10 minutes read
Our retail client (one of the global top 3) collects and processes data from their online store on-premises. Our client’s continuous growth in customer data posed a potential threat to business growth since the remaining solution showed processing limitations.
Migrating to a modern, advanced infrastructure was imperative for handling and training complex ML models, enhancing the online customer experience, and optimizing sales strategies.
Suitable machine learning methods were crucial to managing big data and meeting business requirements. The existing batch learning approach was suitable for regularly received data. However, for our client’s situation, where data was abundant and increased in frequency, scalability and volume became critical factors.
Therefore, they required a solution with greater elasticity. The hybrid model proved to be the best solution. This was the time our client reached out to VirtusLab.
VirtusLab provided the client with a manageable setup for data processing, utilizing Azure Machine Learning service, ETL pipelines, and feature engineering. The hybrid cloud and batch offline processing strategy enabled our client to train ML models on data from 5 million customers, with real-time adaptability to customer behavior.
Additionally, the incorporated feedback loop allowed for continuous learning, supporting the client’s sales optimization strategy by making real-time predictions based on retained and new customer behavior data.
The new hybrid platform provided the retailer with numerous benefits, including:
Languages: Python, Java
Databases: Hive
Eventing platform: kafka, Flink
Infrastructure: Azure, Azure DevOps, Terraform, Terragrunt, CHEF, GitHub Actions
Modeling: TensorfFlow, scikit-learn, Azure Machine Learning