Predictive modelling and machine learning may provide tremendous value for organizations that leverage these techniques as an integral part of their business model. Many organizations spanning both the public and private sectors have adopted a data-driven business strategy where insights derived from either comprehensive data analyses or the application of highly complex machine learning algorithms are used to influence key business or operational decisions. Although there are many organizations leveraging machine learning at scale, and the variety of use cases abound at a high level, the overall machine learning life cycle has a common structure among all organizations irrespective of the specific use case or application. Specifically, for any organization leveraging data science at scale, the machine learning life cycle is defined by four key components: Model Development, Model Deployment, Model Monitoring, and Model Governance.

When approaching a project to build a data-driven decision-making deployment the following key aspects should be taken into consideration:

• Build your ML system/platform taking into consideration the requirements from all your stakeholders, including model compliance teams, businesses, and the end customer.
• Align on the architecture approach for the ML systems that unlock required capabilities in your organization.
• Select the appropriate tools for model deployment; model containerization is the most common method of deploying ML models.
• Perform thorough model validation tests on the deployment containers and packages.
• Independent code base replicating the entire data processing and deployment scoring pipeline can serve as a ground truth.
• Well-documented quality control measures for upstream and downstream processes, contingency plans during production issues, and logging can be helpful.