Bank Risk Intelligence Engine Relies on MLOps and Data Analytics Tools

This article details the daily tasks of a Risk Data Analytics professional within a modern bank, focusing on the MLOps pipeline. The role involves utilizing tools like Python, SQL, and Jupyter Notebooks to manage and analyze data, deploy machine learning models on platforms such as AWS SageMaker, and ensure smooth integration through CI/CD practices with tools like Git, Docker, and Kubernetes. The narrative highlights the practical application of these technologies in building and maintaining a bank's risk intelligence engine. AI

IMPACT Details the practical application of MLOps and data analytics in a financial institution's risk intelligence engine.

RANK_REASON Article describes the day-to-day use of MLOps tools and practices within a specific industry context, rather than a new release or significant industry event.

Read on Medium — MLOps tag →

• AWS
• Bank of America
• Ci Cd
• Dataiku
• Docker
• Git
• Jupyter Notebooks
• Kubernetes
• MLOps
• Python
• Risk Data Analytics
• sagemaker
• SQL

• product
• infra

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