This overview explores advanced machine learning components and features within Rayven.io, focusing on repositories, pre-processing actions, forecasting models, anomaly detection techniques, predictive maintenance analytics, and visualization tools.
Rayven.io provides a comprehensive machine learning (ML) environment integrated into its low-code workflow builder. This enables organizations to preprocess data, train and deploy models, detect anomalies, forecast future trends, and visualize results—all within a unified, real-time application.
This article covers key ML components, supported models, workflow integration steps, and visualization strategies.
1. ML Actions and Workflow Process
Rayven.io provides built-in ML actions that are used to configure data pipelines and apply models.
Core Actions
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Preprocessing: Clean and transform data for modeling. Includes filtering, normalization, and column selection.
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Aggregate: Summarize data streams using functions like average, sum, min, max. Supports trend analysis and anomaly detection.
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Run Model: Execute a trained model within a workflow, generating real-time predictions or anomaly flags.
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Save Model: After training, store the model in a repository for reuse across apps.
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Load Model: Retrieve and apply a saved model to new data streams.
These actions are performed inside Rayven’s Machine Learning Node, which is available in the workflow builder.
2. Supported Machine Learning Models
Rayven.io includes a wide range of built-in models for forecasting, classification, anomaly detection, and predictive maintenance.
Forecasting Models
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Forecasting with OLS (Ordinary Least Squares): Linear model used for simple trend prediction.
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Forecasting with Linear Regression: Predict continuous outcomes using historical inputs.
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Forecasting with Prophet: Robust time series model that accounts for trends, seasonality, and holidays.
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Forecasting with ARIMA: Statistical model for time series forecasting based on past values and residuals.
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Forecasting with STL (Seasonal-Trend Decomposition): Separates trend and seasonal components for improved forecasts.
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Lazy Prediction: Rapidly compare multiple forecasting models to identify the best-performing one.
Anomaly Detection Models
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Threshold Anomaly Detection: Flags values outside defined numeric thresholds.
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Level Shift Anomaly Detection: Detects changes in average behavior over time.
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Bayesian Anomaly Detection: Uses probabilistic methods to detect rare or abnormal values.
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Persist Anomaly Detection: Tracks long-term anomalies and system drift.
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LSTM Autoencoder: Deep learning model that detects anomalies by reconstructing time-series patterns.
Predictive Maintenance Models
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Vibration Analysis: Identifies equipment wear or imbalance from vibration sensor data.
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Survival Analysis: Estimates time-to-failure to support proactive maintenance.
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XGBoost Analysis: Tree-based algorithm for classification and regression, ideal for high-accuracy maintenance predictions.
Data Profiling
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Pandas Data Profiling: Generates statistical summaries, missing value analysis, and distribution reports. Supports model readiness assessments.
3. Connecting Machine Learning to Applications
Workflow Integration
To use machine learning in a Rayven app:
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Open the Workflow Builder
Navigate to the relevant workflow within your app environment. -
Insert a Machine Learning Node
Drag in the ML node and connect it to the data source or preprocessing steps. -
Select the Desired Model
Configure the ML node to choose from the supported models listed above. You may load a saved model or define parameters for on-the-fly training and execution. -
Run the Model
Connect the ML node to downstream workflow steps for actions (e.g., alerts, table writes) or visualization.
4. Visualizing Model Outputs
Dashboards and Widgets
You can display the output of any model in a dashboard by connecting it to Rayven’s visualization widgets:
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Line charts for time-series forecasts
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Gauges for thresholds
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Heatmaps for anomaly scores
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Tables for classification results
Widgets are configured to bind to the output of the ML node or associated workflow result tables.
Conditional Display
You may also configure conditional formatting or visibility based on user groups, allowing role-based access to ML outputs (e.g., field techs see alerts, analysts see prediction scores).
5. Bringing in Your Own Model
Rayven.io supports integration with custom models built outside the platform, such as in Python, R, or other environments.
Supported Methods
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API Integration: Expose your model via a REST API and use Rayven’s HTTP node to send input data and receive predictions.
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File Import/Export: Import model predictions generated externally and load them into a workflow for analysis or visualization.
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Custom Workflow Nodes: For advanced users, Rayven supports scripting or custom nodes where your own logic or algorithm can be embedded directly.
Use Cases
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Use pretrained neural networks hosted in external ML services
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Deploy a Python-based survival analysis model and connect it via API
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Integrate with cloud services such as AWS SageMaker or Azure ML
Best Practices
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Standardize input/output formats for consistency
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Store API keys and tokens securely using Rayven’s environment variable management
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Combine external models with Rayven’s real-time workflow logic for production use
This enables teams to leverage their existing ML investments while taking advantage of Rayven’s orchestration, automation, and visualization capabilities.
Summary
Rayven.io provides a full-stack ML environment for:
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Data preprocessing
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Model selection and execution
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Forecasting and anomaly detection
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Predictive maintenance
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Real-time visualization
The ML node within workflows allows direct integration between data streams, models, and dashboards—eliminating the need for external tools or complex infrastructure.
Q&A
Q: How do I use machine learning in a workflow?
A: Drag a Machine Learning Node into your workflow. Connect it to input data, select a model (e.g., ARIMA, LSTM, XGBoost), and define outputs for predictions, flags, or classifications.
Q: Can I reuse models across projects?
A: Yes. After training, use the Save Model action to store it in a repository. Later, use the Load Model action in any workflow to deploy it without retraining.
Q: What’s the best way to test different forecasting models?
A: Use Lazy Prediction. It applies and compares multiple algorithms and reports performance so you can choose the best option.
Q: Can I view ML results in real time?
A: Yes. Connect the output of the ML node to dashboards using widgets such as line charts, gauges, or tables.
Q: What kind of anomalies can I detect?
A: Rayven supports simple thresholds, persistent anomalies, Bayesian probability-based detection, and advanced LSTM-based deep learning methods.
Q: How do I prepare my data for modeling?
A: Use Preprocessing and Aggregate actions to clean, normalize, and summarize your data before feeding it into a model.
Q: Can I use external Python models or only built-in ones?
A: Rayven supports both. Built-in models are no-code ready, but you can also use external models via APIs or Python workflows where supported.
Q: Is visualization role-based?
A: Yes. Interface access and widget visibility can be controlled by user group, so each role sees only what they need.