Examining ML and data engineering roles
In previous chapters, we have used the term ML practitioner as a blanket term for any person responsible for automating the ML process. Within the context of the MLSDLC process, we typically see this role split into two distinct functions, namely the following:
- Data scientist: The data scientist is primarily responsible for building, training, and tuning an ML model that meets the business requirements of the use case.
- ML engineer: Among numerous responsibilities, the ML engineer is primarily responsible for designing the overall ML system to support the model, managing the appropriate datasets for model training, and ensuring the final ML application addresses the business requirements for the use case.
However, for the sake of the ACME application example, we will group these two functions under the banner of the ML team, with the following diagram highlighting how this team fits into the MLSDLC process:
