Council Post: Accelerating Insurance Innovation: How MLOps Is Streamlining Data Science And ML Model Scaling

Council Post: Accelerating Insurance Innovation: How MLOps Is Streamlining Data Science And ML Model Scaling

Saurav Basu is Founder and President at Exavalempowering digital transformation in insurance, financial services & healthcare industries

Recent advancements in technology, data availability and changing consumer preferences have opened new opportunities for insurers to leverage data and insights. This allows them to enhance operations, offer personalized experiences and compete in innovative ways. Insurers are increasingly adopting AI-driven improvements, including utilizing DataOps and MLOps in order to enable effective iteration and progress tracking throughout the life cycle of algorithmic models.

However, as a consulting professional in the insurance industry, I have found that traditional MLOps often fall short in accelerating business results across the data science life cycle, as they focus on model development tools and ad hoc deployment. To address this, I believe a more holistic approach called enterprise XOps/MLOps is needed.

In this article, I will discuss the challenges of scaling data science, discuss the role of enterprise MLOps in overcoming these challenges, and provide insights on integrating MLOps for success in the data-driven insurance industry.

Why is scaling machine learning in the insurance industry difficult?

According to Gartner“Half of current finance artificial intelligence (AI) deployments will be either delayed or cancelled by 2024.” Despite the reasonable progress made by insurers and other finance industry players in laying the groundwork for AI, the challenges of scaling up the solutions may make many CFOs rethink their AI strategy. Gartner also highlighted how the absence of a truly functional automated process to scale AI/ML operations is preventing insurers from meeting the expected benefits outlined in business cases for deploying these technologies.

This is a direct result of the lack of focus on using developed AI models at an enterprise level. Much of the focus has instead been given to developing various AI models. This focus has resulted in multiple challenges and barriers for data scientists, like limited availability and quality of data, complex data structures, model validation and deployment, and legacy systems and processes.

To address these, I recommend shifting focus toward developing advanced infrastructure management and DevOps automation to operationalize ML model development and deployment in the insurance industry.

How do enterprise MLOps help insurers scale data science and machine learning?

Enterprise MLOps is a type of platform designed to enable insurers to manage and scale their data science efforts. These platforms focus on four areas:

1. Managing Data

This data management discipline aims to break down data silos that prevent collaboration among data scientists, who often use different tools and processes to work. This can lead to challenges in areas like governance, auditability and reproducibility. To address these issues, enterprise MLOps provides tools such as data governance, feature store solutions, model development tools, workflow and model orchestration, deployment tools, and monitoring tools.

2. Developing Models

When data scientists don’t have access to the right tools and infrastructures, they may waste time building their systems instead of focusing on their work. Data orchestration is a new approach that helps data scientists access all the data they need in one place without having to manually move around the data, making it easier for them to do their work and improve the quality of their models. Other benefits include shared resources, centralized access to data, and better governance and security.

3. Deploying Models

During the model deployment stage, models are put into production and used by the business to generate value. However, this phase can be time-consuming and require a lot of support from IT and software developers. Enterprise MLOps platforms can streamline the deployment and change management process, allowing data scientists to deploy models without relying on IT support.

4. Monitoring Business Outcomes

In this stage, enterprise MLOps can help ensure that models are continually learning, rebuilding and performing as expected, and prevent problems like “model drift,” or improper use of models. A machine-learning monitoring platform can help improve the observability of a project and troubleshoot production AI.

How can insurers get started with enterprise MLOps?

To ensure the success of an enterprise MLOps platform, it’s important to take a strategic approach. Here are some steps to keep in mind while integrating.

1. Define insurance machine learning use cases. These should be based on business goals and data availability. Early use case definition helps identify operational requirements such as automated deployment, model updates, connection to business rules and access to third-party data.

2. Create a knowledgeable team. When choosing who will be in charge of integrating your enterprise MLOps, I recommend building a multidisciplinary team of data scientists, data engineers, software developers and business analysts with the right technology expertise. Enterprises often don’t design, obtain and implement MLOps as fast as they should because they don’t use the right tools and approaches to ensure AI program success, so consider also working with insurers who can bypass this by establishing an MLOps platform that supports the entire life cycle of data science, model versioning, reproducibility and automated testing. This should ensure that MLOps is established and that the proper tools are implemented to automatically support AI development and model deployment.

3. Determine metrics and processes for your systems. These metrics and processes should track performances and identify areas of improvement, including those related to data quality, model accuracy and operational metrics such as uptime and latency.

4. Create a dynamic sandbox environment. Use this to allow your business’s team members and data scientists to experiment and analyze different models, without breaking production systems, until they get their model right.

5. Make a continuous deployment pipeline to automate the deployment of machine learning models. This is needed to ensure consistency and reliability, and it allows for rapid development, management, testing, deployment and running of models efficiently and with lower risk and fewer delays.

Conclusion

The software development industry has been built on age-old engineering practices for development, version control, testing, security and deployment, using tools mainly designed for IT organizations. However, organizations that are embracing AI models need a new set of tools and techniques to streamline ML model development, automated model testing, security, version control and automated deployment in a reliable way while fostering a collaborative and highly experimental culture for data scientists. By using enterprise MLOps principles and modern DevOps tools, organizations can build and deploy elastic models at scale while ensuring reliability, security and timely deployment.


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