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

It defines an operation to perform on the first 10 objects in a collection.

It defines an operation to perform on a set of objects in a collection.

It defines an operation to perform on each object in a collection.

It defines the number of objects in a collection.

What does a <code>DoFn</code> step define?

Apache Beam is an open-source library for building data processing workflows using _________, __________ and __________ .

Apache Beam is an open-source library for building data processing workflows using _________, __________ and __________ .

Cloud Datastore is a NoSQL database.

A quiz on cloud Dataflow for batch modeling 

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

Quiz - Dataflow and Batch Modeling