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

Dataflow is a powerful data pipeline tool that enables data scientists to rapidly prototype and deploy data processing workflows that can apply machine learning algorithms. The framework provides a few basic operations that can be chained together to define complex graphs of workflows. One of the key features of Dataflow is that it builds upon an open-source library called Apache Beam, which enables the workflows to be portable to other cloud environments.


Conclusion to 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

Conclusion : Cloud Dataflow for Batch Modeling