Leverage an integrated MLOps environment for creating, maintaining, and monitoring AI/ML processes
Deploy predictive models in minutes instead of days
We accelerate the work of Data Science and Machine Learning teams
Lack of an MLOps strategy is costly and inefficient
Design
- Scattered tools for process and model creation
- Data spread across multiple applications
- Limited capabilities for creating decision-making scenarios
Modeling
- Limited formats and types of applicable models
- No versioning and change history
- Difficulties in testing models in production
Management
- Time-consuming deployments
- Low efficiency and scalability
- No real-time monitoring
Scoring.One increases ML process agility by integrating all its elements into a single environment
Data orchestration (any structure of input/output data)
- Internal database with a built-in data lake
- Integrations with any external data source via REST API / SOAP (including ready-made integrations with popular databases such as BIK)
- Support for standard and complex data types (objects, blobs, dictionaries, vectors, arrays)
- Streaming data processing and queue mechanisms with a built-in feature store
- Built-in data quality improvement mechanisms (validation, standardization, and data enrichment)
Implementation of ML&AI models
- Import models in various formats (PMML, Java, Python, R)
- Support for complex ML/AI models (neural networks, advanced classifiers, segmentation)
- Access to ready-made generic models created by Algolytics (credit scoring, behavioral biometrics, models for structured data, models for graph data, LLM for sentiment analysis [for PL], and more)
- Model versioning
- Testing models and scenarios (via graphical interface or API)
Creation of decision-making scenarios
- Graphical (low-code) decision scenario builder (synchronous and asynchronous) with a wide selection of available node types
- Start / End – definition of input/output parameters for the process
- Scoring Code – ML/AI model code
- Scenario Code – ability to nest other scenarios
- Expression – script in Groovy/R/Python
- Decision – decision node enabling different processing paths
- Internal Data – access to internal data stored in the engine (dictionaries, data marts, processing history)
- External Data – access to external data (REST API / SOAP / native integrations)
- Database – saving to a database (SQL / NoSQL)
- Cross-checking – cross-check mechanisms, anti-fraud rules
- Cache – temporary data storage mechanism with expiration control
- Support for complex decision rules
- Support for using multiple ML models simultaneously or alternately
- Import and export of scenarios in JSON and YAML formats
- Scenario execution testing (via graphical interface or API)
- A/B testing of multiple scenarios
- Audit Log – access to processing results along with individual calculations
- Management of the decision scenario lifecycle (monitoring, versioning, configuration)
Deployment of processes and models to production
- "One-click deployment" – deploying built processes and models without involving the IT team
- Implementation of data processing scenarios in production mode
- Integration with external tools (data retrieval and transmission, action initiation)
- Configuration migration between DEV/TEST/UAT/PROD environments via API
- Access to data in scenarios via API (REST, SOAP, others)
Reporting, monitoring, and optimization
- Monitoring the performance of models and processes
- AutoML – continuous model improvement
- Access to calculation results – both complete and partial
- Ability to store model results in cache
- Capability to edit active processes, models, or scenarios
- Scalability support
- Reporting of technical and business KPIs / SLA tracking
- Integration with Business Intelligence tools
Management and security of the ML environment
- Access control and user management
- Ability to whitelist IP addresses / access via VPN
- Audit log
- High scalability and reliability
Do you want to explore all the features of the Scoring.One platform?
What makes Scoring.One stand out?
Low code
Fast process creation and deployment using drag-and-drop technology
Broad application
Support for multiple data sources, advanced models, and complex decision rules
Highest data quality
Built-in data improvement mechanisms (standardization, validation, enrichment)
Ready integrations
Connections with external databases (public registers, credit bureaus, and more)
High performance
Handling hundreds of thousands of queries and records per second, real-time processing < 2ms
Flexible deployment
Cloud, on-premise, or hybrid
Start building AI/ML processes with Scoring.One
Trusted by 50+ major financial institutions, telecommunications, e-commerce and logistics companies
Collaboration with Algolytics has allowed us to focus on what is crucial for us – developing our financial products. Algolytics technology has provided us with tools for quick and accurate risk assessment of our clients, enabling us to significantly increase our operational efficiency and deliver value to our clients faster.
SERGII DEMCHUK
CEO, Novalend
Test Scoring.One and discover how it can streamline the deployment and management of ML/AI processes.
Fill out the form and get access to the Scoring.One demo.
See how Scoring.One enables the deployment of predictive models in a few days instead of weeks or months and processes thousands of queries per second – for dozens of models and scenarios simultaneously.
