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How to Put Machine Learning to Work
The most obvious stakeholder is the business unit for which machine learning takes place: the HR department, for example, in the case of a re-analysis project.
Fremont, CA: Putting machine learning to practical business use is difficult. Here's the hope that these tips will help make your journey a little easier.
Defining the Project
Create a project charter so that everyone can be aligned from the outset. Identify the objectives, risks and opportunities of the project as specifically as possible.What's the team trying to do? What is the current process that is being modified? What kind of team is responsible for delivering? How long is this expected to take?
By its nature, the ML project can change over time. While it may be the case that the team will need to change its objectives explicitly in line, it is good to have at least written agreement on what they were when the project started.
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Build Broad Buy-Among Stakeholders
If you do not need to operate under the radar, it is wise to engage as many relevant stakeholders as possible to help you vet and carry out your initial mission.While this may seem to be a slow start to the process, failure to take account of certain perspectives could ultimately bring the project to a quick end. The difference between proof of concept and practical implementation can be the involvement of all the right stakeholders.
The most obvious stakeholder is the business unit for which machine learning takes place: the HR department, for example, in the case of a re-analysis project. It is clear that although they are not ML experts, their guidance is essential, as it is their team that will ultimately use the product and judge its usefulness.Leaders of this unit are likely to be involved in the project from the kick-off stage. They will attend weekly meetings and work closely with data scientists and engineers.