This post is co-authored with Piotr Klesta, Robert Meisner and Lukasz Luszczynski of ERGO

Artificial intelligence (AI) and related technologies are already finding applications in our homes, cars, industries, and offices. The insurance business is no exception to this. When AI is implemented correctly, it adds a major competitive advantage. It enhances the decision-making process, improves efficiency in operations, and provides hassle-free customer assistance.

At ERGO Group, we realized early on that innovation using AI required more flexibility in data integration than most of our legacy data architectures allowed. Our internal governance, data privacy processes, and IT security requirements posed additional challenges towards integration. We had to resolve these issues in order to use AI at the enterprise level, and allow for sensitive data to be used in a cloud environment.

We aimed for a central system that introduces ‘intelligence’ into other core application systems, and thus into ERGO’s business processes. This platform would support the process of development, training, and testing of complex AI models, in addition to creating more operational efficiency. The goal of the platform is to take the undifferentiated heavy lifting away from our data teams so that they focus on what they do best – harness data insights.

Building ERGO AI Factory to power AI use cases

Our quest for this central system led to the creation of AI Factory built on AWS Cloud. ERGO AI Factory is a compliant platform for running production-ready AI use cases. It also provides a flexible model development and testing environment. Let’s look at some of the capabilities and services we offer to our advanced analytics teams.

Figure 1: AI Factory imperatives

Figure 1: AI Factory imperatives

  • Compliance: Enforcing security measures (for example, authentication, encryption, and least privilege) was one of our top priorities for the platform. We worked closely with the security teams to meet strict domain and geo-specific compliance requirements.
  • Data governance: Data lineage and deep metadata extraction are important because they support proper data governance and auditability. They also allow our users to navigate a complex data landscape. Our data ingestion frameworks include a mixture of third party and AWS services to capture and catalog both technical and business metadata.
  • Data storage and access: AI Factory stores data in Amazon Simple Storage Service (S3) in a secure and compliant manner. Access rights are only granted to individuals working on the corresponding projects. Roles are defined in Active Directory.
  • Automated data pipelines: We sought to provide a flexible and robust data integration solution. An ETL pipeline using Apache Spark, Apache Airflow, and Kubernetes pods is central to our data ingestion. We use this for AI model development and subsequent data preparation for operationalization and model integration.
  • Monitoring and security: AI Factory relies on open-source cloud monitoring solutions like Grafana to detect security threats and anomalies. It does this by collecting service and application logs, tracking metrics, and generating alarms.
  • Feedback loop: We store model inputs/outputs and use BI tools, such as Amazon QuickSight, to track the behavior and performance of productive AI models. It’s important to share such information with our business partners so we can build their trust and confidence with AI.
  • Developer-friendly environment: Creating AI models is possible in a notebook-style or integrated development environment. Because our data teams use a variety of machine learning (ML) frameworks and libraries, we keep our platform extensible and our framework agnostic. We support Python/R, Apache Spark, PyTorch and TensorFlow, and more. All this is bolstered by CI/CD processes that accelerate delivery and reduce errors.
  • Business process integration: AI Factory offers services to integrate ML models into existing business processes. We focus on standardizing processes and close collaboration with business and technical stakeholders. Our overarching goal is to operationalize the AI model in the shortest possible timeframe, while preserving high quality and security standards.

AI Factory architecture

So far, we have looked at the functional building blocks of the AI Factory. Let’s take an architectural view of the platform using a five-step workflow:

Figure 2: AI Factory high-level architecture

Figure 2: AI Factory high-level architecture

  1. Data ingestion environment: We use this environment to ingest data from the prominent on-premises ERGO data sources. We can schedule the batch or Delta transfer data to various cloud destinations using multiple Kubernetes-hosted microservices. Once ingested, data is persisted and cataloged as ERGO’s data lake on Amazon S3. It is prepared for processing by the upstream environments.
  2. Model development environment: This environment is used primarily by data scientists and data engineers. We use Amazon EMR and Amazon SageMaker extensively for data preparation, data wrangling, experimentation with predictive models, and development through rapid iterations.
  3. Model operationalization environment: Trained models with satisfactory KPIs are promoted from the model development to the operationalization environment. This is where we integrate AI models in business processes. The team focuses on launching and optimizing the operation of services and algorithms.
    • Integration with ERGO business processes is achieved using Kubernetes-hosted ‘Model Service.’ This allows us to infuse AI models provided by data scientists in existing business processes.
    • An essential part of model operationalization is to continuously monitor the quality of the deployed ML models using the ‘feedback loop service.’
  4. Model insights environment: This environment is used for displaying information about platform performance, processes, and analytical data. Data scientists use its services to check for unexpected bias or performance drifts that the model could exhibit. Feedback coming from the business through the “feedback loop service’ allows them to identify problems fast and retrain the model.
  5. Shared services: Though shown as the fifth step of the workflow, the shared services environment supports almost every step in the process. It provides common, shared components between different parts of the platform managing CI/CD and orchestration processes within the AI factory. Additional services like platform logging and monitoring, authentication, and metadata management are also delivered from the shared services environment.

A binding theme across the various subplatforms is that all provisioning and deployment activities are automated using Infrastructure as Code (IaC) practices. This reduces the potential for human error, provides architectural flexibility, and greatly speeds up software development and our infrastructure-related operations.

All components of the AI factory are run in the AWS Cloud and can be scaled and adapted as needed. The connection between model development and operationalization happens at well-defined interfaces to prevent unnecessary coupling of components.

Lessons learned

Security first

  • Align with security early and often
  • Understand all the regulatory obligations and document them as critical, non-functional requirements

Modular approach

  • Combine modern data science technology and professional IT with a cross-functional, agile way of working
  • Apply loosely coupled services with an API-first approach

Data governance

  • Tracking technical metadata is important but not sufficient, you need business attributes too
  • Determine data ownership in operational systems to map upstream data governance workflows
  • Establish solutions to data masking as the data moves across sub-platforms
  • Define access rights and permissions boundaries among various personas

FinOps strategy

  • Carefully track platform cost
  • Assign owners responsible for monitoring and cost improvements
  • Provide regular feedback to platform stakeholders on usage patterns and associated expenses

Working with our AWS team

  • Establish cadence for architecture review and new feature updates
  • Plan cloud training and enablement

The future for the AI factory

The creation of the AI Factory was an essential building block of ERGO’s strategy. Now we are ready to embrace the next chapter in our advanced analytics journey.

We plan to focus on important use cases that will deliver the highest business value. We want to make the AI Factory available to ERGO’s international subsidiaries. We are also enhancing and scaling its capabilities. We are creating an ‘analytical content hub’ based on automated text extraction, improving speech to text, and developing translation processes for all unstructured and semistructured data using AWS AI services.

Categories: Architecture