With our applications re-architected for the cloud and our organization functionally re-aligned, we could now focus Phase 3 efforts on optimizing and maturing our Azure cloud operating model for maximum performance, efficiency, and business impact.
The key objectives during this final phase were:
Advanced Cloud Architecture
Multi-cloud deployment models with abstraction
Implementation of modern architecture patterns
Cost optimization and cloud consumption modeling
Fully automated deployment pipelines with GitOps
Cloud Platform Engineering
Enterprise-scale cloud resource governance
Robust identity and access management controls
Self-service provisioning and infrastructure catalogs
Compliance and regulatory automation
Cloud Operations Excellence
SRE practices like chaos engineering and game days
Unified monitoring, logging, and telemetry
AI Ops for intelligent monitoring and remediation
Mature incident response and postmortem processes
Modern Data Paradigms
Treating data as a product with data meshes
Data fabric architecture and analytics pipelines
Automated data governance and lineage
Advanced AI/ML operations (MLOps)
FinOps Cost Optimization
Cloud financial operations and accountability
Tagging standards and cost attribution
Consumption monitoring and forecasting
Application rightsizing and workload rebalancing
In many ways, we were now treating our cloud itself as the product - with dedicated teams focused on reliability, scalability, cost efficiency, and continuous improvement. Just as our applications embraced DevOps philosophies, our cloud platform services adopted similar SRE-inspired practices.
On the engineering front, we went all-in on infrastructure-as-code, automated deployments using Git repositories, and progressive delivery strategies. Our cloud networking, security, and governance controls became codified and versionable. We utilized Azure Blueprints, ARM templates, and policy tooling to enforce resource consistency at scale.
For runtime operations, we now had end-to-end observability through unified pipelines for monitoring, logging, distributed tracing, and AI-assisted event correlation. Our incident response playbooks become battle-tested, with chaos engineering games pushing our fault tolerance to the limits.
Our data paradigms evolved to treat data as a distributed self-served product, with data meshes and a consolidated data fabric servicing advanced analytics, reporting, machine learning, and real-time decisioning use cases. MLOps procedures helped us industrialize the ML model lifecycle.
And of course, our FinOps program became deeply embedded, with cost accountability, tagging rigor, forecasting models, and continuous rightsizing of workloads to optimize our cloud spending.
None of these capabilities existed in our old data center world - but by taking a product-minded approach to all aspects of our Azure operating model, we reached new levels of reliability, agility, and enterprise effectiveness. Our cloud platform itself became the engine powering our business' competitive edge and differentiating innovation.
Building a world-class cloud operating discipline is never truly "done". But by continuously iterating across architecture, operations, data, and cost optimization verticals, we've established a sustainable foundation for running Azure as a core strategic asset for decades to come.
9/15/2016
Sash Barige
Links
Cloud Strategy
Phase 1
Phase 2
Phase 3
Making it Happen
DevOps Rigor
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