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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

## Updating models
We've deployed sklearn and Keras models to production using Cloud Functions, but the current implementations of these functions use static model files that will not change over time. It's usually necessary to make changes to models overtime to ensure that the accuracy of the models does not drift too far from expected performance. 

### Approaches
There are few different approaches that we can take to update the model specification that a Cloud Function is using:


1. **Redeploy:** Overwriting the model file on GCS and redeploying the function
will result in the function loading the updated file.
2. **Timeout:** We can add a timeout to the function, where the model is re-downloaded
after a certain threshold of time passes, such as 30 minutes. 
3. **New function:** We can deploy a new function, such as `pred_v2`, and update the URL used by the systems calling the service, or use a load balancer to automate this process. 
4. **Model trigger:** We can add additional triggers to the function to force the function to manually reload the model. 



# Updating models
We've deployed sklearn and Keras models to production using Cloud Functions, but the current implementations of these functions use static model files that will not change over time. It's usually necessary to make changes to models overtime to ensure that the accuracy of the models does not drift too far from expected performance. 

## Approaches
There are few different approaches that we can take to update the model specification that a Cloud Function is using:


1. **Redeploy:** Overwriting the model file on GCS and redeploying the function
will result in the function loading the updated file.
2. **Timeout:** We can add a timeout to the function, where the model is re-downloaded
after a certain threshold of time passes, such as 30 minutes. 
3. **New function:** We can deploy a new function, such as `pred_v2`, and update the URL used by the systems calling the service, or use a load balancer to automate this process. 
4. **Model trigger:** We can add additional triggers to the function to force the function to manually reload the model. 



Updating models to ensure their accuracy. 

- Model Refreshes

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

- Model Refreshes

Updating models