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

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.

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

Prototype Models

Predictive models