Deploying Data Science Models on GCP Online Course

Deploying Data Science Models on GCP Online Course

This comprehensive course teaches Google Cloud Platform (GCP) skills for aspiring cloud engineers and data scientists. You’ll gain hands-on experience with serverless components like Cloud Functions, Cloud Run, and App Engine, as well as machine learning pipelines using Vertex AI with Kubeflow and Serverless PySpark with Dataproc. The course covers cloud fundamentals, GCP setup, serverless application deployment, event-driven functions, data science models, workflow orchestration, and application monitoring. By the end, you’ll be able to confidently deploy and manage scalable applications using GCP’s serverless and ML tools.

Who should take this Course?

The Deploying Data Science Models on GCP Online Course is ideal for data scientists, machine learning engineers, and software developers who want to learn how to deploy and manage AI/ML models on Google Cloud Platform. It is also valuable for students, IT professionals, and researchers looking to gain hands-on experience in cloud-based model deployment, scalability, and monitoring to make their data science solutions production-ready.

What you will learn

• Deploy serverless applications using Google App Engine, Cloud Functions, and Cloud Run
• Learn how to use datastore (NoSQL database) in realistic use cases
• Understand microservice and event-driven architecture with practical examples
• Deploying production-level machine learning workflows on cloud
• Use Kubeflow for machine learning orchestration using Python
• Deploy Serverless PySpark Jobs to Dataproc Serverless and schedule them using Airflow/Composer

Course Outline 

Course Introduction and Prerequisites

• Course Introduction and Section Walkthrough
• Course Prerequisites

Modern-Day Cloud Concepts

• Introduction
• Scalability - Horizontal Versus Vertical Scaling
• Serverless Versus Servers and Containerization
• Microservice Architecture
• Event-Driven Architecture

Get Started with Google Cloud

• Set Up GCP Trial Account
• Google Cloud CLI Setup
• Get Comfortable with Basics of gcloud CLI
• gsutil and Bash Command Basics

Cloud Run - Serverless and Containerized Applications

• Section Introduction
• Introduction to Dockers
• Lab - Install Docker Engine
• Lab - Run Docker Locally
• Lab - Run and Ship Applications Using the Container Registry
• Introduction to Cloud Run
• Lab - Deploy Python Application to Cloud Run
• Cloud Run Application Scalability Parameters
• Introduction to Cloud Build
• Lab - Python Application Deployment Using Cloud Build
• Lab - Continuous Deployment Using Cloud Build and GitHub

Google App Engine - For Serverless Applications

• Introduction to App Engine
• App Engine - Different Environments
• Lab - Deploy Python Application to App Engine - Part 1
• Lab - Deploy Python Application to App Engine - Part 2
• Lab - Traffic Splitting in App Engine
• Lab - Deploy Python - BigQuery Application
• Caching and Its Use Cases
• Lab - Implement Caching Mechanism in Python Application - Part 1
• Lab - Implement Caching Mechanism in Python Application - Part 2
• Lab - Assignment Implement Caching
• Lab - Python App Deployment in a Flexible Environment
• Lab - Scalability and Instance Types in App Engine

Cloud Functions - Serverless and Event-Driven Applications

• Introduction
• Lab - Deploy Python Application Using Cloud Storage Triggers
• Lab - Deploy Python Application Using Pub/Sub Triggers
• Lab - Deploy Python Application Using HTTP Triggers
• Introduction to Cloud Datastore
• Overview Product Wishlist Use Case
• Lab – Use Case Deployment - Part-1
• Lab – Use Case Deployment - Part-2

Data Science Models with Google App Engine

• Introduction to ML Model Lifecycle
• Overview - Problem Statement
• Lab - Deploy Training Code to App Engine
• Lab - Deploy Model Serving Code to App Engine
• Overview - New Use Case
• Lab - Data Validation Using App Engine
• Lab - Workflow Template Introduction
• Lab - Final Solution Deployment Using Workflow and App Engine

Dataproc Serverless PySpark

• Introduction
• PySpark Serverless Autoscaling Properties
• Persistent History Cluster
• Lab - Develop and Submit PySpark Job
• Lab - Monitoring and Spark UI
• Introduction to Airflow
• Lab - Airflow with Serverless PySpark
• Wrap Up

Vertex AI - Machine Learning Framework

• Introduction
• Overview – Vertex AI UI
• Lab - Custom Model Training Using Web Console
• Lab - Custom Model Training Using SDK and Model Registries
• Lab - Model Endpoint Deployment
• Lab - Model Training Flow Using Python SDK
• Lab - Model Deployment Flow Using Python SDK
• Lab - Model Serving Using Endpoint with Python SDK
• Introduction to Kubeflow
• Lab - Code Walkthrough Using Kubeflow and Python
• Lab - Pipeline Execution in Kubeflow
• Lab - Final Pipeline Visualization Using Vertex UI and Walkthrough
• Lab - Add Model Evaluation Step in Kubeflow before Deployment
• Lab - Reusing Configuration Files for Pipeline Execution and Training
• Lab - Assignment Use Case - Fetch Data from BigQuery
• Wrap Up

Cloud Scheduler and Application Monitoring

• Introduction to Cloud Scheduler
• Lab - Cloud Scheduler in Action
• Lab - Set Up Alerting for Google App Engine Applications
• Lab - Set Up Alerting for Cloud-Run Applications
• Lab Assignment - Set Up Alerting for Cloud Function Applications