoring techniques will include AI Explainers, Outlier Detectors, Concept Drift detectors and Adversarial Detectors. We will also be understanding high level architectural patterns that abstract these complex and advanced monitoring techniques into infrastructural components that will enable for scale, introducing the standardised interfaces required for us to enable monitoring across hundreds or thousands of heterogeneous machine learning models.

Speaker: Alejandro Saucedo

Alejandro is the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he leads the development of industry standards on machine learning explainability, adversarial robustness and differential privacy. Alejandro is also the Director of Machine Learning Engineering at Seldon Technologies, where he leads large scale projects implementing open source and enterprise infrastructure for Machine Learning Orchestration and Explainability. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and has delivered multi-national projects with top tier investment banks, magic circle law-firms and global insurance companies. He has a strong track record building cross-functional departments of software engineers from scratch, and leading the delivery of large-scale machine learning systems across the financial, insurance, legal, transport, manufacturing and construction sectors (in Europe, US and Latin America).

LInkedin: https://linkedin.com/in/axsaucedo
Twitter: https://twitter.com/axsaucedo
Github: https://github.com/axsaucedo
Website: https://ethical.institute/