Deploying and monitoring a data product

Finally, your team is at the stage of deploying the model to production. This should be the aim of every successful machine learning or artificial intelligence product project, but it must be done with care. There are several steps and best practices to follow:

• Integration: Integrate the model into the broader system architecture. This involves ensuring the model can communicate with other components of the system, such as databases, APIs, and user interfaces.
• Deployment infrastructure: Establish deployment processes and infrastructure. This includes setting up the necessary servers, containers, or cloud services to host the model. Automation tools such as Docker, Kubernetes, and cloud-specific services can streamline this process.
• Online testing: Alongside offline evaluation and testing, an important process before deploying to production is online testing – that is, testing the system on real, live data before deployment...