In the past decade, data science and machine learning (ML) have shown significant promise in addressing complex business problems. Companies across various industries are keen to leverage the advantages these tools offer for decision making. However, as more tools are adopted, complexity increases when developing and operating large-scale solutions. This complexity is amplified when scaling such processes across organizations where data, practices, and tools may lack consistency due to siloed organizational structures.

This issue underscores the importance of ML governance and adoption of MLOps practices, fostering agility, reproducibility, and standardization while enabling innovation. These practices are particularly valuable in sectors like utilities, where strict regulatory standards and safety concerns heighten the risk of data solutions. As data-driven cultures expand, governance of data and models becomes increasingly vital. In advanced organizations, data product development is democratized, facilitating the creation of cascading data products.

MLOps Foundation

In a standard data science workflow, the process involves framing the business problem, cataloging data, forming a hypothesis, and developing and evaluating a model. At this stage, a decision is made based on the model’s performance and potential business value (go/no-go decision) before moving to deployment.

The deployment phase for an MLOps project automates a machine learning model to become production ready. The model is packaged into a self-contained artifact, stored in a model registry, and deployed as a service for real-time or batch predictions. This process introduces concerns like scalability, reliability, and monitoring. Machine learning models add complexity beyond traditional software deployment methods with the need to include considerations for data drift, quality, model drift, and governance. Advanced deployment strategies (such as A/B testing) can be used to assess the model’s performance against live data.

MLOps Maturity

As utility companies are beginning to consider leveling up their data-driven cultures, it becomes important to set a solid foundation for program success. The following milestones can be used to evaluate the maturity of an analytics program:

Setting a Solid Data Foundation

Enable teams to connect securely to enterprise data sources, provide consistent mechanisms to connect to data, and develop robust data and lineage documentation.

Enable Environment for Experimentation, Collaboration, and Scale

Create a scalable environment for data scientists to collaborate, experiment, and develop ideas quickly, incorporating notebook-based environments, clusters, and other data tools.

Promote Automation and Repeatability

Create a distinct environment for hosting training and inference model pipelines to facilitate a pipeline-based ML process and provide teams with templates for batch processing or endpoint-based model consumption.

Service Hardening and Robust Change Management

Enhance model update procedures by integrating advanced automated testing, service monitoring, and drift detection tools for improved model operations.

Scale Tools Across Enterprise

Establish an IT operating model, select enabling tools, define governance standards, and engage data science teams to drive continuous improvement in ML product development.

ML Model Governance

Complexity in AI/ML systems can heighten risks, particularly in data quality, transparency, compliance, privacy, security, resource management, and ethics. Utilities must deliver critical services to the public responsibly and ethically, making governance crucial in navigating this complex landscape.

A mature ML governance program at its core exists to enable the business to set clear directives.

Clear Roles, Responsibilities, and Standards

A mature program establishes clear policies and standards for ML model management, including identifying key stakeholders, best practices for the model lifecycle, compliance adherence, and ethical considerations. This provides a comprehensive framework for data scientists and business stakeholders to follow.

Cross-Functional Collaboration

Effective ML governance involves collaboration among diverse teams across the organization. A mature program encourages cross-functional cooperation between technical data science and engineering teams, compliance specialists, and business stakeholders. This alignment ensures governance practices meet business goals while meeting compliance and ethical standards.

Continuous Improvement

Effective governance is a team effort, and good governance requires iterative refinement of policies, practices, and stakeholder collaboration to ensure long-term success in accordance with business goals and standards.

These practices contribute to a mature governance program and the ability to manage risks, maintain transparency and compliance, adhere to ethical standards, and enable data scientists to be more effective in using toolsets and frameworks.

Integrating machine learning into utility organizations offers numerous benefits in terms of safety, efficiency, and sustainability through analytics. However, it’s vital to manage the ML model lifecycle, enhance MLOps maturity, and uphold ethical standards. As capabilities expand, establish mechanisms to accelerate development, standardize practices, and enhance cross-functional capabilities to reduce project risks. Effective organizational governance and planning can mitigate potential risks associated with the introduction of new technologies.