ml model

42 Topics

How are organizations using Microsoft Fabric for end-to-end Machine Learning pipelines?
I'm curious how companies are implementing complete ML workflows in Microsoft Fabric. Do you perform: Data ingestion Feature engineering Model training Model deployment Monitoring entirely inside Fabric, or do you combine Fabric with Azure ML, Databricks, or other platforms? I'd love to learn from real production experiences.
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binitafulpagare
13 days ago Place Data Science
271Views
3likes
5Comments
Getting Started with Machine Learning in Microsoft Fabric
Getting Started with Machine Learning in Microsoft Fabric Microsoft Fabric brings data engineering, data science, and analytics together in one unified platform, making it easier to build and manage machine learning solutions. With Fabric, you can: ✅ Prepare and transform data efficiently ✅ Build and train ML models using notebooks ✅ Track experiments and compare model performance ✅ Deploy models for real-world predictions ✅ Collaborate with your team in a single workspace Machine learning isn't just about creating accurate models—it's about turning data into actionable insights that drive better decisi
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tanziyaashaq
14 days ago Place Data Science
279Views
3likes
4Comments
What are the biggest limitations you've encountered with OneLake in production?
OneLake is one of the most interesting features of Microsoft Fabric. For those using it in production: What challenges have you faced? Are there any performance bottlenecks? How do you organize large enterprise datasets? Any best practices for governance and security? Looking forward to hearing real-world experiences.
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binitafulpagare
21 days ago Place Data Science
205Views
0likes
5Comments
Fabric Capacity
Hi everyone, Capacity planning seems to be one of the most important aspects of successfully running Microsoft Fabric in production. For those managing enterprise environments: How do you estimate the right Fabric capacity before deployment? Have you ever underestimated or overestimated your capacity needs? Which workloads consume the most resources in your environment? What monitoring practices help you avoid performance issues? I'd appreciate any practical advice or lessons learned. Thank you!
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binitafulpagare
28 days ago Place Data Science
137Views
0likes
4Comments
Notebook Strategy
Hi everyone, Many organizations use Microsoft Fabric notebooks for data engineering, analytics, and machine learning. I'm curious about how experienced teams organize notebooks in large projects. Do you separate notebooks by business domain, pipeline stage, or individual developers? How do you manage reusable code, version control, documentation, and collaboration across multiple team members? If you've developed standards or best practices that improved maintainability, I'd really appreciate hearing about them. Looking forward to learning from your experience.
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binitafulpagare
29 days ago Place Data Science
101Views
1like
2Comments
Data Science/MLOps in AML vs MS Fabrics
Hi everyone, could someone explain the key differences between implementing MLOps in Azure Machine Learning Studio versus Microsoft Fabric? I am looking to understand the advantages and disadvantages of each. Additionally, which platform is generally preferred for standard data science workflows, and why?
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AlAmeenN
2 months ago Place Data Science
1.6KViews
4likes
7Comments
AI Skills Fest 2026
🚀 Looking to build your AI and Data Science skills? Microsoft is hosting the AI Skills Fest 2026, a free global learning event packed with AI-focused training, expert-led sessions, hands-on activities, and learning paths for students, developers, data professionals, and business users. Participants can also earn digital badges and may qualify for certification opportunities. 📅 Event Dates: June 8–12, 2026 Whether you're just starting your Data Science journey or looking to expand your AI knowledge, this is a great opportunity to learn from Microsoft experts and the global community. 🔗 Register here: https://aiskillsnavigator.microsoft.com/events/AISF2026 Has anyone here joined? Which learning track are you exploring—AI, Data Science, Power BI, Fabric, or Machine Learning?
Prince0011
2 months ago Place Data Science
1.3KViews
1like
1Comment
Validation/optimisation options for AI features
Hello Are there any ways to validate or optimise the output of the Fabric AI models (sentiment analysis, classification etc)? We have noticed that the models sometimes output erroneous data - e.g. labelling a text as 'positive' instead of 'negative' - and we would like to make sure that the data sent to users is as accurate as possible. Are there any upcoming plans for this in the roadmap, or is it just expected behaviour that we have take into consideration? If there are any workaround we can try in the meantime, please let me know! Thanks
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fabricuser123_
4 months ago Place Data Science
2KViews
2likes
5Comments
MLFlow - Not logging results of all epochs
I am trying to use MLFlow to log the results of my model training to do hyperparameter tuning however it is only logging the last val_loss & loss value in the experiment. Running in a Python 3.11 notebook with tensorflow: 2.18.0 mlflow: 3.8.1 pandas: 2.2.2 with mlflow.start_run() as run: mlflow.tensorflow.autolog() history, model = run_model(X_train, X_val) for epoch, (tr, vl) in enumerate( zip(history.history["loss"], history.history["val_loss"]) 😞 mlflow.log_metric("loss", tr, step=epoch) mlflow.log_metric("val_loss", vl, step=epoch) Then this is the only output in the experiment:
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Zoe_Guest
5 months ago Place Data Science
2.7KViews
0likes
6Comments
MLFlow - Problem with aliases and metrics
Our team is trying to use MLFlow to manage the models created in fabric, however this has been a hard task since many features of MLFlow Python library are apparently unavailable in Fabric. We tried to implement aliases in our models without any success, the same thing happened when we try to access metrics via models (not runs). Every time we instanciate a model only some data are available like model_name, version and tags, but we can´t access to params and metrcis asociated to that model. I would appreciate any help, as I don't know if these issues are due to a limited implementation of MLFlow's capabilities in Fabric or incorrect use of the MLFlow API. If anyone could share some code that allows acess to model metrics and parameters using any type of model object (LoggedModel, ModelVersion, ModelInfo, etc) would be very appreciated.
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claudevs
5 months ago Place Data Science
6.6KViews
1like
5Comments