# Overview {% embed url="" %} Overview - Infrastructure and Tooling {% endembed %} ## Summary * Google's seminal paper "Machine Learning: The High-Interest Credit Card of Technical Debt" states that if we look at the whole machine learning system, the actual modeling code is very small. There are a lot of other code around it that configure the system, extract the data/features, test the model performance, manage processes/resources, and serve/deploy the model. * The **data component:** * Data Storage - How to store the data? * Data Workflows - How to process the data? * Data Labeling - How to label the data? * Data Versioning - How to version the data? * The **development component:** * Software Engineering - How to choose the proper engineering tools? * Frameworks - How to choose the right deep learning frameworks? * Distributed Training - How to train the models in a distributed fashion? * Resource Management - How to provision and mange distributed GPUs? * Experiment Management - How to manage and store model experiments? * Hyper-parameter Tuning - How to tune model hyper-parameters? * The **deployment component** * Continuous Integration and Testing - How to not break things as models are updated? * Web - How to deploy models to web services? * Hardware and Mobile - How to deploy models to embedded and mobile systems? * Interchange - How to deploy models across systems? * Monitoring - How to monitor model predictions? * All-In-One: There are solutions that handle all of these components!