MLOps Best Practices - KDnuggets

It is now well recognized, across multiple industries, that predictive modeling 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 (see Figure 1). Most data scientists are well versed in the model development part of the machine learning life cycle and have a high degree of familiarity with complex data queries (e.g., SQL), data wrangling, feature engineering, and algorithm training.