The State of Production AI & Machine Learning in 2020

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-production-machine-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.