摘要： While training the AI model, multi-stage activities are performed to utilize the training data in the best manner, so that outcomes are satisfying. So, here are the 6 common mistakes you need to understand to make sure your AI model is successful.
Developing an AI or a ML model is not a child’s play. It requires lot of knowledge and skills with enriched experience to make the model work successfully in multiple scenarios.
Additionally, you need high-quality computer vision training data especially to train your visual perception based AI model. The most crucial stage in AI development is acquiring & collecting the training data and using this data while training the models.
Any mistake while training your model will not only makes your model perform inaccurately but also could be disastrous while making crucial business decisions, especially in certain areas such as Healthcare or Self Driving Cars.
While training the AI model, multi-stage activities are performed to utilize the training data in the best manner, so that outcomes are satisfying. So, here are the 6 common mistakes you need to understand to make sure your AI model is successful.
Using Unverified and Unstructured Data
The use of unverified & unstructured data is one of the most common mistakes machine learning engineers do in AI developments. The unverified data might have errors such as duplication, conflicting data, lack of categorization, data conflict, errors and other data issues that could create anomalies during the training process.
Hence, before you use the data for your machine learning training, carefully examine your raw data set and eliminate the unwanted or irrelevant data, helping your AI model work with better accuracy.
Using the Already Used Data to Test Your Model
One should avoid re-using the data that has already been used to test the model. Hence, such mistakes should be avoided. For example, if someone has already learned something and has applied that knowledge to their area of work; using the same learnings on another area of work could lead to one being biased and repetitive in inferencing.
Similarly, in machine learning, the same logic applies, AI can learn with the bulk of datasets to predict the answers accurately. Using the same training data on Models or AI based applications could lead the model to be biased and derive results which are the resultant of their previous learning. Hence, while testing the capabilities of your AI model, it is very important to test using the new datasets that were not used earlier for machine learning training.
Using the Insufficient Training Data Sets
To make your AI model successful you need to use the right training data so that it can predict with highest level of accuracy. Lack of sufficient data for training is one of the primary reasons behind the failure of the model.
However, depending on the type of AI model or industries, the fields of requirement of training data is varied. For deep learning, you need more quantitative as well as qualitative datasets to make sure it can work with the high precision.
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