Scientific Modules from AIS Sokol is a platform for the development and implementation of solutions based on artificial intelligence and machine learning. It covers the full cycle of working with data: from data collection and preprocessing to model training, deployment and monitoring in a productive environment. The product allows you to speed up the creation of predictive and analytical systems, increase the accuracy of models and reduce the time to market for solutions.

Main features and functionality

• Data collection and preprocessing
Automated ETL pipelines for data purification, normalization and augmentation.
Support for structured and unstructured sources (CSV, JSON, databases, logs).

• Model development and training
Classification, regression, clustering, and time series analysis.
Hyper parametric optimization (Grid Search, Random Search, Bayesian).

• Experiment management
MLflow Experiment Tracker: storing metrics, artifacts, and model versions.
Comparison and visualization of the results of different launches.

• Deploying models
Packaging in Docker containers and deployment in Kubernetes.
Automatic generation of REST endpoints for online predictions.

• Monitoring and additional training
Collection of quality metrics (accuracy, precision, recall, RMSE, etc.) in real time.
Detecting data drift and starting the retraining processes.

Technologies used

• Language: Python
• Scientific libraries: NumPy, pandas, Scikit learn
• Deep learning frameworks (as required): TensorFlow, PyTorch
• Experiment management: MLflow, DVC
• Workflow orchestration: Apache Airflow, Prefect
• Containerization and orchestration: Docker, Kubernetes
• Data warehouses: PostgreSQL, MongoDB, HDFS, S3
• Visualization and dashboards: Matplotlib, Plotly, Dash

Target audience and customer segments

• Financial institutions: credit scoring, forecasting market risks, algorithmic trading.
• Analytical centers: macroeconomic modeling, marketing analytics, customer insights.
• R&D departments: scientific research, new product development, complex modeling.
• Industrial enterprises: predictive maintenance of equipment, optimization of production processes.

Development and integration opportunities

• Deep Learning and AutoML
Implementation of AutoML modules for automatic selection of algorithms and hyperparameters.
Support for graph networks and recommendation systems.

• Edge AI
Compact models for deployment on IoT devices and edge servers.
• Cloud platforms
Integration with AWS SageMaker, Azure ML, Google AI Platform for cloud training and hosting.
• BI systems and visualization
Export results and metrics to Power BI, Tableau, ElasticSearch for end-to-end analytics.
• Integration with corporate systems
Connectors for ERP/CRM, Data Lake, BI platforms and monitoring systems.

Conclusion

AIS Sokol Scientific modules platform let the companies quickly and reliably implement AI/ML-based solutions, increasing the accuracy of forecasts and the efficiency of business processes. Flexible architecture, complete set of tools and ready-made integrations ensure scalability, transparency and long-term maintenance of models in any infrastructure.