Customer
The Tier-2 Eastern European bank is a full-service institution with an impressive AUM of over $50 billion, offering a complete portfolio of banking services for both private individuals and companies.
Challenge
Low time-to-market for new and amended credit risk models. High operational risk and effort associated with deployment of ML models to production environments and data streams. Poor monitoring of model performance in production.
Project Results
The new MLOps Platform enabled quick implementation of ML-models of any complexity into business processes and eliminated operational risk associated with deployment of models into production environments. The deployed architecture of the platform automates the management of models, experiment tracking, code and data versioning as well as model verification, deployment and serving.
Business Value
Dramatic reduction of time-to-market for new ML-models. Improved performance of model-driven services thanks to the new state-of-the-art deployment architecture running on Kubernetes cluster. Simplified onboarding of new data science team members, which now takes no more than 1 day.
Tech Stack
JupyterHub, Tensorflow, Scikit-learn, MLFlow, Apache Airflow, Apache Spark, Cloudera CDP, Kubernetes, Jenkins, ArgoCD.
The Tier-2 Eastern European bank is a full-service institution with an impressive AUM of over $50 billion, offering a complete portfolio of banking services for both private individuals and companies.
Challenge
Low time-to-market for new and amended credit risk models. High operational risk and effort associated with deployment of ML models to production environments and data streams. Poor monitoring of model performance in production.
Project Results
The new MLOps Platform enabled quick implementation of ML-models of any complexity into business processes and eliminated operational risk associated with deployment of models into production environments. The deployed architecture of the platform automates the management of models, experiment tracking, code and data versioning as well as model verification, deployment and serving.
Business Value
Dramatic reduction of time-to-market for new ML-models. Improved performance of model-driven services thanks to the new state-of-the-art deployment architecture running on Kubernetes cluster. Simplified onboarding of new data science team members, which now takes no more than 1 day.
Tech Stack
JupyterHub, Tensorflow, Scikit-learn, MLFlow, Apache Airflow, Apache Spark, Cloudera CDP, Kubernetes, Jenkins, ArgoCD.