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 FeaturesHow to decide to make a decision about when to retrainHow to observe the drifting patterns in ML modelsHow to build cloud networks to support the relevant services and infrastructureBook 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 learnChoosing the appropriate data storage for your ML systemsSelecting the most suitable ingestion method for your ML systemsUtilizing the collaborative notebook service to create, train, test, and fine-tune your modelsSetting up a custom conda environment for the notebook serviceExporting the ML model artifactsImplementing distributed training using SparkDeploying the model artifacts in a scalable infrastructureMonitoring and scaling the serving infrastructure to ensure optimal performance of your modelsWho 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 ContentsIntroduction to Machine LearningMachine Learning LifecycleComponents of Machine LearningIntroduction to Oracle Cloud servicesData StorageBuilding, training, and testing ML modelsManaging and versioning of the ML model artifactsDeployment of ML modelsMonitoring of ML modelsData lakes vs Data warehousesDistributed training with Spark clusterFeature management