Real Time Monitoring for Machine Learning in Production “As the number of production machine learning use-cases increase, we find ourselves facing new and bigger challenges where more is at stake. Because of this, it’s critical to identify the key areas to focus our efforts, so we can ensure our machine learning pipelines are reliable and scalable. In this talk we dive into the state of production machine learning for 2020. In this talk we dive into the concepts that make produciton machine learning so challenging, as well as some of the recommended tools available to tackle these challenges.
This talk will cover a set of practical end-to-end examples showcasing the machine learning phases of training, deploying and monitoring a model – specifically diving into some of the key areas in our production machine learning tools list (https://github.com/EthicalML/awesome-productionmachine- learning/). The end to end example will show how we train a machine learning model that will perform a critical task; we will dive into best practices on how to leverage tools to interpret the model, as well as tools to deploy it and furthermore the best practices and approaches to monitor the model through outlier detection, adversarial robustness techniques and concept drift detecton tools.
Date & Time: 6 November 2020, Friday 17:50-18:20
Speaker: Alejandro Saucedo
The Institute for Ethical AI & Machine Learning
Alejandro is 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. 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 hypergrowth 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 crossfunctional 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).