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 standardized interfaces required for us to enable monitoring across hundreds or thousands of heterogeneous machine learning models.


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Instructor's Bio

Alejandro Saucedo

 Chief Scientist | The Institute for Ethical AI & Machine Learning

Alejandro is 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. Alejandro is also the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he leads the development of industry standards on machine learning bias, adversarial attacks and differential privacy. 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 departments of machine learning 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).

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    Production Machine Learning Monitoring: Principles, Patterns and Techniques

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