Machine Learning on Oracle Cloud: Mastery of Machine Learning lifecycle on Oracle Cloud

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.

Table of Contents:
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

About the Author :
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.