Common challenges of serving models in production

Deploying and hosting ML models in production often comes with numerous challenges. If you’re developing and serving just one model, you may encounter some of these challenges, but if you are developing tens, hundreds, or thousands of models, then you will likely run into the majority of these challenges and concerns.

Deployment infrastructure

Choosing the right infrastructure to host ML models, setting it up, and managing it can be complex, particularly in hybrid or multi-cloud environments. Again, Google Cloud Vertex AI takes care of all of this for us automatically, but without such cloud offerings, many companies find this to be perhaps one of the most challenging aspects of any data science project.

Model availability and scaling in production

This is an extension of deployment infrastructure management. As demand increases, our model needs to serve more predictions. The ability to scale services up and down...