For modern businesses, advanced analytics is indispensable for converting raw data into actionable insights that drive growth.
Azure Synapse machine learning Analytics provides a powerful platform for enterprise-scale analytics and AI, while Power BI delivers intuitive self-service BI capabilities. Together, they enable a best-of-breed analytics stack.
Specifically, the native integration between Synapse ML and Power BI unlocks game-changing capabilities for business users. Let’s explore how.
An Overview of Azure Synapse ML
Azure Synapse ML offers a robust machine learning platform for the data warehouse:
- Drag-and-drop visual interface to train ML models on data in Synapse. No coding needed.
- AutoML for automated model creation, hyperparameter tuning and feature selection.
- Broad library of algorithms including regression, classification, time series forecasting and text analytics.
- Model management and monitoring capabilities.
- Python SDK for coding custom ML models and experiments.
With Synapse ML, data teams can build, deploy and manage ML models that generate predictive insights.
Power BI for Exploring Synapse ML Models
Power BI provides intuitive interfaces for BI users to consume Synapse ML model insights:
- Interactive Reports – Embed ML predictions into reports using Power BI visuals. Easily tweak parameters.
- Natural Language Query – Ask questions in plain language to get forecasted values and trends.
- Predictions in dashboards – Include model predictions in live dashboards that update dynamically.
- Sharing and collaboration – Easily share interactive reports and dashboards with stakeholders.
This allows everyone to leverage predictions and ML-powered analytics themselves without coding.
How Power BI Accesses Synapse ML Models
Seamlessly integrating the platforms, Power BI connects directly to Synapse ML models for exploration:
- Model registration – Models are registered in Power BI via Synapse Studio.
- Live connection – Power BI maintains a live connection to the models in Synapse.
- Predictions invoked via DAAS – Power BI uses the Data Analysis Expressions (DAX) language behind the scenes to invoke predictions.
- Auto-generated datasets – For each model, Power BI creates a virtual dataset containing its output schema.
- Transparent to users – The complexity is abstracted away, providing users simple access to model insights.
This backend plumbing enables self-service analytics leveraging Synapse ML.
Use Cases: Turning Data into Decisions
Some example use cases where Power BI + Synapse ML generate tremendous value:
Demand Forecasting – Embed demand predictions directly into inventory planning reports to optimize stock levels.
Risk Scoring – Include customer credit risk scores when analyzing account profiles in Power BI to guide credit limits.
Chomsky Intent Analysis – Categorize customer survey responses based on sentiment analysis model predictions.
Anomaly Detection – Flag anomalies in manufacturing sensor data visualized in Power BI dashboards for rapid incident response.
Price Optimization – Enable marketers to fine-tune pricing scenarios using price elasticity model forecasts.
The possibilities are endless for infusing BI with the power of ML!
Get More from Data with Synapse and Power BI
Together, Azure Synapse ML and Power BI augment business intelligence with:
- Sophisticated ML models developed by data scientists without coding
- Intuitive visibility into model insights for business users via self-service BI
- Smarter predictions and recommendations operationalized into key workflows
- Streamlined collaboration around advanced analytics between roles
