How are teams using AI agents to automate data science workflows?
I am exploring how AI agents can support modern data science workflows by automating repetitive tasks and improving collaboration between data teams. Some potential use cases: Automating data exploration and preprocessing steps Generating insights from datasets using natural language queries Assisting with feature engineering and model evaluation Triggering ML pipelines based on business events Monitoring model performance and identifying data quality issues I would like to understand how data science teams are combining Microsoft Fabric, notebooks, ML workflows, and AI agents in real-world projects. What approaches, tools, or architecture patterns are you using for AI-assisted data science workflows?Challenges in Deploying Machine Learning Models into Production Workflows
Hi everyone, I am exploring how teams are moving machine learning models from experimentation into reliable production workflows. In many projects, the challenge is not only training the model but also managing the complete lifecycle: - Preparing and maintaining quality datasets - Tracking experiments and model versions - Deploying models for real-world usage - Monitoring performance after deployment - Handling model updates over time I would like to understand how the community is approaching this with Microsoft Fabric. A few questions: What are the recommended patterns for managing ML model lifecycle in Fabric? How are teams handling model versioning and experiment tracking? Are you using Fabric notebooks, MLflow, or external platforms for managing production ML workflows? What challenges have you faced when moving data science projects from development to production? Would love to hear practical experiences and approaches from the community.
Starting Data Science journey with Python - What is the next step after lists?
Hello everyone! I am a beginner starting my journey in Data Science. I have recently learned the basics of Python, including lists. Could anyone guide me on what I should learn next in Python for Data Science? Also, as a beginner, what is the best way to practice using Microsoft Fabric for Data Science? Thank you!
Certificate validation error between Fabric and Huggingface
Hi, I'm tyring to download a couple of transformer/sentence-transformer model weights into a notebook and I keep running into certificate mismatch issues between fabric and huggingface. I was wondering if anyone else has ran into this issue or if this is a known issue that Microsoft plans on handling in the very near future? Environment Microsoft Fabric Notebook (PySpark) Python 3.11 PyTorch 2.2.1 Canada tenant sentence-transformers==2.7.0 successfully installed What works Package installation succeeds %pip install sentence-transformers==2.7.0 Import succeeds from sentence_transformers import SentenceTransformer version check succeeds import torch import sentence_transformers print(torch.__version__) print(sentence_transformers.__version__) What fails from sentence_transformers import SentenceTransformer model = SentenceTransformer("all-MiniLM-L6-v2") Or Observations The notebook appears able to reach Hugging Face and download metadata files. For example, the following files are successfully cached: config.json config_sentence_transformers.json modules.json README.md sentence_bert_config.json However, the model weight file never downloads. The Hugging Face cache contains *.incomplete with a file size of 0 MB. Error The relevant error message appears to be "No CA certificates were loaded from the system" Is downloading Hugging Face model weights from Fabric notebooks currently supported? If so, is there a known issue with certificate validation? Is there a recommended workaround? If not. then is there a recommended approach for generating semantic text embeddings within Fabric notebooks utilizing some kind of ANN based algorithm instead of classic techniques such as TF-IDF or SVD followed by some type of clustering? The goal of this notebook is semantic clustering of approximately 500 short "type" descriptions for taxonomy development. Thanks!
