Leveraging AI to Optimize Database Architectures in a Multicloud World [LRN3299].pdf

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1、1|Copyright 2024 Deloitte Development LLC.All rights reserved.Leveraging AI/ML to Optimize Architectures in a Multi-Cloud WorldLRN3299:September 11,2024|4:45 PM PDTO R A C L E C L O U D W O R L D 2 0 2 4what day is it?2|Copyright 2024 Deloitte Development LLC.All rights reserved.Introductions Not al

2、l workloads are“commodity cloud”capable due to on-premises infrastructure capability Establish corporate cloud shapes or patterns to increase application and database agility.Optimizing multi-cloud solutions to reduce cloud costs and improve application agility Machine Learning has many use cases ac

3、ross the industry,as well as projecting services models for all industries Establishing data sources to build on-premises and cloud based data metrics for predicting workload requirementsThings to considerThings to considerObjectives for Workload AssessmentObjectives for the SessionHenry(Hank)Tullis

4、Henry(Hank)TullisMult-Cloud Infrastructure and Data ArchitectDeloitte ConsultingDeloitte ConsultingMark SaltzmanMark SaltzmanConsumer Enterprise ArchitectDeloitte ConsultingDeloitte ConsultingMichelle Michelle MalcherMalcherDirector,Database Product ManagementOracle CorporationOracle CorporationColl

5、ect workload metrics with respect to database environmentsAnalyze workload metrics for performance and classificationsReview and establish standardized patterns reducing the architecture requirementsProject standardized patterns on available cloud shape and service catalogsReview the approach for st

6、rategic workload assessment and benefits of the establishing workload characteristicsDiscuss workload classification and workload projection for cloud deployment strategiesHow is Machine Learning changing the approach for strategic workload assessmentsHow the current strategic workload assessment pr