Struggling to build an effective training data strategy for Machine Learning? Get some effective tips in this insightful article where Vatsal Ghiya, CEO and co-founder of Shaip has shared some insightful tips on how to build a training data strategy for Machine Learning(ML).
The Key Takeaways from Article Are:
- Unlike other services or solutions, AI models don’t offer instant applications and immediately 100% accurate results. These results and innovations get more evolved only after the addition of quality data. It’s important for the ML model to learn a day in and out to ultimately become the best at what it is supposed to do.
- But, before estimating the amount of time needed to spend on building an ML model, it’s vital to decide on the amount of money your business could invest in training your model. Moreover, the quality of data eventually decides the performance of the Machine Learning model.
- And most of the time the data collected is raw and unstructured. To make it understandable data annotation must be consistent and accurate throughout to prevent skewing of results.
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