About Hayat Finans Bank
Hayat Finans Bank, Turkey's First Digital Bank, aimed to improve processes and accelerate machine learning deployment pipelines for its newly established artificial intelligence department.
Project Scope and Objectives
Hayat Finans Bank's newly established artificial intelligence department faced significant challenges in developing and deploying machine learning models. The team struggled with creating an efficient Python development environment, encountering frequent conflicts between Python libraries. Due to the bank's strict security policies, they were unable to automatically download and manage the necessary libraries, further complicating the development process. Additionally, the department lacked a robust system for tracking and managing machine learning models, making it difficult to monitor versioning, performance, and deployment pipelines. These challenges hindered the team's ability to streamline workflows and deliver models efficiently, highlighting the need for a centralized, scalable solution.
Challenges
Hayat Finans Bank's newly established artificial intelligence department faced several key challenges:
Python Development Environment
The team struggled with managing Python libraries, frequently encountering dependency conflicts that disrupted the development process. Due to the bank's strict security policies, they were unable to automatically fetch and install required libraries, further delaying workflows.
Model Tracking and Versioning
The department lacked a robust system for tracking, managing, and versioning machine learning models. This made it difficult to monitor performance, ensure reproducibility, and automate deployment pipelines, which are critical for scaling operations.
Collaboration and Efficiency
Without a centralized development environment, collaboration between team members was inefficient. This led to duplicated efforts, slower iteration cycles, and reduced productivity in both data engineering and machine learning tasks.
Data Governance and Accountability
The department did not have a structured system to track who created a document, which teams had access to it, or how it was being utilized. This complete absence of accountability and transparency hindered effective collaboration and posed compliance challenges, particularly in the highly regulated banking environment.
Model Deployment Automation
Deploying machine learning models was a manual and time-consuming process, increasing the risk of errors and inconsistencies. The lack of automation also delayed the integration of new models into production systems.
Implemented Solution
To address the challenges faced by Hayat Finans Bank's artificial intelligence department, a comprehensive and scalable solution was developed:
Centralized Python Development Environment
A JupyterHub-based environment was implemented to provide a centralized platform for Python development. Kernels were defined for different teams, preventing conflicts between Python libraries used by various departments. The system was integrated with the bank's internal software repository, enabling controlled access to required Python libraries without compromising security policies. Furthermore, the default user-centric approach of JupyterHub was adapted to a department-centric model, allowing team members within the same department to access and review each other’s work seamlessly.
Model Tracking and Versioning
MLflow was deployed as a tracking system to manage the lifecycle of machine learning models, from development to deployment. This included version control, performance monitoring, and reproducibility, ensuring that the models could be efficiently managed and updated.
Interactive Model Testing and Documentation with Streamlit
A Streamlit-based platform was developed to document machine learning models and enable interactive testing. This platform incorporated data contracts, allowing the team to track who created a document, which team shared it, and how it was utilized. This enhanced governance, accountability, and compliance while fostering better collaboration across teams.
Automated Model Deployment
Kubernetes was used to create an automated and scalable deployment pipeline. This system streamlined the process of deploying machine learning models into production, ensuring consistency and minimizing errors.
Collaboration and Workflow Optimization
By integrating JupyterHub, MLflow, and Streamlit, a centralized ecosystem was created that optimized workflows and improved collaboration between data engineering and business teams. The department-centric JupyterHub model further facilitated teamwork within the same department, while the interactive Streamlit platform allowed business users to provide real-time feedback, accelerating the iteration cycle.To address the challenges faced by Hayat Finans Bank's AI department, a centralized Python environment was implemented using JupyterHub, enabling seamless collaboration within departments while ensuring secure access to necessary libraries. MLflow was deployed to manage machine learning models throughout their lifecycle, providing version control and performance tracking. A Streamlit-based platform was developed to document and test models interactively, enhancing governance and collaboration through data contracts. Kubernetes was utilized to automate and scale model deployment, ensuring consistency and minimizing errors. This integrated approach streamlined workflows and strengthened collaboration across teams.
Results
Success Metrics
Machine learning models were deployed faster and more reliably with the implementation of automated pipelines.
Centralized tracking systems improved version management and ensured reproducibility.
Collaboration within departments was enhanced using JupyterHub’s department-centric approach, enabling teams to share and review work seamlessly.
Added Value
The integration of data contracts provided accountability and transparency in documentation and model sharing, ensuring compliance with the bank’s governance requirements.
Interactive testing and documentation through Streamlit encouraged faster feedback cycles and better alignment with business goals.
Automated workflows reduced operational inefficiencies, allowing teams to focus more on strategic tasks and innovation.
Overall Evaluation
This project marked a significant milestone in Hayat Finans Bank's journey toward building a modern MLOps ecosystem. By addressing critical challenges such as efficient model tracking, automated deployments, and governance through data contracts, the project delivered a scalable and robust platform for machine learning operations.
The integration of tools like JupyterHub, MLflow, and Streamlit not only streamlined workflows but also empowered teams with the ability to collaborate effectively and innovate faster. The automated pipelines and structured documentation processes significantly reduced operational overhead, enabling the department to focus on driving strategic AI initiatives.
This successful implementation of MLOps principles has not only transformed the artificial intelligence department’s operations but also established a foundation for future innovations. The collaboration with Hayat Finans Bank has strengthened trust and laid the groundwork for a long-term partnership aimed at further advancing AI-driven solutions.