课程: MLOps Essentials: Model Development and Integration
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Assembling the team
- [Instructor] What are the best practices in putting a team together to execute an ML project? Building machine learning solutions requires a team with diverse skill sets across data engineering, data science, software engineering and operations. Assembling and heterogeneous team like this within the cost constraints of an organization is a challenge. What is the recommended team composition? There is a general myth that all team members in an ML project will be data scientists. - All an organization needs is to hire a couple of data scientists and they will do the job But that is not the case. Based on real life experience, the table below lists the percentage split between various roles in an ML project. Data scientists, who build models only constitute 20% of the team. Data engineers, who do the data processing and wrangling constitute 30%. Another 20% goes to the engineers building wrapper services and APIs around…
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