# 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! --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/overview.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.