Learn about different cloud environments and tools for building scalable data and model pipelines.

Become a Data Scientist

In this module, we’ll cover different cloud environments and tools for building scalable data and model pipelines. We’ll learn the different data sets and types of models that will be used heavily in everyday production. Throughout the module, we’ll have plenty of exercises and challenges to get us comfortable working with the diverse toolset. Lastly, we’ll explore streaming model workflows that are crucial for building real-time data pipelines.
By the end of this module, we'll have a better understanding of how to build scalable machine learning pipelines in a cloud environment.

Data Science in Production: Building Scalable Model Pipelines

## Overview
While the standard deployment of a model as a web service is an API that you can
call programmatically, it's often useful to expose models as interactive web applications.

For example, we might want to build an application where there is a UI for specifying different
inputs to a model, and the UI reacts to changes made by the user. While Flask can be used to build web pages that react to user input, there are libraries built on top of Flask that provide higher-level abstractions for building web applications with the Python language. 




We'll create a simple Dash application that provides a UI for interacting with a model. The application layout will contain three text boxes, where two are for user inputs and the third shows the output of the model. We'll create a file called `dash_app.py` and start by specifying the libraries to import. 

# Overview
While the standard deployment of a model as a web service is an API that you can
call programmatically, it's often useful to expose models as interactive web applications.

For example, we might want to build an application where there is a UI for specifying different
inputs to a model, and the UI reacts to changes made by the user. While Flask can be used to build web pages that react to user input, there are libraries built on top of Flask that provide higher-level abstractions for building web applications with the Python language. 




Building interactive web services using Dash.

Introduction to Building Scalable Model Pipelines

Models as Web Endpoints

Models as Serverless Functions

Containers for Reproducible Models

Workflow Tools for Model Pipelines

PySpark for Batch Pipelines

Cloud Dataflow for Batch Modeling

Streaming Model Workflows

Conclusion

Interactive Web Services with Dash

Overview