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The delineation between serverless and containers isn’t clear cut. There are container platforms deemed to be serverless such as AWS Fargate, which manage capacity and more, and there are FaaS platforms, for which an organization still manages underlying systems. For simplicity, we’ll stick to the “classic” definitions here of a managed FaaS such as AWS Lambda.

Gradient Descent is a generic optimization algorithm capable of finding optimal solutions to a wide range of problems. The general idea of Gradient Descent is to tweak parameters iteratively in order to minimize a cost function.

A data scientist needs to learn statistics, machine learning, and other new methods and technologies, and this book briefly sketch them but does not try to teach them in any detail. However, data scientists need to understand the fundamental concepts of information organization, resource description, category design, and classification that are at the heart of this book. Data scientists need to select resources wisely and decide how best to describe them, they need to understand that resource description and categorization can be biased, they need to understand tradeoffs and complements between people and computers, and they need to understand when interpretability of features and organizing principles are more important than a bit more classification accuracy in a machine learning model.

You don’t have to choose one of the big three to go serverless. If your organization is using Kubernetes, there are a number of open-source options to run functions as you might run containers. Or even better, you can run containers as if they were functions. Knative, one of these options, is actually what powers Google Cloud Run. So don’t feel left out if your organization isn’t in the public cloud, or has gone “all-in” on Kubernetes. Running in Kubernetes may already come with its own sets of pros and cons that you may want to consider when going this route if you have other options.