Data Science Model Deployments and Cloud Computing on GCP

Google Cloud platform is one of the most rapidly growing cloud providers in the market today, making it an essential skill for aspiring cloud engineers and data scientists. This comprehensive course covers all major serverless components on GCP, providing in-depth implementation of machine learning pipelines using Vertex AI with Kubeflow, and Serverless PySpark using Dataproc, App Engine, and Cloud Run.

The course offers hands-on experience using GCP services such as Cloud Functions, Cloud Run, Google App Engine, and Vertex AI for custom model training and development, Kubeflow for workflow orchestration, and Dataproc Serverless for PySpark batch jobs.

The course starts with modern-day cloud concepts, followed by GCP trial account setup and Google Cloud CLI setup. You will then look at Cloud Run for serverless and containerized applications, and Google App Engine for serverless applications. Next, you will study cloud functions for serverless and event-driven applications. After that, you will look at data science models with Google App Engine and Dataproc Serverless PySpark. Finally, you will explore Vertex AI for the machine learning framework, and cloud scheduler and application monitoring.

By the end of the course, you will be confident in deploying and implementing applications at scale using Kubeflow, Spark, and serverless components on Google Cloud.

Type

video

Category

Cloud & Networking

publication date

2023-05-30

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

duration

415

key features

Learn from an easy-to-understand and step-by-step course, along with resource materials * Highly practical explanations and lab exercises to help you grasp the most out of the course * Design and deploy Python applications across various services, using gcloud command-line interface

approach

This course offers a unique approach to learning by combining theory with practical exercises. The step-by-step course structure makes it easy to understand and follow, with lab exercises and practical content.

This course also includes an in-depth, hands-on lab, is designed to help learners understand cloud concepts in a practical manner, and offers a well-balanced approach to learning.

audience

This intermediate course is designed for those who aspire to become data scientists and machine learning engineers, data engineers, architects, and anyone with a decent exposure in IT looking to start their cloud journey. The course is ideally suited for individuals who possess a fair idea of how the cloud works and have prior experience in basic programming using Python and SQL.

A tech background with basic fundamentals and basic exposure to programming languages such as Python and SQL along with the Bash command line will further help individuals fast-track their learning.

meta description

A complete and practice-friendly course that covers both data science and data engineering aspects. The course delves into serverless components on the cloud, including microservices and event-driven architecture, and explores Vertex AI.

short description

Are you interested in learning and deploying applications at scale using Google Cloud platform? Do you lack hands-on exposure when it comes to deploying applications and seeing them in action?

Then this course is for you.

You will also learn microservices and event-driven architectures with real-world use case implementations.

subtitle

Learn to deploy and implement applications at scale using Kubeflow, Spark, and serverless components on Google Cloud

keywords

Data Science, Model deployments, Cloud Computing, Google Cloud Platform, Kubeflow, Spark, Serverless, Google App Engine, Cloud Functions, Cloud Run, datastore, Microservice Architecture, Event Driven Architecture, machine learning workflows, Dataproc, Airflow/Composer, Vertex AI, Cloud Scheduler

Product ISBN

9781805120438