Data Science Model Deployments and Cloud Computing on GCP
Learn to deploy and implement applications at scale using Kubeflow, Spark, and serverless components on Google Cloud
Created by Siddharth Raghunath
Gain practical experience deploying scalable data science models and applications using Google Cloud. Explore serverless computing, workflow orchestration, and machine learning pipelines with real-world tools like Kubeflow and Spark. Build confidence working with cloud services and modern deployment strategies.
Packt | May 2023 | 415 min
What You Will Learn
You will work through hands-on labs and guided exercises that walk you through setting up cloud environments, deploying models, and managing workflows. Each step focuses on applying concepts directly within Google Cloud, so you build practical skills by actually using the tools and services covered.
Key Features
- Deploy scalable applications using App Engine, Cloud Functions, and Cloud Run
- Orchestrate machine learning workflows with Kubeflow and Vertex AI
- Run and schedule serverless PySpark jobs on Dataproc for efficient data processing
Target Audience
Perfect for data scientists, machine learning engineers, and IT professionals with some cloud and programming experience. If you know Python and SQL basics and want to move from theory to real-world cloud deployments, you will find these skills valuable for advancing your career in data and cloud engineering.
