AI projects run on the data underneath them
CloudServus helps mid-market IT teams unify data in Microsoft Fabric and OneLake, then build machine learning and AI workloads on top of it.
Talk to an Expert →Data readiness comes before AI readiness
Most AI initiatives stall before the model ever gets built, buried under scattered data spread across disconnected systems. Predictions built on that foundation come back wrong, and teams spend more time reconciling sources than improving the model.
Microsoft Fabric fixes this at the foundation. OneLake gives every workload in Fabric, from data engineering to Power BI to data science, a single logical data lake to read from and write to. No duplicate copies, no separate pipeline feeding every tool.
What Fabric gives your AI initiatives
Unify your data
- OneLake is the single logical lake behind every Fabric workload, so data engineering, BI, and data science teams work from the same governed source.
- Shortcuts connect to data in other systems or tenants without copying it, cutting the ETL work that usually precedes an AI project.
Build and manage models
- Notebooks with Spark, MLflow experiment tracking, and Data Wrangler cover the data science workflow end to end, from cleaning to training to model scoring, directly on governed OneLake data.
- Direct Lake mode serves predictions straight into Power BI reports with no separate data load or refresh.
Put insights in front of business users
- Copilot in Fabric speeds up data prep and analysis inside the platform.
- Data agents in Microsoft 365 Copilot let business users chat with governed Fabric data from Teams and Excel, no separate tool required.
Govern what you build
- Built-in security, sensitivity labels, and governance controls travel with the data, so AI outputs inherit the same protections as the source.
Where data science work happens in Fabric
CloudServus builds the Data Science workload inside Fabric to fit how your team already works: notebooks for exploration and model training, MLflow for tracking experiments and versions, and the Fabric model registry for handling scored models without side systems to maintain.
Everything reads from and writes to OneLake, so a model trained this week can serve predictions to a live Power BI report next week. No export step, no stale copy to babysit.
Fabric is the data layer. Microsoft Foundry is the AI layer.
Fabric handles the data side of AI: unifying it, governing it, and preparing it for use. Building AI applications and agents on top of that data happens in Microsoft Foundry, the platform formerly known as Azure AI Foundry, Microsoft's environment for building and deploying AI apps and agents.
CloudServus connects the two, so the models and agents you build draw on the same governed OneLake data your BI reports already trust instead of a separate, disconnected source.
Get your data ready for AI
Talk to CloudServus about unifying your data in Microsoft Fabric and OneLake, so your AI and machine learning initiatives start from a foundation you can trust.
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