Episode Title : Focus on Business Use Cases First – Greg Coquillo
With so much hype about machine learning, people think every problem needs to be solved and can be solved with ML. In this episode, Greg Coquillo goes over the importance of separating use cases where ML can be beneficial and use cases where just a rule-based approach might work. Greg talked about re-learning statistics, probability, and data science to apply strategically in his job. Greg is taking his team to using ML where it makes sense such as classification so ML can do lot more and lot faster than humans. The episode also covered the importance of transparency in AI/ML and how documentation is key to driving that transparency.
02:29: Machine Learning helps with classification as humans can’t do as accurately. And classification is very important because a mis-classified object, it may not go through all compliance tasks. And it could be costly as well.
06:04 (HEADLINER): Not every process needs machine learning. Sometimes, a rule based algorithm is just fine. So it is important to separate the processes that need ML and others that don’t.
10:07 (HEADLINER): In identifying use cases best for AI, start small. Then understand the pain points in their specific department. If ML is a solution for that problem, understand where the data is coming from.
12:42 (HEADLINER): Started learning data science because of curiosity. Greg mentioned that he is re-learning what he has learnt in college such as statistics and probability.
16:59: To make a real business impact, both technical teams and business teams need to stretch and understand each other. Given that data science is probabilistic, business needs to explain if they are comfortable with that uncertainty.
20:32 (HEADLINER): Driving transparency is very important in data science and documenting and enforcing the discipline of documenting is crucial.
Resources mentioned in this episode:
Connect with Greg: https://www.linkedin.com/in/greg-coquillo/
Podcast website: https://DataTransformersPodcast.Com
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