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

## AWS and GCP
AWS also provides an ecosystem for serverless functions called Lambda. AWS Lambda is useful for gluing different components within an AWS deployment together since it supports a rich set of triggers for function inputs and outputs. While Lambda does provide a powerful tool for building data pipelines, the current Python development environment is a bit clunkier than GCP. 

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<img src="/api/collection/10370001/6068402050301952/page/6319225321816064/image/6514740818870272" width="200">
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# AWS and GCP
AWS also provides an ecosystem for serverless functions called Lambda. AWS Lambda is useful for gluing different components within an AWS deployment together since it supports a rich set of triggers for function inputs and outputs. While Lambda does provide a powerful tool for building data pipelines, the current Python development environment is a bit clunkier than GCP. 

<center>
<img src="/api/collection/10370001/6068402050301952/page/6319225321816064/image/6514740818870272" width="200">
</center>





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

Lambda Functions (AWS)

AWS and GCP