type

project_id

private_key_id

private_key

client_email

client_id

auth_uri

token_uri

auth_provider_x509_cert_url

client_x509_cert_url

kaggle_username

kaggle_key

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

PROJECT_ID

aws_access_key_id

aws_secret_access_key

aws_account_id

jupyter-hello

code_widget_advanced

spa_job

spa_job_model_persistence

spa_job_gcloud

spa_job_gcloud_authen

spa_gcloudAuthen_sklearnWorkflows

spa_job_apacheKafka

spa_job_jupyter_gcloudAuth_pubsub

code_widget_basic

spa_job_aws

spa_job_gcloudAuth_GCP_Kubernetes

spa_job_jupyter

spa_job_gcloud_authen_model

spa_job_aws_model

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spa_job_Airflow

spa_job_aws_kaggle_nhl_data

spa_job_kaggle_nhl_data_jupyter

airflow_liveApp

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test-athar

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spa_job_aws_echo_service-copy

spa_job_aws_kaggle_nhl_data-copy

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 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