Data Science Model Deployments and Cloud Computing on GCP

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Overview

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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you'll gain hands-on experience with deploying data science models on Google Cloud Platform (GCP) while mastering cloud computing concepts. By the end, you will understand essential cloud tools like Google App Engine, Cloud Functions, and Cloud Run, and you’ll be able to efficiently deploy machine learning models into production environments. You'll also explore how cloud scalability, serverless computing, and containerization impact model deployment, ensuring you can deploy models in various environments seamlessly. You will start by exploring key cloud concepts such as scalability and serverless computing, followed by practical exercises using GCP tools. You'll walk through deploying Python applications, using Docker containers, and setting up continuous deployment pipelines with Cloud Build and GitHub. The course will introduce you to machine learning model lifecycle management and how to use GCP's Vertex AI and Kubeflow for model training and deployment. This course is perfect for data scientists, developers, and cloud enthusiasts looking to apply machine learning models in real-world applications. No advanced cloud experience is required, though basic Python and machine learning knowledge will be beneficial. The course has a hands-on, practical approach to GCP, ensuring you can deploy data science models confidently.

Syllabus

Course Introduction and Prerequisites

In this module, we will introduce the course structure, scope, and learning outcomes. You’ll explore what each section covers and the foundational skills required to maximize your learning. This sets the stage for a seamless learning experience throughout the course.

Modern-Day Cloud Concepts

In this module, we will explore key cloud-native concepts critical to building scalable and resilient applications. You will gain a clear understanding of cloud scalability, architecture paradigms, and when to apply serverless versus container-based solutions. These foundational principles are essential for deploying data-driven systems efficiently in the cloud.

Get Started with Google Cloud

In this module, we will guide you through setting up your GCP environment, including account creation and CLI installation. You'll also gain hands-on experience with gcloud and gsutil, empowering you to navigate and manage cloud resources using terminal-based commands. This is your launchpad for all GCP-related tasks ahead.

Cloud Run - Serverless and Containerized Applications

In this module, we will take a deep dive into containerization with Docker and deploying applications using Cloud Run. You'll work through hands-on labs that show how to run containers, integrate with the Container Registry, and automate deployments with Cloud Build. This prepares you for efficient serverless deployments in real-world scenarios.

Google App Engine - For Serverless Applications

In this module, we will explore Google App Engine and its capabilities for deploying scalable serverless applications. You’ll gain experience deploying Python applications, integrating with BigQuery, and improving efficiency through caching. These skills enable you to build optimized applications tailored for serverless environments.

Cloud Functions - Serverless and Event-Driven Applications

In this module, we will focus on building event-driven applications using Google Cloud Functions. You'll learn to deploy and trigger functions via different GCP services and apply this knowledge in a complete use-case deployment. This helps you create responsive, scalable architectures driven by events.

Data Science Models with Google App Engine

In this module, we will guide you through deploying machine learning models using App Engine. From understanding model lifecycles to solving a fraud detection use case, you’ll learn how to train, validate, and serve models in a production-grade serverless environment.

Dataproc Serverless PySpark

In this module, we will work with Dataproc to run serverless PySpark jobs for large-scale data processing. You'll learn about cluster persistence, monitoring tools, and how to automate workflows using Airflow. This equips you with the tools to manage big data pipelines in a cloud-native way.

Vertex AI - Machine Learning Framework

In this module, we will explore Vertex AI, Google’s fully managed ML platform. You’ll gain hands-on experience with model training, deployment, and pipeline orchestration using both the UI and SDK. This section is essential for productionizing ML workflows on Google Cloud.

Cloud Scheduler and Application Monitoring

In this module, we will explore how to schedule jobs and monitor applications using Google Cloud tools. You’ll learn how to set up alerts for App Engine, Cloud Run, and Cloud Functions to maintain reliability and observability in production systems. This ensures operational excellence for your deployed applications.