Who should use the Perform predictive analytics workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
A streamlined workflow to prepare data, build predictive models, and monitor their ongoing performance for reliable business insights.
Deliverable outcome
An operational monitoring dashboard displaying real-time metrics and automated alerts for any performance degradation.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
An operational monitoring dashboard displaying real-time metrics and automated alerts for any performance degradation.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Prefect to a fully automated data pipeline that consistently delivers clean, preprocessed datasets to the analytics environment on a scheduled basis. Then, you pass the output to Accenture AI Solutions to a fully validated predictive model with documented accuracy, precision, recall, and feature importance for stakeholder review and integration. Finally, SAS Viya is used to an operational monitoring dashboard displaying real-time metrics and automated alerts for any performance degradation.
Orchestrate data workflows
A fully automated data pipeline that consistently delivers clean, preprocessed datasets to the analytics environment on a scheduled basis.
Perform predictive analytics
A fully validated predictive model with documented accuracy, precision, recall, and feature importance for stakeholder review and integration.
Monitor model performance
An operational monitoring dashboard displaying real-time metrics and automated alerts for any performance degradation.
Set up automated data pipelines to collect, clean, and transform raw data from various sources into a structured format suitable for predictive analytics. This ensures data quality and availability for model training.
High-quality data is the foundation of accurate predictive models; without proper orchestration, the analytics step may produce unreliable results.
A fully automated data pipeline that consistently delivers clean, preprocessed datasets to the analytics environment on a scheduled basis.
Use machine learning algorithms to analyze historical data, identify patterns, and generate forecasts or predictions for business outcomes. This step trains and validates models to ensure they generalize well.
This is the core analytical step that transforms data into actionable insights driving decision-making.
A fully validated predictive model with documented accuracy, precision, recall, and feature importance for stakeholder review and integration.
Deploy the model into production and continuously track its accuracy, drift, and other key metrics over time. Set up alerts for when performance degrades below acceptable thresholds.
Ongoing monitoring ensures the model remains effective as data patterns change, preventing costly errors from stale predictions.
An operational monitoring dashboard displaying real-time metrics and automated alerts for any performance degradation.
Timeline Map
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
§ Related
A streamlined workflow to prepare data, train a neural network model, and evaluate its performance using AI tools.
Streamlined workflow to automatically refactor existing code, debug errors, and finalize the refactored code for deployment.
End-to-end workflow to orchestrate data pipelines: start by performing predictive analytics to inform the pipeline, then orchestrate the data flow, and finally monitor model performance for ongoing reliability.