Data Science Model Deployments and Cloud Computing on GCP

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Description of 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.
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