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6 Mistakes To Avoid While Training Your Machine Learning Model

Updated: Apr 23

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 machine learning model is a complex task that requires extensive knowledge and skills, along with practical experience, to ensure that the model performs successfully in a wide range of scenarios.

One of the most critical factors in AI development is acquiring high-quality training data, especially for computer vision-based AI models. The collection and use of training data during the training phase is crucial for developing a robust and accurate model. Any errors made during the training process can result in the model's inaccurate performance, which can be catastrophic, particularly in industries such as healthcare or self-driving cars.

During the training process, multiple stages of activities are performed to utilize the training data effectively and ensure the best possible outcomes. However, there are several common mistakes that developers often make while training AI models that can significantly impact the model's accuracy and success. By avoiding these mistakes, developers can increase the chances of creating a successful AI model that performs accurately in real-world scenarios.

#1 Using Unverified and Unstructured Data

The use of unverified and unstructured data is a common mistake made by machine learning engineers during AI development. Unverified data may contain errors such as duplication, conflicting data, lack of categorization, and other data issues that could create anomalies during the training process.

Before using data for machine learning training, it's important to carefully examine the raw data set and eliminate any unwanted or irrelevant data. This will help your AI model work with better accuracy.

#2 Using the Already Used Data to Test Your Model

Reusing data that has already been used to test the model should be avoided. Using the same training data on models or AI-based applications could lead the model to be biased and derive results that are the result of their previous learning.

To test the capabilities of your AI model, it's important to use new datasets that were not used earlier for machine learning training.

#3 Using Insufficient Training Data Sets

To make your AI model successful, you need to use the right training data so that it can predict with the highest level of accuracy. The 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 industry, the fields of the requirement of training data vary. For deep learning, you need more quantitative as well as qualitative datasets to make sure they can work with high precision.

#4 Making Sure Your AI Model is Unbiased

Just like humans, machines could also be biased due to various factors such as age, gender, orientation, and income level, 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 the process.

#5 Relying on AI Model Learning Independently

While you need experts to train your AI model, using a huge amount of training datasets, it's important to ensure 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 a 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 make the machine learning models more reliable among the end-users.

Training data accurately with the highest level of precision is highly crucial in making AI successful and working with the best level of accuracy in various scenarios. If your data is not properly labelled, it will affect the performance of the model.

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