Mistakes to Avoid When Training Your Machine Learning Model

When training a machine learning model, you need high-quality training data. The most crucial stage in AI development is acquiring the training data and how to use this data while training the models. Any kind of mistake while training your model will not only make your model a failure but can be disastrous if used in making crucial decisions.

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 you need to understand what mistakes need to be avoided to make sure your AI model is successful.  

Mistakes to Avoid While Training Your AI Model

The use of unverified data is one of the most common mistakes machine learning engineers do in AI developments. The unverified data might have mistakes, duplicacy, conflicting data, lack of categorization, data conflict, errors, and other data that are not required for creating 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.  

1. Using Already Used Data to Test Your Model  

If you are re-using the data to test the model that has been already used you need to avoid such mistakes. For example, if someone has learned anything or given to study and test his learning capability, one is re you put the same set of questions, the person can easily give an accurate answer.

Similarly, in machine learning, the same logic applies, AI can learn with the bulk of huge datasets to predict the answers accurately. Hence, while testing the capabilities of your AI model, it is important to test use the completely new datasets that were not used earlier for machine learning training.    

2. Using Insufficient Training Datasets

To make your AI model successful you need to use the right training data so that it can predict at the highest accuracy level. Lack of sufficient data for training is one of the leading reasons behind the failure of the model.

However, depending on the type of AI model or industries and fields the 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 highest accuracy.

3. Developing a Biased AI Model

It is not possible to develop an AI model that can give a hundred percent accurate results in various scenarios. Just like humans, machines can also be biased, which might be due to various factors like age, gender, orientation, and income level, etc., which can affect the results one way or another. 

Here you need to minimize this using statistical analysis to find out how each personal factor is affecting the data and AI training process for better accuracy.  

4. Relying on AI Model Learning Independently

You need experts to get trained 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 like; is your data sourced from a trustworthy reliable source, if your AI covers a wide demographic, and is there anything else affecting the results.

5. Not Using the Properly Labeled 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 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 is crucial in making the AI successful and work with the best level of accuracy in various scenarios. If your data is not properly labeled, 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 labeled data is another challenge for AI companies while training the model. But there are many companies offering data labeling for machine learning and AI.

Further Reading

Author’s Bio

Mr. Roger Brown is the subject knowledge expert who possesses a deep interest in reading and writing about AI and machine learning-related topics with expertise in creating useful insights about the role and importance of training data while developing AI-based models. In this article, the author has tried to cover the points that can help readers to get to know what are the things that AI developers need to avoid while training such models.

This UrIoTNews article is syndicated fromDzone