ml model
78 TopicsBayesian Thinking for Data Science in Microsoft Fabric
In this edition, we’re exploring Bayesian modeling and how to think about uncertainty in a more realistic, practical way using PyMC3. By the time you’re done, you’ll have a clear intuition for what Bayesian thinking really means, why it’s so useful in day-to-day data work, and how it changes the way you interpret results. We’ll also explore how PyMC3 supports this mindset in a structured but approachable way.60Views2likes1CommentHow 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.Solved271Views3likes5CommentsGetting 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 decisiSolved279Views3likes4CommentsCausal Inference for Data Science in Microsoft Fabric
In this edition, we’re exploring into causal inference and why it matters once you move beyond basic reporting and prediction. By the time you’re done, you’ll have a clear understanding of how causal thinking differs from traditional analytics and how to reframe everyday business questions around cause and effect instead of simple correlation. And because insights only matter if they’re understood, we’ll look at how to communicate causal findings clearly and responsibly so decision-makers know what they can trust and act on.54Views2likes0CommentsWhat 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.Solved205Views0likes5CommentsFabric 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!Solved136Views0likes4CommentsNotebook 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.Solved101Views1like2CommentsLevel Up Your Forecasting with Temporal Fusion Transformers for Data Science in Microsoft Fabric
In this edition, we’re exploring Temporal Fusion Transformers in a way that actually makes sense in the real world. You’ll also get a clear walkthrough of the key ideas inside the architecture, like variable selection, gating, and attention, and how they work together to make sense of messy, real-life data. And more importantly, you’ll walk away understanding how TFTs can support you with complexity every day, giving you both clarity and confidence in your forecasting work.438Views4likes2CommentsExploring Text Intelligence through TF-IDF for Data Science in Microsoft Fabric
In this edition, we’re exploring how TF-IDF helps you discover meaning from language. You’ll see how this technique balances frequency and rarity to spotlight the words that truly matter, instead of the ones that just appear most often. By the time you’re done, you’ll have a solid understanding of how TF-IDF bridges the gap between unstructured text and structured analytics and why they are still relevant in the rise of Large Language Models (LLMs).822Views6likes3CommentsMastering Advanced Regression for Data Science in Microsoft Fabric
In this edition, we’re exploring two regression techniques that every data professional eventually bumps into when the simple models stop telling the full story. You’ll get a clear sense of what quantile regression actually solves, especially when your data behaves in unpredictable or uneven ways. By the time you’re done, you’ll feel more confident choosing the regression approach that truly fits the question you’re trying to answer, instead of defaulting to whatever is familiar.328Views2likes5Comments