AWS analytics and applied AI
AWS Data and AI
Build AWS data platforms, analytics, ML, and generative AI capabilities with governance, evaluation, and cost visibility.
Trusted data foundations
Storage, ingestion, transformation, cataloging, access patterns, and quality controls are aligned so analytics and AI programs start from reliable data.
Analytics for business decisions
Warehousing, semantic layers, dashboards, cost controls, lineage, and ownership practices help teams use AWS analytics outputs with confidence.
Machine learning and generative AI
Use cases, feature pipelines, model evaluation, prompt design, retrieval strategy, privacy boundaries, and monitoring are defined before AI moves into production.
Production governance
Data and AI initiatives need deployment patterns, role separation, access reviews, incident response, and clear measures of value to remain useful.
How InSkyto helps
Platform choices connected to delivery outcomes
Match Microsoft, AWS, and Google services to governance, identity, data, and adoption requirements.
Use proven patterns for tenant configuration, cloud foundations, access control, automation, and monitoring.
Document ownership, cost, compliance evidence, support boundaries, and improvement routines for each platform.