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?
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?
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:
Thank you for the Microsoft's Fabric Community and these events, interest in learning more!
Hello, Thank you so much for hosting this wonderful event; I am enjoying these sessions and learning a lot. I am grateful to have had the chance to learn about Microsoft and its broad AI endeavors and opportunities. A little about myself is that: My name is Ayush Saha. I am a 1st year MS of CS (AI) major at USC, and an AI Engineer Intern Alumnus from Volkswagen (2025), and accepted presenter to the 2025 US-RSE Research Conference for my research in computer vision/ incident scene reconstruction. My LinkedIn is: https://www.linkedin.com/in/ayush-saha/ This event has definitely inspired me to apply to join Microsoft's internship programs! I am looking forward to the remainder of the event, Ayush S.
