Learn how the Everyday Essentials team built a scalable personalized recommendation platform on AWS using a batch-first architecture with Amazon MWAA for orchestration, Amazon SageMaker for training and vector search, and AWS Lake Formation for governed data access, then extended it to real-time with Amazon MemoryDB.
Discovery
Learnings
- auto_storiesStories
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- helpFAQ
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- descriptionPapers
- mailNewsletters
- video_libraryVideos
- timelineTimeline
Guidance
- codeTechnology of APIs
- business_centerBusiness of APIs
- campaignEvangelism of APIs
- gavelGovernance of APIs
- historyHistory of APIs
- account_balancePolitics of APIs
Toolbox
- domainProviders
- apiAPIs
- cloudSolutions
- buildTools
- verifiedStandards
- gavelRegulations
- horizontal_ruleHeaders
- layersOverlays
- extensionExtensions
- schemaSchema
- policyPolicies
- ruleRules