The course's general theme is to take simple machine learning models and scale them up in different configurations across multiple cloud environments. Here are the topics covered in this course:

1. Introduction to Building Scalable Model Pipelines : This chapter will motivate the use of Python and discuss the discipline of applied data science, present the data sets, models, and cloud environments used throughout the course, and provide an overview of automated feature engineering.

2. Models as Web Endpoints: This chapter shows how to use web endpoints for consuming data and hosting machine learning models as endpoints using the Flask and Gunicorn libraries. We’ll start with scikit-learn models and also set up a deep learning endpoint with Keras.

3. Models as Serverless Functions: This chapter will build upon the previous chapter and show how to set up model endpoints as serverless functions using AWS Lambda and GCP Cloud Functions.

4. Containers for Reproducible Models: This chapter will show how to use containers for deploying models with Docker. We’ll also explore scaling up with ECS and Kubernetes, and building web applications with Plotly Dash.

5. Workflow Tools for Model Pipelines: This chapter focuses on scheduling automated workflows using Apache Airflow. We’ll set up a model that pulls data from BigQuery, applies a model, and saves the results.

6. PySpark for Batch Modeling: This chapter will introduce readers to PySpark using the community edition of Databricks. We’ll build a batch model pipeline that pulls data from a data lake, generates features, applies a model, and stores the results to a No SQL database.

7. Cloud Dataflow for Batch Modeling: This chapter will introduce the core components of Cloud Dataflow and implement a batch model pipeline for reading data from BigQuery, applying an ML model, and saving the results to Cloud Datastore.

8. Streaming Model Workflows: This chapter will introduce readers to Kafka and PubSub for streaming messages in a cloud environment. After working through this material, readers will learn how to use these message brokers to creating streaming model pipelines with PySpark and Dataflow that provide near real-time predictions.

Create an Echo Function in Lambda

Working with S3 in Lambda

Working with API in Lambda

Working with AWS Container Registry

Thank You