Using Machine Learning and NLP techniques, infer what the customers are not buying for the supplier and target these gaps with personalized sales promotions. The product is used to assist Sales Manages in increasing basket sizes for their customers.

• Starting from locally running MVP, move to a production solution running on Kubernetes on Cloud.
• Lead implementation of incremental performance improvements, to fit the demands of production load.
• Spearheaded the implementation of modern ML application deployment technologies and frameworks to the project.
• Automate data ingestion, model training, evaluation and deployment.
• Unit, integration and end-to-end test automation.

In stock exchange companies, the volume of collected data in- creases daily. Also market value for these data increases. Considering these dynamic features of the project scope, we created an architecture where historical data could be provided to users and allowing them to enrich this data in their own scope.

• Creating an architecture for fast and cost efficient access of historical data.
• Development of data transformation strategy to persist extensions of pre-calculation layer.
• Development of schema evolution strategy.

Project aims to create a decoupled architecture to process and persist data on daily basis, collected into GCP. Also, in the scope of this project, we defined the CI/CD strategy for all the components of the data pipeline.

• Create ingestion layer to make data available in BigQuery on daily basis.
• Create the CI/CD pipeline for all components.
• E2E-Integration testing (design, data generation, execution)
• Documentation

Create a self service framework for ingesting data from different sources and preprocessing, keeping in mind requirements from end consumer of these data. The framework is designed to work in a cloud native environment. A set of HTTP Rest APIs was made available to users to provide necessary functionality around on-demand AWS EMR clusters.

• Development of data transformation and aggregation algorithms (Scala) to facilitate new load types.
• Development of the metadata management and data transformation orchestration application, written in Python, to support new destination environments.
• Development of unit, integration and end-to-end test for all components of the application, Scala and Python.