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The insights available through effective machine learning models can help businesses develop customer personas, clarify marketing efforts, improve the user experience for their products and services, and much more. However, none of that can happen if an ML model isn’t carefully and correctly set up from the beginning. A flawed ML model can produce misleading outputs that may lead to costly missteps.
Most of us are familiar with the concept of “garbage in, garbage out”—that is, we know it’s important to input quality data to get quality information out. But there’s more to developing a truly effective ML model than that. It’s important to understand what even the best ML models can—and can’t—do and to be aware that it’s far from a “fix it and forget it” process. Here, 16 members of Forbes Technology Council share essential steps in the creation and maintenance of an effective machine learning model.
1. Begin With Operational Leaders’ Insights
Operational leaders understand what’s working for their business. However, this business sense isn’t scalable or repeatable to drive impact. That’s where ML models excel. While creating models, companies should start with operational leaders’ insights and then build, test and deploy the model in production. However, the models must be built for actionability to make them fully effective.
Most of us are familiar with the concept of “garbage in, garbage out”—that is, we know it’s important to input quality data to get quality information out. But there’s more to developing a truly effective ML model than that. It’s important to understand what even the best ML models can—and can’t—do and to be aware that it’s far from a “fix it and forget it” process. Here, 16 members of Forbes Technology Council share essential steps in the creation and maintenance of an effective machine learning model.
1. Begin With Operational Leaders’ Insights
Operational leaders understand what’s working for their business. However, this business sense isn’t scalable or repeatable to drive impact. That’s where ML models excel. While creating models, companies should start with operational leaders’ insights and then build, test and deploy the model in production. However, the models must be built for actionability to make them fully effective.
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