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 Lambda does not currently support a file for defining dependencies.

For defining dependencies when writing functions with the inline code editor, which file is used in AWS Lambda?

For defining dependencies when writing functions with the inline code editor, which file is used in AWS Lambda?

Each bucket name on AWS must be ____________. 

Each bucket name on AWS must be ____________.

Which command shows the folders and files in the s3 bucket named `dsp-model`?

Which command shows the folders and files in the s3 bucket named <code>dsp-model</code>?

In order to host the Lambda function so that other services can interact with the function, we’ll need to define a(n) ____________.

In order to host the Lambda function so that other services can interact with the function, we’ll need to define a(n) ____________.

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 - Lambda Functions