Machine Learning on Oracle Cloud
Mastery of Machine Learning lifecycle on Oracle Cloud
Published on 5. September 2025
Book
Paperback/Softback
306 pages
978-1-80323-976-7 (ISBN)
Description
Master Machine Learning L lifecycle management with Oracle Cloud and you will be ready to implement ML driven applications.
Key Features
How to decide to make a decision about when to retrain
How to observe the drifting patterns in ML models
How to build cloud networks to support the relevant services and infrastructure
Book DescriptionUnlock the full potential of your ML models with Oracle Cloud data science services. This guide provides a hands-on approach to managing the entire ML lifecycle, from creation to deployment and optimization. Master the art of deployment, serving, and monitoring infrastructure to gain a competitive edge and differentiate yourself from other developers. Learn to easily create, train, test, deploy, monitor and optimize ML models with step-by-step explanations, practical examples and self-assessment questions. With Oracle Cloud, you'll be able to build ML systems from scratch and be up-and-running 24/7 for your ML-driven applications without worrying about scalability. Discover the best storage options for your ML systems, use OCI DS notebook service and create custom conda environments for your projects. Learn to do distributed training with Spark clusters, extract model artifacts and deploy them in a highly scalable infrastructure. Monitor your models using Oracle Cloud services and build cloud networks to go to market with your ML-driven applications quickly. By the end of this guide, you'll be a master of ML lifecycle management with Oracle Cloud and ready to implement ML-driven applications.What you will learn
Choosing the appropriate data storage for your ML systems
Selecting the most suitable ingestion method for your ML systems
Utilizing the collaborative notebook service to create, train, test, and fine-tune your models
Setting up a custom conda environment for the notebook service
Exporting the ML model artifacts
Implementing distributed training using Spark
Deploying the model artifacts in a scalable infrastructure
Monitoring and scaling the serving infrastructure to ensure optimal performance of your models
Who this book is forMachine Learning Engineers, Data scientists and machine learning developers who need practical guidelines to master machine learning lifecycle. From data exploration to model deployment and monitoring.
Companies - customers of Oracle or teams within Oracle who need practical guidelines on how to use Oracle Cloud's services to build ML driven intelligent applications.
Key Features
How to decide to make a decision about when to retrain
How to observe the drifting patterns in ML models
How to build cloud networks to support the relevant services and infrastructure
Book DescriptionUnlock the full potential of your ML models with Oracle Cloud data science services. This guide provides a hands-on approach to managing the entire ML lifecycle, from creation to deployment and optimization. Master the art of deployment, serving, and monitoring infrastructure to gain a competitive edge and differentiate yourself from other developers. Learn to easily create, train, test, deploy, monitor and optimize ML models with step-by-step explanations, practical examples and self-assessment questions. With Oracle Cloud, you'll be able to build ML systems from scratch and be up-and-running 24/7 for your ML-driven applications without worrying about scalability. Discover the best storage options for your ML systems, use OCI DS notebook service and create custom conda environments for your projects. Learn to do distributed training with Spark clusters, extract model artifacts and deploy them in a highly scalable infrastructure. Monitor your models using Oracle Cloud services and build cloud networks to go to market with your ML-driven applications quickly. By the end of this guide, you'll be a master of ML lifecycle management with Oracle Cloud and ready to implement ML-driven applications.What you will learn
Choosing the appropriate data storage for your ML systems
Selecting the most suitable ingestion method for your ML systems
Utilizing the collaborative notebook service to create, train, test, and fine-tune your models
Setting up a custom conda environment for the notebook service
Exporting the ML model artifacts
Implementing distributed training using Spark
Deploying the model artifacts in a scalable infrastructure
Monitoring and scaling the serving infrastructure to ensure optimal performance of your models
Who this book is forMachine Learning Engineers, Data scientists and machine learning developers who need practical guidelines to master machine learning lifecycle. From data exploration to model deployment and monitoring.
Companies - customers of Oracle or teams within Oracle who need practical guidelines on how to use Oracle Cloud's services to build ML driven intelligent applications.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
ISBN-13
978-1-80323-976-7 (9781803239767)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
Tural Gulmammadov has been leading a group of data scientists and machine learning engineers at Oracle to tackle applied machine learning problems from various industries. He is dedicated to and motivated by the applications of graph theory and discrete mathematics in machine learning over distributed computational environments. He is a cognitive science, statistics, and psychology enthusiast, as well as a chess player, painter, seasonal horse rider, and paddler.
Content
Table of Contents
Introduction to Machine Learning
Machine Learning Lifecycle
Components of Machine Learning
Introduction to Oracle Cloud services
Data Storage
Building, training, and testing ML models
Managing and versioning of the ML model artifacts
Deployment of ML models
Monitoring of ML models
Data lakes vs Data warehouses
Distributed training with Spark cluster
Feature management
Introduction to Machine Learning
Machine Learning Lifecycle
Components of Machine Learning
Introduction to Oracle Cloud services
Data Storage
Building, training, and testing ML models
Managing and versioning of the ML model artifacts
Deployment of ML models
Monitoring of ML models
Data lakes vs Data warehouses
Distributed training with Spark cluster
Feature management