Databricks AI/BI, where reporting meets machine learning.
Databricks AI/BI is what we reach for when a dashboard needs to do more than describe the past. It combines Databricks' data engineering strength with genuine machine learning, so a report can forecast, flag anomalies, or answer a question in plain language, not just chart yesterday's numbers.
Where Databricks AI/BI earns its place in the stack.
Predictive analytics, not just historic charts
Forecasting trends from the same data platform that already holds the pipeline, instead of exporting into a separate ML tool.
Natural language, genie-style queries
Letting a non-technical stakeholder ask a question in plain English and get a real answer from governed data.
AI model deployment, in the same platform
Models trained on Databricks deploy back into the same environment the dashboards already live in — no separate MLOps handoff.
One platform for the pipeline and the report
Delta Lake holds the data, the same platform trains the model and renders the dashboard — one less system to keep in sync.
Reporting that doesn't stop at description.
Databricks AI/BI is our choice whenever a reporting problem is genuinely also a machine learning one — when the useful answer isn't 'what happened' but 'what's likely to happen next'. It sits on the same lakehouse as the data engineering work, so there's no separate export into a modelling tool.
That matters for governance as much as convenience. A model trained on Delta Lake stays inside the same access controls and lineage as the dashboards built on top of it, instead of the data quietly leaving the platform to get modelled somewhere else.
Machine learning on the same platform as the data
Apache Spark and MLflow sit right next to the pipelines and dashboards, so a model never has to leave the lakehouse to get built.
From descriptive to predictive, in one report
A dashboard can chart what happened and forecast what's next, without stitching together two separate tools.
Governance that follows the data, not around it
A model trained on Delta Lake inherits the same access controls and lineage as the tables it was trained on.
Built for genuine scale
TensorFlow and PyTorch on Databricks ML Runtime handle production-sized datasets, not just a notebook-sized sample.
Want your dashboard to predict, not just report? Let's talk Databricks AI/BI.
Book a 30-minute working session with a senior engineer — a real conversation about your reporting, not a sales call.
What a Databricks AI/BI build usually includes.
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Read moreThe stack we ship on.
Pragmatic, mostly boring, and chosen because it works in production — not because it's on the front page of Hacker News.
Have an outcome in mind?
We'll help you
ship it.
- You're building a data product and need a team that can deliver.
- You want to get AI-ready — pragmatically, not theoretically.
- Your reporting is a mess and you need a real platform underneath it.