Blueprint and governance
The catalog and schema layout, access groups, model approval steps and the promotion path from development to production workspaces are agreed with the data platform owner, risk and the ML lead before build.
Platform implementation
Databricks Lakehouse and ML
We implement Databricks for machine learning and generative AI on your lakehouse, with Unity Catalog governing data, features and models and MLflow tracking every experiment and release.
On Databricks the model and its data share one platform, so the work centres on Unity Catalog: who can read which tables, which features are shared, and how models are registered and approved. Databricks runs on AWS, Azure and Google Cloud, and the workspace region sets where data and compute sit, so we match it to residency rules such as the NCA Cloud Cybersecurity Controls in Saudi Arabia and the UAE's data protection law.
Experiments are tracked in MLflow, and models reach production through the Unity Catalog model registry and Model Serving. For generative AI we use Vector Search and the Agent Framework, with Agent Evaluation to judge answers before and after release. Jobs and pipelines are defined in Databricks Asset Bundles and deployed through CI/CD.
Databricks Lakehouse and ML and related product names may be trademarks of their respective owners.
How we implement Databricks Lakehouse and ML
Design, build, integration and run-state support, with each decision recorded for your steering committee.
Feature reuse
Shared feature tables with owners and documentation, so churn, fraud and demand models stop rebuilding the same inputs.
Model monitoring
Lakehouse Monitoring on inference tables to detect drift and data quality issues, with alerts that trigger retraining.
Cluster and cost policy
Cluster policies, serverless compute where it fits, and tags that show spend per team and per model.
Hypercare and managed support
Databricks releases runtime versions often and supports long-term support (LTS) runtimes for longer, so we pin production jobs to LTS runtimes, plan upgrades, and watch job failures, model drift, serving latency and spend.
Ready for machine learning on Databricks?
Databricks suits organisations with data engineering and data science teams who need one platform for pipelines, models and generative AI, and whose data already sits in a Databricks lakehouse.
- A lakehouse on Databricks, or a plan to build one
- Unity Catalog enabled across workspaces
- Data scientists comfortable with Python and notebooks
- An approval process for models that affect customers
Plan Databricks Lakehouse and ML with Hosn Al Tomoh
Share your current systems, compliance themes and integration map. We will return a phased plan, a risk register and a governance model for your stakeholders to review.