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Data Science Model Deployments and Cloud Computing on GCP
English | Tutorial | Size: 5.1 GB
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 snce 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.
1. Course Introduction and PRerequisites
2. Modern-Day Cloud Concepts
3. Get Started with Google Cloud
4. Cloud run – Serverless and Containerized Applications
5. Google App Engine – For Serverless Applications
6. Cloud Functions – Serverless and Event-Driven Applications
7. Data Science Models with Google App Engine
8. Dataproc Serverless PySpark
9. Vertex AI – Machine Learning Framework
10. Cloud Scheduler and Application Monitoring
