Summary
In this chapter, we covered the basics of MLOps, the different deployment approaches on Databricks, and their reference architectures.
Selecting a model deployment approach should be based on your team’s proficiency in implementing DevOps processes for ML projects. It’s important to acknowledge that there is no universal solution as each approach we have discussed has its own advantages and disadvantages. However, it is possible to create a customized hybrid ModelOps architecture within the Databricks environment.
By considering your team’s strengths and expertise, you can determine the most suitable deployment approach for your project. It’s essential to assess scalability, maintainability, ease of deployment, and integration with existing infrastructure. Evaluating these aspects will help you make an informed decision and optimize the model deployment process.
In Databricks, you have the flexibility to tailor your ModelOps architecture...
