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| Chapter 1 Course Introduction and Prerequisites | |||
| 001. Course Introduction and Section Walkthrough.mp4 | 8.34 MB | ||
| 002. Course Prerequisites.mp4 | 3.29 MB | ||
| Chapter 10 Cloud Scheduler and Application Monitoring | |||
| 001. Introduction to Cloud Scheduler.mp4 | 3.63 MB | ||
| 002. Lab - Cloud Scheduler in Action.mp4 | 19.88 MB | ||
| 003. Lab - Set Up Alerting for Google App Engine Applications.mp4 | 34.26 MB | ||
| 004. Lab - Set Up Alerting for Cloud-Run Applications.mp4 | 28.7 MB | ||
| 005. Lab Assignment - Set Up Alerting for Cloud Function Applications.mp4 | 12.17 MB | ||
| Chapter 2 Modern-Day Cloud Concepts | |||
| 001. Introduction.mp4 | 2.02 MB | ||
| 002. Scalability - Horizontal Versus Vertical Scaling.mp4 | 15.32 MB | ||
| 003. Serverless Versus Servers and Containerization.mp4 | 31.72 MB | ||
| 004. Microservice Architecture.mp4 | 13.5 MB | ||
| 005. Event-Driven Architecture.mp4 | 13.66 MB | ||
| Chapter 3 Get Started with Google Cloud | |||
| 001. Set Up GCP Trial Account.mp4 | 15.16 MB | ||
| 002. Google Cloud CLI Setup.mp4 | 18.37 MB | ||
| 003. Get Comfortable with Basics of gcloud CLI.mp4 | 38.27 MB | ||
| 004. gsutil and Bash Command Basics.mp4 | 37.21 MB | ||
| Chapter 4 Cloud Run - Serverless and Containerized Applications | |||
| 001. Section Introduction.mp4 | 1.26 MB | ||
| 002. Introduction to Dockers.mp4 | 8.48 MB | ||
| 003. Lab - Install Docker Engine.mp4 | 15.36 MB | ||
| 004. Lab - Run Docker Locally.mp4 | 23.68 MB | ||
| 005. Lab - Run and Ship Applications Using the Container Registry.mp4 | 52.03 MB | ||
| 006. Introduction to Cloud Run.mp4 | 3.61 MB | ||
| 007. Lab - Deploy Python Application to Cloud Run.mp4 | 43.96 MB | ||
| 008. Cloud Run Application Scalability Parameters.mp4 | 23.88 MB | ||
| 009. Introduction to Cloud Build.mp4 | 6.71 MB | ||
| 010. Lab - Python Application Deployment Using Cloud Build.mp4 | 38.12 MB | ||
| 011. Lab - Continuous Deployment Using Cloud Build and GitHub.mp4 | 44.42 MB | ||
| Chapter 5 Google App Engine - For Serverless Applications | |||
| 001. Introduction to App Engine.mp4 | 3.77 MB | ||
| 002. App Engine - Different Environments.mp4 | 3.17 MB | ||
| 003. Lab - Deploy Python Application to App Engine - Part 1.mp4 | 17.11 MB | ||
| 004. Lab - Deploy Python Application to App Engine - Part 2.mp4 | 22.16 MB | ||
| 005. Lab - Traffic Splitting in App Engine.mp4 | 14.18 MB | ||
| 006. Lab - Deploy Python - BigQuery Application.mp4 | 26.62 MB | ||
| 007. Caching and Its Use Cases.mp4 | 10.92 MB | ||
| 008. Lab - Implement Caching Mechanism in Python Application - Part 1.mp4 | 43.71 MB | ||
| 009. Lab - Implement Caching Mechanism in Python Application - Part 2.mp4 | 12.39 MB | ||
| 010. Lab - Assignment Implement Caching.mp4 | 12.15 MB | ||
| 011. Lab - Python App Deployment in a Flexible Environment.mp4 | 18.82 MB | ||
| 012. Lab - Scalability and Instance Types in App Engine.mp4 | 36.94 MB | ||
| Chapter 6 Cloud Functions - Serverless and Event-Driven Applications | |||
| 001. Introduction.mp4 | 8.56 MB | ||
| 002. Lab - Deploy Python Application Using Cloud Storage Triggers.mp4 | 52.22 MB | ||
| 003. Lab - Deploy Python Application Using PubSub Triggers.mp4 | 16.87 MB | ||
| 004. Lab - Deploy Python Application Using HTTP Triggers.mp4 | 14.98 MB | ||
| 005. Introduction to Cloud Datastore.mp4 | 6.92 MB | ||
| 006. Overview Product Wishlist Use Case.mp4 | 5.74 MB | ||
