# Kaggle This is day one of a three day event, held during [Kaggle CareerCon 2019](https://www.kaggle.com/careercon2019). Each day we’ll learn about a new part of developing an API and put it into practice. By day three, you’ll have written and deployed an API of your very own! * [**Day 1: The Basics of Rest APIs – What They Are and How to Design One.**](https://www.kaggle.com/rtatman/careercon-intro-to-apis) By the end of this day you’ll have written the OpenAPI specification for your API. * [**Day 2: How to Make an API for an Existing Python Machine Learning Project.** ](https://www.kaggle.com/rtatman/careercon-making-an-app-from-your-modeling-code)By the end of this day, you’ll have a Flask app that you can use to serve your model. * [**Day 3: How to deploy your API on your choice of services – Heroku or Google Cloud.**](https://www.kaggle.com/rtatman/careercon-deploying-apis-on-heroku-appengine/) By the end of this day, you’ll have deployed your model and will be able to actually use your API! (Note that if you're planning on using Google Cloud, you’ll need a credit card to create a billing account. If you don’t have access to a credit card you can still use Heroku.) --- # 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://stephanosterburg.gitbook.io/scrapbook/data-science/kaggle-1.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.