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Gain insights into building scalable data and model pipelines, explore different cloud environments, delve into streaming workflows, and discover essential tools for creating real-time data products.

dsp-new.tar.gz

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The goal of this course is to provide you with a set of tools that can be used to build predictive model services for product teams.

In this course, you’ll start by covering the different cloud environments and tools for building scalable data and model pipelines. You’ll then learn the different data sets and types of models that will be used heavily in everyday production. Throughout the course, you’ll have plenty of exercises and challenges to get you comfortable working with the diverse toolset.

Lastly, you’ll explore streaming model workflows which is crucial for building real-time data pipelines that move data between different components in a cloud environment. 


After working through this course, you will have gained valuable hands-on experience with many of the tools needed to build data products. You will also have a better understanding of how to build scalable machine learning pipelines in a cloud environment.

Data Science in Production: Building Scalable Model Pipelines

Machine learning is one of the most important steps in the pipeline of a data product. We can use predictive models to identify which users are most likely to purchase an item, or which users are most likely to stop using a product. The goal of the following lessons is to present simple versions of predictive models that we’ll later scale up in more complex pipelines. This course will not focus on state-of-the-art models, but instead cover tools that can be applied to a variety of different machine learning algorithms.

Prototype Models

Introduction to Building Scalable Model Pipelines

Models as Web Endpoints

Models as Serverless Functions

Create an Echo Function in Lambda

Working with S3 in Lambda

Working with API in Lambda

Containers for Reproducible Models

Working with AWS Container Registry

Workflow Tools for Model Pipelines

PySpark for Batch Pipelines

Cloud Dataflow for Batch Modeling

Streaming Model Workflows

Course Conclusion

Prototype Models

Predictive models