| 007. Lab – Use Case Deployment - Part-1.mp4 | 45.03 MB | ||
| 008. Lab – Use Case Deployment - Part-2.mp4 | 24.18 MB | ||
| Chapter 7 Data Science Models with Google App Engine | |||
| 001. Introduction to ML Model Lifecycle.mp4 | 9.25 MB | ||
| 002. Overview - Problem Statement.mp4 | 7.51 MB | ||
| 003. Lab - Deploy Training Code to App Engine.mp4 | 51.43 MB | ||
| 004. Lab - Deploy Model Serving Code to App Engine.mp4 | 26.64 MB | ||
| 005. Overview - New Use Case.mp4 | 5.25 MB | ||
| 006. Lab - Data Validation Using App Engine.mp4 | 38.05 MB | ||
| 007. Lab - Workflow Template Introduction.mp4 | 26.37 MB | ||
| 008. Lab - Final Solution Deployment Using Workflow and App Engine.mp4 | 60.07 MB | ||
| Chapter 8 Dataproc Serverless PySpark | |||
| 001. Introduction.mp4 | 7.86 MB | ||
| 002. PySpark Serverless Autoscaling Properties.mp4 | 6.83 MB | ||
| 003. Persistent History Cluster.mp4 | 28.33 MB | ||
| 004. Lab - Develop and Submit PySpark Job.mp4 | 35.26 MB | ||
| 005. Lab - Monitoring and Spark UI.mp4 | 18.35 MB | ||
| 006. Introduction to Airflow.mp4 | 15.07 MB | ||
| 007. Lab - Airflow with Serverless PySpark.mp4 | 53.06 MB | ||
| 008. Wrap Up.mp4 | 4.8 MB | ||
| Chapter 9 Vertex AI - Machine Learning Framework | |||
| 001. Introduction.mp4 | 6.45 MB | ||
| 002. Overview – Vertex AI UI.mp4 | 7.35 MB | ||
| 003. Lab - Custom Model Training Using Web Console.mp4 | 56.92 MB | ||
| 004. Lab - Custom Model Training Using SDK and Model Registries.mp4 | 41.47 MB | ||
| 005. Lab - Model Endpoint Deployment.mp4 | 7.73 MB | ||
| 006. Lab - Model Training Flow Using Python SDK.mp4 | 16.08 MB | ||
| 007. Lab - Model Deployment Flow Using Python SDK.mp4 | 60.28 MB | ||
| 008. Lab - Model Serving Using Endpoint with Python SDK.mp4 | 34.03 MB | ||
| 009. Introduction to Kubeflow.mp4 | 12.89 MB | ||
| 010. Lab - Code Walkthrough Using Kubeflow and Python.mp4 | 40.29 MB | ||
| 011. Lab - Pipeline Execution in Kubeflow.mp4 | 30.17 MB | ||
| 012. Lab - Final Pipeline Visualization Using Vertex UI and Walkthrough.mp4 | 11.17 MB | ||
| 013. Lab - Add Model Evaluation Step in Kubeflow before Deployment.mp4 | 35.94 MB | ||
| 014. Lab - Reusing Configuration Files for Pipeline Execution and Training.mp4 | 27.78 MB | ||
| 015. Lab - Assignment Use Case - Fetch Data from BigQuery.mp4 | 7.23 MB | ||
| 016. Wrap Up.mp4 | 6.15 MB | ||
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PacktPub - Data Science Model Deployments and Cloud Computing on GCP [Video] [FCO]
About this video
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.
Instructor(s)
Siddharth Raghunath
Siddharth Raghunath is a business-oriented engineering manager with a vast experience in the field of software development, distributed processing, and cloud data engineering. He has worked on different cloud platforms such as AWS and GCP as well as on-premise Hadoop clusters. He conducts seminars on distributed processing using Spark, real-time streaming and analytics, and best practices for ETL and data governance. He is passionate about coding and building optimal data pipelines for robust data processing and streaming solutions.
General Info:
Author(s): Siddharth Raghunath
Language: English
Released: May 2023
Videos Duration: 6h 55m
ISBN: 9781805120438
Course Source: https://www./product/data-science-model-deployments-and-cloud-computing-on-gcp-video/9781805120438
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