Forum Discussion
Data Science/MLOps in AML vs MS Fabrics
Hi AlAmeenN ,
Thank you for reaching out to Microsoft Fabric Community and Thanks to Prince0011 for sharing meaningful insights.
Based on the requirements you've described production-grade MLOps, CI/CD integration, automated retraining, model monitoring, governance, and scalable deployment workflows Azure Machine Learning would generally be the stronger choice for the MLOps and model operationalization layer. Fabric, meanwhile, continues to be well-suited for data engineering, feature preparation, analytics, and experimentation workloads.
Regarding the hybrid architecture you described, Microsoft does not currently publish a single reference stating that Fabric + Azure Machine Learning is the recommended architecture for all scenarios. However, Microsoft does provide documented integration capabilities between Fabric, OneLake, and Azure Machine Learning, enabling organizations to leverage the strengths of both platforms when appropriate for their requirements.
The following overviews provide useful guidance on the capabilities of each platform:
1) Fabric Data Science Overview:
https://learn.microsoft.com/fabric/data-science/data-science-overview
2) Azure Machine Learning Documentation:
https://learn.microsoft.com/azure/machine-learning/The most appropriate architecture ultimately depends on factors such as deployment requirements, governance needs, operational scale, and model lifecycle management expectations.
Best Regards,
Abdul Rafi
Hi AlAmeenN ,
Thank you for using Microsoft Fabric Community,
The answer really depends on what you're trying to achieve and how mature your machine learning requirements are.
If your primary focus is enterprise-grade MLOps, production deployments, CI/CD integration, model monitoring, and advanced AI/ML workloads, Azure Machine Learning is generally the more mature and feature-rich platform. It has been Microsoft's dedicated machine learning platform for several years and offers a broader set of capabilities for managing the complete ML lifecycle.
On the other hand, Microsoft Fabric is becoming an increasingly attractive option, especially for organizations that are already using Fabric for data engineering, analytics, and reporting. Its strength lies in providing a unified experience where data preparation, analytics, data science, and business intelligence can all work together within the same platform.
In practice, many organizations use both services together. Fabric is often used for data preparation, exploration, and experimentation, while Azure Machine Learning is used when models need to be deployed, monitored, and managed in production at scale.
To provide a more specific recommendation, it would be helpful to understand your exact requirements. Are you building a proof of concept or a production solution? Do you need capabilities such as CI/CD, automated retraining, or model monitoring? Are you working with deep learning or GPU-based workloads? Also, is your data already stored in Fabric and OneLake?
For Reference:
What is Data Science in Microsoft Fabric ?
What is Azure Machine Learning ?
If you can share more details about your use case, we'd be happy to suggest the approach that best aligns with your requirements.
Best Regards,
Abdul Rafi
Hi v-moharafi-msft ,
Thank you for the detailed explanation.
Our requirement is mainly around building a production-grade enterprise MLOps framework with capabilities such as CI/CD integration, automated retraining, model monitoring, governance, and scalable deployment pipelines. We are also looking at long-term maintainability and operational maturity rather than only experimentation or PoC workloads.
Currently, most of our data platform already resides in Microsoft Fabric/OneLake, and Fabric is working well for data engineering, transformations, feature engineering, notebook-based processing, and analytics workloads.
Because of this, we are evaluating whether a hybrid architecture would be the recommended approach, where:
Microsoft Fabric handles data engineering and feature preparation workloads
Azure Machine Learning handles model training orchestration, model registry, deployment, monitoring, and MLOps lifecycle management
From the discussions and documentation we reviewed, Azure ML still appears to be the more mature option for enterprise-scale MLOps and production ML operations.
Could you please confirm whether Microsoft recommends this hybrid architecture pattern currently? Also, are there any official reference architectures, best practices, or customer implementation examples specifically for:
Fabric + Azure ML integration
OneLake integration with Azure ML
Using Fabric pipelines/notebooks together with Azure ML endpoints and deployment workflows
Would really appreciate any guidance or documentation references around this approach.
Hi AlAmeenN ,
Thank you for reaching out to Microsoft Fabric Community and Thanks to Prince0011 for sharing meaningful insights.
Based on the requirements you've described production-grade MLOps, CI/CD integration, automated retraining, model monitoring, governance, and scalable deployment workflows Azure Machine Learning would generally be the stronger choice for the MLOps and model operationalization layer. Fabric, meanwhile, continues to be well-suited for data engineering, feature preparation, analytics, and experimentation workloads.
Regarding the hybrid architecture you described, Microsoft does not currently publish a single reference stating that Fabric + Azure Machine Learning is the recommended architecture for all scenarios. However, Microsoft does provide documented integration capabilities between Fabric, OneLake, and Azure Machine Learning, enabling organizations to leverage the strengths of both platforms when appropriate for their requirements.
The following overviews provide useful guidance on the capabilities of each platform:
1) Fabric Data Science Overview:
https://learn.microsoft.com/fabric/data-science/data-science-overview
2) Azure Machine Learning Documentation:
https://learn.microsoft.com/azure/machine-learning/The most appropriate architecture ultimately depends on factors such as deployment requirements, governance needs, operational scale, and model lifecycle management expectations.
Best Regards,
Abdul Rafi