Emerging Tech
Practical guidance on AI, Azure OpenAI, Microsoft Fabric, and the data foundations behind them.
Deploying AI Agents With Microsoft Foundry Agent Service
Most enterprise teams building AI agents on Azure start by asking the wrong question.
FinOps for Azure AI: Controlling Token and Compute Costs
Azure AI spending doesn't behave like other cloud costs. You're not just paying for compute and storage. You're paying per token, per API call, per model.
Detecting Data Drift and Model Drift in Production ML Models
A model that performed well at deployment can quietly degrade for months before anyone notices. The inputs change. The real-world patterns your model learned shift.
Automating Azure ML Pipelines With MLOps
Automating Azure ML pipelines with MLOps is the difference between a model that runs once and a model that stays reliable in production. Most teams can train a model.
Moving Legacy Applications to the Cloud on Azure
Moving legacy applications to the cloud on Azure is one of the most consequential decisions an engineering organization makes.
Turning Enterprise Data Into Decision-Ready Intelligence on Azure
Data intelligence consulting addresses a problem most enterprises can name precisely: the organization has data, plenty of it, but the...
Building an Enterprise AI Strategy on Azure
AI strategy consulting has become one of the most requested conversations Winmill has with technical leaders.
Designing a Microsoft Fabric Architecture for Enterprise Data
Microsoft Fabric architecture decisions made early in a deployment determine whether the platform scales well or accumulates technical debt.
Serverless Kubernetes on Azure Explained
Serverless Kubernetes on Azure describes a model where teams deploy containerized workloads without managing the underlying node infrastructure.
Building a Model Governance Framework on Azure
A model governance framework is what separates enterprise AI that organizations can defend to regulators, auditors, and boards from AI...
Ready to put these ideas to work?
Tell us what you're working on, and we'll bring the right engineers to the conversation. We respond within one business day.