Data science and machine learning services for predictive analytics

Data analysis and business intelligence tell you what happened and why. Data science goes a step further: it uses statistics and machine learning to estimate what is likely to happen next and which cases need attention first. The two belong together. A forecast nobody trusts has no value; neither does a score that never reaches the person who decides.

That is why we treat data science as part of the same estate as your reporting, not as a separate laboratory. Models read from the data your dashboards already use. Their results come back into the reports, applications and approval routes your teams rely on.

• Data & BI: Microsoft Fabric and Power BI reporting

Across the region, data is moving from reporting into decisions. In Saudi Arabia, SDAIA’s guidance notes that Vision 2030 assumes extensive use of data analytics and insights in policy-making, economic planning and business strategy.

Most organisations already hold the raw material: years of ERP, CRM, finance, supply chain and service data, often in both Arabic and English. The real question is which decisions would change if a reliable prediction were available and whether the data can support it. We start there, not with an algorithm.

Language matters here too. The people who use a model’s output should be able to read it, question it and act on it in their own language, so we deliver in Arabic and English.

Good first use cases have a clear decision, a measurable result and enough history to learn from. We help you choose one that is valuable enough to matter and small enough to deliver and measure. We also check early whether a simpler rule or a better report would answer the question, because not every question needs a model. Typical starting points include:

• Demand and sales forecasting for planning, purchasing and inventory.
• Service-demand and workload forecasting for staffing contact centres, branches and field teams.
• Cash-flow and collections forecasting; payment-risk scoring for receivables.
• Customer churn, renewal and next-best-offer models that feed your CRM.
• Anomaly detection in transactions, expenses or sensor readings.
• Maintenance prediction for equipment and fleets.
• Classification of documents, requests and free text in Arabic and English, with human review where it matters.

We take a use case from question to production and keep it working afterwards.

• Use-case framing and a feasibility check on your data before any build starts.
• Data preparation and feature engineering in Microsoft Fabric, working on data in OneLake rather than on copies.
• Model development in Python with open-source libraries such as scikit-learn, with experiments and models tracked in MLflow.
• Deployment as scheduled batch scoring in Fabric or as real-time endpoints in Azure Machine Learning.
• Predictions surfaced in Power BI reports, Dynamics 365 and Power Apps, so they reach the people who act on them.
• Monitoring, retraining and documentation, so models stay accurate and auditable.
• Knowledge transfer to your analysts and data engineers, in Arabic or English, so your team can run, question and extend the models.

Both are Microsoft platforms for data science and many projects use both. The Data Science experience in Microsoft Fabric works directly on governed data in OneLake, with notebooks, Spark, built-in MLflow tracking and batch scoring. Predictions written back to OneLake can be read by Power BI in Direct Lake mode without copying. Semantic link lets data scientists reuse the measures analysts have already defined in Power BI semantic models.

Azure Machine Learning is Microsoft’s service for managing the whole machine learning lifecycle: training; automated machine learning; managed endpoints for real-time and batch scoring; MLOps for monitoring, retraining and audit.

As a rule of thumb, Fabric fits when your data and reporting already live there. Azure Machine Learning fits when models need production real-time serving or stricter MLOps; Fabric’s own real-time model endpoints are still in preview. The choice follows your estate, not our preference.

A model is only as acceptable as the data it uses. Azure Machine Learning does not store or process your data outside the region where you deploy it. Microsoft lists UAE North as a region where all Fabric workloads are available, while Qatar Central and UAE Central are listed for Power BI only. With Fabric, your tenant’s home region matters too: Microsoft notes that if you create capacity in a different region, some customer data still remains in the home geography. The platform choice can therefore depend on where your data must stay. We check current availability during design.

Personal data needs particular care. Saudi Arabia’s Personal Data Protection Law (regulated by SDAIA) and the data-protection rules of the UAE, Qatar, Jordan, Kuwait and Oman shape what can be used for training and where. We design with your compliance team, keep personal data in features to a minimum and document what each model uses.

Arabic text, names and addresses need their own handling. In practice that means normalising letter variants such as the forms of alef and taa marbuta, matching names written in Arabic and Latin script, reading Hijri and Gregorian dates correctly and testing results on Arabic data as well as English.

A prediction is a recommendation, not a decision. We follow the same principle as the rest of our AI work: AI acts within roles and approvals; people stay accountable. In practice that means explaining which factors drive a score, setting thresholds with the business owner, routing borderline cases to a person and keeping a record of what the model recommended and what was decided.

Azure Machine Learning supports this with tools for fairness, explainability, tracking and auditability. Both platforms keep a history of experiments and model versions.

Each model is documented in plain language: the decision it supports, the data it uses, how it performed on held-back data, its known limits and who owns it. That record makes a model easier to review, to hand over and to retire when it no longer earns its place.

• AI with control: our approach
• AI agents for business processes