MLOps Automation in Rulex Platform
Enable continuous learning with automated monitoring, retraining, and feedback loops
Simplify MLOps and optimize machine learning workflows
Managing machine learning (ML) operations can be complex, but Rulex Platform simplifies the process with an end-to-end MLOps automation solution.
From automating the tedious tasks of data preparation to continuously monitoring and tuning model performance, Rulex enables data teams to focus on what matters most: delivering business value.
How Rulex automates MLOps
AUTOMATE DATA PREPARATION AND MODEL SELECTION
Rulex eliminates the manual effort required in data ingestion, cleansing, and preparation by automating these steps.
With AutoML capabilities, Rulex helps you find the best features, tune hyperparameters, and compare algorithms to select the optimal model for each specific use case. This enables faster and more efficient machine learning model development across use cases.
MONITOR MODEL PERFORMANCE IN REAL TIME
To maintain high-quality ML models, Rulex continuously monitors performance based on business-specific KPIs.
Early warnings detect signs of model drift, triggering alerts and initiating automated retraining processes to keep models accurate and reliable.
RETRAIN AND FINE-TUNE MODELS
Rulex enables continuous model improvement by integrating human expertise into MLOps workflows.
Feedback loops combine machine learning insights with domain knowledge, allowing:
- Continuous model refinement
- Improved robustness and accuracy
- Adaptive learning over time
See Rulex’s MLOps automation in action
IMPROVING DATA QUALITY WITH EXPERT FEEDBACK LOOPS
A Fortune 50 supply chain company faced challenges in maintaining high data quality across daily operations, as traditional rule-based systems failed to detect deeper logical inconsistencies.
The solution not only autonomously identified the logical inconsistencies that frequently slip through standard business rule checks, but also integrated the domain knowledge of business experts in an expert feedback loop.
This resulted in continuous data quality through automated corrections and adaptive processes, aligning with the key MLOps principle of continuous improvement.
ACCELERATING TASKS AND PROCESS INTEGRATION
A global pharmaceutical company needed to integrate data processing, modeling, and forecasting into a unified workflow while improving speed and accuracy.
Rulex implemented an AutoML-driven demand forecasting solution, integrating data pre-processing, modeling, and forecasting.
Rulex Platform incorporated sales data and external variables like geographic information, automatically selecting the best-fit model and enabling parallel testing and execution. Forecasts over a three-month horizon included automated re-runs with new data to maintain high accuracy. This all-in-one solution seamlessly integrated forecasts into inventory and logistics processes.
ASSESSING CREDITWORTHINESS
A major Italian bank needed to assess credit risk across different customer segments – including private, small business, and corporate clients – using multiple models such as transactional, sociodemographic, and behavioral, while ensuring ongoing model accuracy and reliability.
The solution provided by Rulex unified weighted scores from different models into a single output, which was then refined through the integration of expert-driven rules. Real-time alerts flagged model performance decay, triggering model re-extraction and reconfiguration, with reports documenting drops and the new optimal settings.
+1 million credit positions analyzed per day
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