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

spa_job_aws_echo_service

spa_job_Airflow

spa_job_aws_kaggle_nhl_data

spa_job_kaggle_nhl_data_jupyter

airflow_liveApp

spa_job_heroku

test-athar

spa_job_heroku-copy

core-jupyter

core-jupyter-copy

spa-jupyter

jupyter-hello-spa

osama

sklearn_workflow_job

spa_job_model_endpoint_keras

spa_job_aws-copy

spa_job_aws_model-copy

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

Now that we’ve covered loading and transforming data with PySpark, we can use the machine learning libraries in PySpark to build a predictive model.

# MLlib
The core library for building predictive models in PySpark is called MLlib. This library provides a suite of supervised and unsupervised algorithms.

While this library does not have complete coverage of all of the algorithms in sklearn, it provides functionality for the majority of the types of operations needed for data science workflows. In this
section, we'll show you how to apply MLlib to a classification problem and save the outputs from the model application to a data lake. 

games_df = spark.read.csv("s3://dsp-ch6/csv/games-expand.csv", header=True, inferSchema=True)
games_df.createOrReplaceTempView("games_df")

games_df = spark.sql("""
  select *, row_number() over (order by rand()) as user_id
    ,case when rand() > 0.7 then 1 else 0 end as test
  from games_df
""")

## Loading the data
The first step in the pipeline is loading the dataset that we want to use for model training. 

<center>
<img src="/api/collection/10370001/6068402050301952/page/5623215754838016/image/5113252187996160" width="300">
</center>



The snippet above shows how to load the games dataset and append two additional attributes to the loaded dataframe using Spark SQL. The result of running this query is that about $30\%$ of users will be assigned a test value, which we'll use for model application, and each record is assigned a unique user ID, which we'll use when saving the model predictions. 

## Splitting the data
The next step is splitting up the dataset into train and test dataframes. For this pipeline, we’ll use the test dataframe as the dataset for model application, where we’ll predict user behavior. 

An example of splitting up the dataframes using the test column is shown in the snippet below. This should result in roughly $16.1k$ training users and $6.8k$ test users. 

Learn about machine learning libraries in PySpark to build predictive models.


MLlib Batch Pipeline

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

MLlib Batch Pipeline

MLlib