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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...
| Clasificación: | Libro Electrónico |
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| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
Packt Publishing,
2023.
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| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | 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 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. About The Author 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. |
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| Notas: | "Published in May 2023." |
| Descripción Física: | 1 online resource (1 video file (6 hr., 57 min.)) : sound, color. |
| Tiempo de Juego: | 06:57:00 |
| ISBN: | 9781805120438 1805120433 |