Deploying Data Science Models on GCP Practice Exam

Deploying Data Science Models on GCP Practice Exam

About the Deploying Data Science Models on GCP Exam

Deploying data science models on Google Cloud Platform (GCP) involves using services like AI Platform, Vertex AI, and Cloud Run to ensure scalability and efficiency. The process typically includes training the model using AI Notebooks or BigQuery ML, containerizing it with Docker, and deploying it using Vertex AI for managed serving or Cloud Run for serverless execution. GCP provides integrated tools for monitoring, version control, and auto-scaling, making it ideal for deploying machine learning models in production with minimal operational overhead.

Skills Required

• Basic Cloud Computing Knowledge – Understanding cloud concepts, virtualization, and cloud deployment models.
• Python & Machine Learning – Familiarity with Python programming and ML frameworks like TensorFlow, Scikit-learn, or PyTorch.
• GCP Fundamentals – Knowledge of core GCP services such as Compute Engine, Cloud Storage, and IAM (Identity and Access Management).
• Docker & Containers – Understanding how to containerize applications using Docker for scalable deployments.
• Kubernetes Basics – Familiarity with Kubernetes and GKE (Google Kubernetes Engine) for managing containerized applications.
• APIs & RESTful Services – Experience in creating and consuming APIs to interact with deployed models.
• CI/CD Pipelines – Knowledge of DevOps practices and CI/CD tools like Cloud Build for automating deployments.

Knowledge Gained

In this course you will gain:

• Knowledge of cloud-based model deployment using services like Vertex AI, AI Platform, and Cloud Run.
• Understanding how to package models into Docker containers for scalable and portable deployment.
• Expertise in automating ML workflows using CI/CD tools like Cloud Build and AI Pipelines.
• Learning to manage containerized models using Google Kubernetes Engine (GKE).
• Exposure to API integration for creating RESTful APIs with Flask, FastAPI, or Google Cloud Endpoints.
• Mastery of auto-scaling, load balancing, and performance monitoring using Cloud Monitoring and Stackdriver.
• Understanding best practices for securing model deployments using IAM, VPCs, and service accounts.
• Knowledge of pricing models and cost-efficient strategies for deploying ML models on GCP.
• Ability to efficiently build, deploy, and manage machine learning models in a production environment.

Who should take the Exam?

• Data Scientists – Professionals looking to deploy and scale their machine learning models in a cloud environment.
• Machine Learning Engineers – Those who want to learn model deployment, containerization, and cloud-based inference.
• Software Developers – Developers interested in integrating ML models into applications using APIs and cloud services.
• Cloud Engineers – Individuals working with GCP who want to specialize in ML model deployment and management.
• DevOps Engineers – Those looking to automate and optimize the deployment of ML models using CI/CD and Kubernetes.
• AI/ML Enthusiasts – Learners who want to gain hands-on experience in deploying AI models on GCP.
• Business Analysts & Data Engineers – Professionals aiming to operationalize data-driven insights using cloud-based ML solutions.

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