Jaggle.AutoML brings automated machine learning into one structured environment, helping teams connect data, define prediction tasks, train and evaluate models, and use the results within Jaggle applications and business processes.
Organisations collect large volumes of information through business applications, spreadsheets, databases, devices and operational systems.
Much of that information is used to explain what has already happened. Turning it into reliable predictive models can require specialised expertise, separate technology environments and considerable manual effort across data preparation, experimentation and deployment.
As a result, forecasting, classification and risk analysis may remain isolated technical exercises rather than becoming part of everyday operational decisions.
Jaggle.AutoML provides a more structured path from available data to models that can be evaluated, deployed and connected with the processes where their results are needed.
See how Jaggle.AutoML supports the complete model-building journey through one guided environment:
Jaggle.AutoML structures the principal stages of automated machine learning within one connected workflow.
Organise datasets, training activity, models and deployments around a specific prediction objective.
Bring in data from files, databases, Jaggle applications and connected systems. Review data types, missing values, feature characteristics and relationships before training begins.
Select the model use case, identify the target variable and determine which available features should contribute to the analysis.
Automate data preparation and model training. Evaluate candidate models and identify the best-performing result for the selected prediction task.
Deploy validated models, generate predictions and make results available to Jaggle applications or connected systems through an application programming interface.
Organise datasets, training activity, models and deployments around a specific prediction objective.
Bring in data from files, databases, Jaggle applications and connected systems. Review data types, missing values, feature characteristics and relationships before training begins.
Select the model use case, identify the target variable and determine which available features should contribute to the analysis.
Automate data preparation and model training. Evaluate candidate models and identify the best-performing result for the selected prediction task.
Deploy validated models, generate predictions and make results available to Jaggle applications or connected systems through an application programming interface.
Jaggle.AutoML helps organisations use machine learning more practically and consistently across business functions.
Jaggle.AutoML can support focused prediction and classification requirements across sectors and departments.
Analyse historical demand, seasonality and relevant operating factors to support production, purchasing and inventory planning.
Identify behavioural patterns and assess which customers or accounts may require retention or engagement activity.
Estimate delivery outcomes, identify potential delays and support more informed logistics planning.
Use operational and equipment data to identify patterns associated with potential failure or service requirements.
Classify activity according to defined risk indicators and support the review of potentially unusual or higher-risk cases.
Use production, storage and environmental data to support product-quality and inventory decisions.
The suitability and performance of each use case depend on the quality, volume and relevance of the available data.
Analyse historical demand, seasonality and relevant operating factors to support production, purchasing and inventory planning.
Identify behavioural patterns and assess which customers or accounts may require retention or engagement activity.
Estimate delivery outcomes, identify potential delays and support more informed logistics planning.
Use operational and equipment data to identify patterns associated with potential failure or service requirements.
Classify activity according to defined risk indicators and support the review of potentially unusual or higher-risk cases.
Use production, storage and environmental data to support product-quality and inventory decisions.
The value of a predictive model depends on whether its results can reach the people, applications and processes that need them.
Jaggle.AutoML models can be used within broader Jaggle applications, where a prediction can support a workflow, user decision or automated action.
Organisations can also create dedicated Jaggle.AutoML applications that connect with external data sources and expose results through a REST application programming interface.
This enables teams to begin with a focused prediction requirement and extend it into a wider operational process as needs develop.
Jaggle.AutoML combines automated machine learning with the application and process capabilities of Jaggle.
Connected to Business Applications
Predictions can become part of the applications and workflows where operational decisions take place.
Structured Across the Lifecycle
Data connection, feature review, training, evaluation, deployment and prediction are managed through one environment.
Transparent by Design
Performance metrics and feature importance help teams assess results and understand the principal factors behind them.
Built for Governed Adoption
Jaggle.AutoML benefits from the enterprise-grade security, access control, auditing, integration and deployment capabilities of the Jaggle platform.
Jaggle.AutoML is part of the Jaggle.Apps family and is built on the AI-powered Jaggle Low-code application generation platform.
This foundation enables organisations to connect predictive models with adaptable applications, workflows, data sources and operational systems within one governed environment.
Jaggle provides:
Comsys can configure, integrate, extend and support Jaggle.AutoML around each organisation’s data environment and operational objectives.
See how Jaggle.AutoML can bring data preparation, model training, explainable results and operational deployment into one structured environment. onboarding, administration, self-service and organisational workflows.
