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

Cron is useful for simple pipelines but runs into challenges when tasks have dependencies on other tasks that can fail.

Cron is useful for simple pipelines but runs into challenges when tasks have dependencies on other tasks that can fail.

In Airflow, DAGs are set up using ___________.

In Airflow, DAGs are set up using ___________.

Cloud Composer is built on top of ___________.

Cloud Composer is built on top of ___________.

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 - Workflow Tools