Vatsal Ghiya, CEO and co-founder of Shaip has 20 years of experience in offering healthcare AI solutions for better patient care. In this guest feature, he discussed the reason why Machine Learning Project fails and what to keep in consideration to make it a success.
The Key Takeaway from the Article is
- If you are not aware of the way you are going forward with the new technology trends, the whole process might go awry. As per VentureBeat, around 87% of AI projects fail due to many intrinsic factors. And these failures also cost huge loss of money on the business part.
- The reason because of these ML projects fails is because of a lack of expertise, subpar data volume & quality, erroneous labeling, lack of proper collaboration, dated data strategy absence of efficient leadership, and unpleasant data bias.
- While there could be many reasons that ML projects get failed, but it’s important to keep all the pointers must be kept in consideration if you are onto implementing ML models into your organization. Hence, it is advisable to get a credible end-to-end service provider for ML project handling and get better accuracy and efficiency.
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