Deploying and Monitoring Machine Learning Models

In previous chapters, we learned a lot about different models and techniques. Understanding the concepts and building a machine learning model is only the beginning of the journey toward realizing its true value. The successful deployment and ongoing monitoring of these models are crucial to ensuring their effectiveness and reliability in real-world scenarios.

Ensuring that a model performs optimally, seamlessly integrates with existing systems, and adapts to evolving requirements requires a comprehensive understanding of the deployment process and the associated considerations. In the context of the Qlik platform, most of the typical pain points are handled by the platform itself and the design of the components, but there are still things we have to bear in mind.

Once a machine learning model is deployed, it is vital to continuously monitor its performance to identify potential issues, maintain accuracy, and safeguard against...