When it comes to model implementation and execution, many companies—particularly in the retail finance sector—still rely on manually running SAS or Python scripts, or rewriting model logic as SQL and executing it through stored procedures. These approaches are often fragile, hard to maintain, and prone to human error.
Fortunately, a wide range of open-source tools now make it possible to deploy, manage, and monitor production machine-learning models in a scalable and automated way. These frameworks support modern best practices such as CI/CD, version control, automated retraining, and seamless swapping of model versions.
In this article, we explain the roles of six key technologies commonly used to build a robust end-to-end model-scoring ecosystem:
- Apache Airflow
- Apache Kafka
- Apache Flink
- BentoML
- MLflow
- Docker
Apache Airflow: Orchestrating Batch Workflows
Apache Airflow is best suited for scheduling and managing batch processes. It is a powerful workflow-orchestration platform that allows organizations to extract, transform, and load data at scale, score that data using BentoML services, and push results into downstream systems such as databases or dashboards.
Airflow's workflows ("DAGs") are written in Python, making them flexible and easy to maintain. Airflow also scales seamlessly when deployed on Kubernetes and integrates with a wide range of systems, including:
- Databases: Postgres, MySQL
- Cloud services: AWS, GCP, Azure
- Data platforms: Spark, Databricks, Snowflake, BigQuery
Workflows can be triggered in several ways:
- On a schedule (e.g., hourly, daily, weekly)
- By events, such as new data becoming available
- Manually, which is especially convenient for testing new logic
By adopting Airflow, organizations can eliminate the need for employees to manually execute scripts, while improving reliability, version control, and auditability across all batch processes.
Apache Kafka & Apache Flink: Real-Time Data Processing
For real-time systems, Apache Kafka and Apache Flink play central roles.
Apache Kafka
Kafka is a distributed event-streaming platform that allows producers to publish messages to topics, and consumers to subscribe to and process those messages in real time. It is capable of ingesting data from almost any source, in any format, with extremely high throughput.
Apache Flink
Apache Flink is designed for ultra-low-latency, high-throughput computations. It excels at real-time data transformations, making it ideal for preparing streaming data—such as transactional or application events—for immediate model scoring.
Together, Kafka and Flink form a robust real-time pipeline. Common use cases within the financial sector include:
Fraud Prevention
- Money-transfer fraud: Real-time streaming allows organizations to transform and score payment data for both inbound and outbound transactions before funds are released, reducing fraud losses.
- Card fraud: While card-transaction data often arrives from processors with a slight delay, Flink can immediately prepare this data for scoring as soon as it is received. Although payment processors often provide fraud-prevention solutions, custom machine-learning models trained on an institution's own data frequently outperform generic vendor products.
Real-Time Application Processing
Financial institutions can transform application data and generate instant credit decisions, which is especially beneficial for products targeted at new-to-bank customers who lack historical internal data.
By leveraging Kafka and Flink, organizations can introduce new capabilities: early fraud detection, real-time risk assessments, and reduced dependency on third-party scoring services.
BentoML: Serving Machine-Learning Models
BentoML is an open-source framework for packaging, serving, and deploying machine-learning models. Within the architecture described here, BentoML provides the API endpoint that receives model-ready data—transformed upstream by Airflow, Flink, or other processes—and returns model outputs such as risk scores or event probabilities.
It supports both batch and real-time inference and offers a consistent, production-grade serving environment.
MLflow: Model Tracking and Management
To support model governance, versioning, and performance monitoring, MLflow is used alongside BentoML. MLflow is an open-source platform for managing the entire machine-learning lifecycle, from experiment tracking to model registry and deployment.
In this framework, MLflow manages model artifacts and versions, while BentoML deploys the model selected from the MLflow registry.
Docker: Consistent, Scalable Deployment
All components described above are packaged and deployed using Docker containers. Containerization ensures consistent environments across development, testing, and production. It also enables horizontal scaling based on business needs, making it straightforward to increase throughput during peak periods or deploy additional services as the organization grows.
By adopting this modern, modular architecture, financial institutions can streamline model deployment, reduce operational risk, and unlock new real-time capabilities—without relying on manual processes or rigid legacy systems.
Final Thoughts
Your model deployment framework shouldn't just run models — it should deliver speed, consistency, and real-time intelligence across your entire organisation. We provide the modern infrastructure and expertise so you can move from fragile scripts to scalable, automated pipelines with confidence.
Discover how we can help you adopt industry-leading deployment frameworks with minimal disruption and maximum reliability. Schedule a consultation today and access complimentary, engineering-driven insights tailored to your operational needs.
