In this post, we provide a brief introduction to Discrete Choice Models. We examine the core idea of modeling choice behavior as a utility maximization problem and we show how - under certain conditions - this theory gives rise to the familiar Multinomial Logistic Regression model that is commonly used to address classification tasks in Machine Learning applications.

In this post, we explore some interesting AWS technologies to build scalable Machine Learning services in the cloud. If you are curious to learn more about frameworks such as the AWS Cloud Development Kit and AWS Chalice or about managed services such as Amazon SageMaker and AWS Auto Scaling, this post is for you! For extra fun, we also show how to use the Python library Locust to perform load-tests on a real-time Machine Learning service built using the aforementioned technologies.

Clustering methods are frequently applied in real-world business applications. While clustering is a conceptually simple task, it is not always easy to evaluate whether the clusters that we find represent meaningful characteristics of a dataset. In this post, we discuss 3 tactics that can be used to improve our ability to discover meaningful clusters and be more confident about our discoveries.

Today, many of our experiences are powered by intelligent data-driven systems. Now more than ever, Machine Learning (ML) developers must be able to describe what performance and accuracy standards can be expected from their systems, and they must be able to measure whether these standards are met. Site Reliability Engineering (SRE) offers a set of principles and practices that can help ML developers address these challenges. In this article, we describe what SRE is and we discuss how Service-Level Agreements (SLAs), Service-Level Objectives (SLOs), and Service-Level Indicators (SLIs) can help ML developers build better systems for their users.