6 mistakes to avoid while Training your Machine Learning Model

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This post was originally published by KD Nuggets

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.

Cogito 6 Mistakes Avoid

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.

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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.

 

#1 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.

 

#2 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.

 

#3 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.

 

#4 Making Sure Your AI Model is Unbiased

 
It is not possible to develop an AI model that can give a hundred per cent accurate results in various scenarios. Just like humans, machines could also be biased due to various factors such as age, gender, orientation, and income level etc., which could affect the results one way or another. Hence, you need to minimize this by using statistical analysis to find how each personal factor is affecting the data and AI training data in process.

 

#5 Relying on AI Model Learning Independently

 
Though, you need experts to train in your AI model, using a huge amount of training datasets. But if AI is using the repetitive machine learning process that needs to be considered while training such models.

Here, as a machine learning engineer, you need to make sure that your AI model is learning with the right strategy. To ensure this you must frequently check the AI training process and its results at regular intervals to get the best outcomes.

However, while developing the machine learning AI, you need to keep asking yourself important questions such as; is your data sourced from a trustworthy reliable source? Does your AI cover a wide demographic and is there anything else affecting the results?

 

#6 Not Using the Properly Labelled Datasets

 
To achieve the winning streak while developing an AI model through machine learning you need a well-defined strategy. This will not only help you to get the best outcomes but also to make the machine learning models more reliable among the end-users.

Though, mentioned above are the key points you need to keep in mind while training your model. But training data accurately with highest level of precision is highly crucial in making the AI successful and work with the best level of accuracy in various scenarios. If your data is not properly labelled it will affect the performance of the model.

If your machine learning model is Computer Vision-oriented, to get the right training data, image annotation is the precise technique to create such datasets. Getting the right labelled data is another challenge for AI companies while training the model. But there are many companies offering data labeling for machine learning and AI

This post was originally published by KD Nuggets

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