Machine Learning with Microsoft Fabric

Data science and machine learning are central components of modern data-driven organizations. Increasing volumes of heterogeneous data must be integrated, prepared, analyzed, and operationalized – efficiently, at scale, and seamlessly embedded into existing processes.

Microsoft Fabric provides a modern end-to-end data platform that unifies analytics, data engineering, and BI workloads within a single environment. For data scientists, this creates a workspace where data preparation, modeling, machine learning, and deployment interact seamlessly.

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Why Machine Learning with Microsoft Fabric?

  • One platform instead of isolated tools – data integration, analytics, and reporting in one place
  • Scalability without infrastructure management overhead
  • Seamless Power BI integration for immediate visualization
  • Modern data lakehouse architecture suitable for both classic DWH and data science workloads
  • Faster results through unified tooling and automated pipelines

Services

We support organizations in leveraging data mining and machine learning capabilities within the Microsoft ecosystem. Our services include:

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Consulting and Architecture

  • Development of a data mining strategy aligned with your business processes
  • Selection of appropriate algorithms and modeling techniques
  • Architectural consulting for Fabric implementations (OneLake, domains, workspaces)

Data Integration and Data Engineering

  • Implementation of pipelines in Fabric Data Factory
  • ETL/ELT automation using Fabric Dataflows or Spark notebooks
  • Harmonization, cleansing, and enrichment of large datasets

Modeling and Machine Learning

  • Development of classification, regression, clustering, and forecasting models
  • Feature engineering using Spark, Python, R, and SQL
  • Training, evaluation, and tuning within Fabric notebooks or ML libraries

Operationalization and Deployment

  • Publishing models to Power BI, APIs, or real-time scenarios
  • Monitoring and lifecycle management of machine learning models
  • Establishing CI/CD processes for data science projects

Technical Capabilities of Microsoft Fabric for Data Science and Data Mining

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Microsoft Fabric unifies data engineering, data science, and business intelligence within a shared architecture. For data science and data mining use cases, the platform provides a broad range of advanced tools and capabilities.

Unified Data Lake – OneLake

  • Storage of all data in a unified Delta Lake architecture
  • Direct usability within Spark, SQL, Power BI, and real-time analytics
  • Versioning, time travel, and ACID transactions

Lakehouse

  • Combination of data lake and data warehouse concepts
  • Table-based workflow for data scientists
  • Use of Parquet and Delta formats for large-scale datasets

Notebooks (Python, R, Scala, SQL)

  • Fully integrated notebook environment
  • Direct access to OneLake data without manual connections
  • Support for common libraries: pandas, scikit-learn, statsmodels, spark.ml, tidyverse, etc.

Spark Compute

  • Highly scalable distributed processing engine
  • Suitable for feature engineering, iterative ML models, and large data volumes
  • Batch and streaming processing

Fabric Machine Learning (ML-Flows)

  • Model training, tracking, and versioning
  • Model governance using MLOps concepts
  • Integration with Power BI, Azure ML, and external systems

Seamless Integration with Power BI

  • Direct embedding of ML results into dashboards
  • R and Python visuals for interactive analytics
  • Real-time access to Fabric models

Frequently Asked Questions on Machine Learning with Microsoft Fabric

This FAQ covers the topics most frequently discussed in consulting and training engagements. Each answer is concise and refers to further resources where appropriate. If your question is not listed, please feel free to contact us.

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For which data science use cases is Microsoft Fabric suitable?

Fabric supports classification, regression, clustering, forecasting models, and data mining scenarios involving large, heterogeneous datasets within lakehouse architectures.

How does Microsoft Fabric differ from isolated ML environments?

Unlike separate tools, Fabric integrates data integration, data engineering, modeling, and BI within a single platform, reducing system fragmentation and complex interfaces.

How does Fabric Lakehouse support machine learning on large datasets?

Fabric lakehouse machine learning leverages Delta/Parquet storage, Spark, and SQL for scalable data processing, feature engineering, and model training directly on data stored in OneLake.

What does MLOps in Microsoft Fabric mean in the context of ML-Flows?

MLOps in Microsoft Fabric refers to model training, tracking, versioning, and governance (e.g., via Fabric Machine Learning / ML-Flows), as well as lifecycle management through deployment.

How does data science with Microsoft Fabric integrate into Power BI?

Results generated in Fabric can be seamlessly embedded into Power BI dashboards, either through direct model integration or via R/Python visuals for interactive analysis.