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

In [Cloud Dataflow and Batch Modeling](https://www.educative.io/courses/data-science-in-production-building-scalable-model-pipelines/introduction-to-cloud-dataflow-and-batch-modeling), we explored using Cloud Dataflow to create a batch model pipeline and authored a `DoFn` to apply a sklearn model. Dataflow can also be used to build streaming model pipelines by setting a configuration flag. One of the features of Dataflow is that many components can be reused across batch and streaming workflows, and in the upcoming lessons, we’ll use the same model application class that we defined in the last chapter.

![](/api/collection/10370001/6068402050301952/page/6553952163201024/image/5522047510773760)

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

Dataflow Streaming