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Data Science in Production: Building Scalable Model Pipelines

Advanced

110 Lessons

Certificate of Completion

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

4 Cloud Labs

53 Playgrounds

16 Quizzes

Course Overview

Course Content

Apply Your Skills

Recommendations

Course Overview

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...Show More

Course Content

Introduction to Building Scalable Model Pipelines

18 Lessons

Course Overview

Applied Data Science

Python for Scalable Compute

Cloud Environments

Coding Environments

Quiz - Data Science Preliminary Concepts

🛑 Important Note!

Introduction to Datasets

- BigQuery to Pandas

- Kaggle to Pandas

Quiz - Importing Datasets

Prototype Models

- Linear Regression

- Logistic Regression

- Keras Regression

Automated Feature Engineering

Quiz - Prototype Modeling

Conclusion : Data science models, tools and environments

Models as Web Endpoints

12 Lessons

Introduction to Models as Web Endpoints

Quiz - Web Services and Persistent Models

Deploying a Web Endpoint

- Gunicorn

- Heroku

Interactive Web Services with Dash

Quiz - Web Deployments

Conclusion : Models as Web Endpoints

Models as Serverless Functions

17 Lessons

Introduction to Models as Serverless Functions

Managed Services

Cloud Functions (GCP)

- Echo Service

- Cloud Storage (GCS)

Quiz - Managed Services

Lambda Functions (AWS)

- Echo Function

- Simple Storage Service (S3)

- Model Function

- API Gateway

Quiz - Lambda Functions

Conclusion

Create an Echo Function in Lambda

Cloud Lab

Working with S3 in Lambda

Cloud Lab

Working with API in Lambda

Cloud Lab

Containers for Reproducible Models

9 Lessons

Introduction to Containers as Reproducible Models

Docker

Orchestration

- AWS Container Registry (ECR)

- AWS Container Service (ECS)

- Load Balancing

Kubernetes on GCP

Quiz - Containers

Conclusion: Containers as Reproducible Models

Working with AWS Container Registry

Cloud Lab

Workflow Tools for Model Pipelines

10 Lessons

Introduction to Workflow Tools for Model Pipelines

Sklearn Workflow

Cron

- Cloud Cron

Quiz - Workflows and Scheduling

Workflow Tools

- Apache Airflow

- Managed Airflow

Quiz - Workflow Tools

Conclusion : Workflow Tools for Model Pipelines

10.

PySpark for Batch Pipelines

25 Lessons

Introduction to PySpark for Batch Pipelines

Spark Environments

- Spark Clusters

- Databricks Community Edition

Staging Data

Quiz - Spark Environments

A PySpark Primer

- Persisting Dataframes

- Converting Dataframes

Distributed Deep Learning

Distributed Feature Engineering

Quiz - Machine Learning in PySpark

Quiz - GCP Model Pipeline

Conclusion : Working tools for model pipelines

11.

Cloud Dataflow for Batch Modeling

8 Lessons

Introduction to Cloud Dataflow and Batch Modeling

Quiz - Dataflow and Batch Modeling

Conclusion : Cloud Dataflow for Batch Modeling

12.

Streaming Model Workflows

10 Lessons

Introduction to Streaming Model Workflows

Spark Streaming

- Apache Kafka

- Sklearn Streaming 1

- Sklearn Streaming 2

Dataflow Streaming

- PubSub

- Natality Streaming

Quiz - Streaming Model Workflows

Conclusion : Streaming model workflows

13.

Course Conclusion

1 Lessons

Thank You

Course Author

Ben Weber

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