Data science & machine learning
Microsoft Fabric Featured Partner · Databricks Partner
Platform decisions follow the data foundation, not the other way around
Machine learning engagements land where the data already lives. For Microsoft-anchored data estates, that’s Microsoft Fabric. For clients running advanced ML workloads like production model serving, MLflow lifecycle management, or large-scale training, Databricks is often the better fit.
We deliver across both.
Multi-platform model development and serving on Microsoft Fabric, Azure AI services, or Databricks (MLflow, model serving)
Production-grade MLOps and DataOps practices
Data applications built with tools like Dash and Streamlit
End-to-end data lifecycle management
Our no-obligation workshops are an easy way to get started
Agentic AI Dev Session
Design an AI-powered agent against a real workflow and leave with a deployable MVP.
Operational Data Applications
Build lakehouse-architecture business apps for Monte Carlo simulations, workflow automation, and real-time data workflows.
Ready for smarter workflows?
Why choose Inviso for data science & machine learning
Production discipline. MLOps and DataOps practices that carry models from prototype to serving, not just to a demo.
Multi-platform delivery across Fabric, Azure AI, and Databricks, matched to where your data actually lives.
Low-friction entry. Free workshops that produce a deployable MVP, not a slide deck.
FAQs
Fabric or Databricks for machine learning?
It depends on the data foundation. Microsoft-anchored estates usually run ML in Fabric. Advanced workloads like production model serving, MLflow lifecycle management, and large-scale training often fit Databricks better. We deliver across both and decide after the foundation is understood.
What do MLOps and DataOps mean in practice?
The engineering practices that move models from a data scientist’s notebook into production and keep them there: versioning, automated pipelines, monitoring, retraining, and lifecycle management. It’s the difference between a model that demos and a model that ships.
What happens in the free workshops?
The Agentic AI Dev Session designs an AI-powered agent against one of your real workflows; you leave with a deployable MVP. The Operational Data Applications track builds lakehouse-architecture business apps for scenarios like Monte Carlo simulation and real-time workflows. Both are no-obligation.
Do you build custom data applications?
Yes. Beyond models and pipelines, we build data applications with tools like Dash and Streamlit, operational apps that put models and governed data directly into the hands of business users.