What's new and planned for Synapse Data Science in Microsoft Fabric
Important
The release plans describe functionality that may or may not have been released yet. The delivery timelines and projected functionality may change or may not ship. See Microsoft policy for more information.
Synapse Data Science provides data scientists with an end-to-end workflow for building their machine learning models, from exploration to model scoring. From a data exploration perspective, data scientists can use R and Python in notebooks, and built-in tools like Data Wrangler for easy analysis. Users can track and compare their model experiments and runs with MLFlow. They can save the best performing model in the workspace as a new model item and easily use Predict for batch scoring at scale. Data science in Fabric is deeply integrated with the rest of the stack, meaning it's seamless to score data in a lakehouse, write back the predictions to OneLake, and visualize the data in reports using Direct Lake mode.
To learn more, see the documentation and visit the announcement blog.
Investment areas
| Feature | Estimated release timeline |
|---|---|
| Semantic Link GA | Shipped |
| ML Model endpoints | Q4 2024 |
| AutoML & hyperparameter tuning with FLAML | Shipped |
| Generative AI experiences on your data | Q3 2024 |
| Monitoring hub integration | Q3 2024 |
Semantic Link GA
Estimated release timelines: Shipped
Semantic link bridges the gap between data science and BI by providing a Python library (SemPy) that enables data scientists to interact with Power BI datasets and measures. You can use SemPy to read, explore, query, and validate data in Power BI from Python notebooks, and use the library's features to detect and resolve data challenges. Users can also write back to the Power BI dataset through the lakehouse with Direct Lake mode.
ML Model Endpoints
Estimated release timelines: Q4 2024
Model endpoints in Fabric enable users to perform real-time scoring of machine learning models, providing immediate predictive insights. With Model Endpoints, you'll seamlessly integrate your machine learning models into your workflows for quicker and more effective decision-making based on real-time data analysis.
AutoML & hyperparameter tuning with FLAML
Estimated release timelines: Shipped
You can automate the process of training and optimizing machine learning models with the flexibility of FLAML. You can use Spark to reduce costs by parallelizing hyperparameter trials and easily tune SparkML and SynapseML models. MLflow automatically captures AutoML runs and hyperparameter metrics.
Generative AI experiences on your data
Estimated release timeline: Q3 2024
We'll be introducing the capabilities to build custom generative AI experiences for your data, enabling you to engage in tailored Q&A within Fabric. These custom experiences are compatible with Microsoft 365 Copilot, Copilot Studio and AI Studio. Additionally, scalable, AI-powered data processing makes it easier to use generative AI to transform data at scale.
Monitoring hub integration
Estimated release timeline: Q3 2024
Data science item runs are available to monitor inside the monitoring hub alongside all other Fabric items.