machine-learning-mistakes
Programming

Avoid These 6 Mistakes When Training Your Machine Learning Model

  •  
  •  
  •  
  •  
  •  
  •  

Multi-stage tasks are carried out while training the AI model in order to make the best use of the training data and achieve satisfactory results. So, here are six popular blunders to avoid in order to ensure the performance of your AI model.

Creating an AI or ML model is not a simple task. To make the model work in different contexts, it takes a lot of knowledge and skills, as well as a lot of experience.

You’ll also need high-quality computer vision training data, especially for your visual perception-based AI model. Obtaining and collecting training data, as well as using this data when training models, is the most important stage in AI development.

Any error made when training your model would not only cause your model to perform incorrectly, but it could also be catastrophic when making critical business decisions, particularly in fields like healthcare and self-driving cars.

Multi-stage tasks are carried out while training the AI model in order to make the best use of the training data and achieve satisfactory results. So, here are six popular blunders to avoid in order to ensure the performance of your AI model.

1 Using Data That Isn’t Checked or Standardized

One of the most common mistakes made by machine learning engineers in AI development is the use of unverified and unstructured data. Duplication, conflicting data, lack of categorization, data conflict, errors, and other data issues in the unverified data may cause anomalies during the training phase.

As a result, carefully review your raw data set before using it for machine learning training, removing any unnecessary or irrelevant data to help your AI model function more accurately.

2 Using Data That Has Already Been Used to Test The Model

It’s best not to reuse data that’s already been used to test the model. As a result, such errors should be avoided. For example, if someone has already learned something and applied it to their field of work, applying the same information to another field of work may lead to bias and repetition in inferencing.

In machine learning, the same principle applies; AI can learn from a large number of datasets to accurately predict the answers. Using the same training data on Models or AI-based applications may cause the model to become biassed and produce results that are a product of their previous learning. As a result, it’s critical to test the AI model’s capabilities using different datasets that weren’t previously used for machine learning training.

3 Using Inadequate Training Data Sets

To ensure that your AI model is accurate, you must use the appropriate training data to ensure that it can predict with the highest degree of accuracy. One of the main reasons for the model’s failure is a lack of adequate data for preparation.

However, the types of training data required vary depending on the type of AI model or industry. To ensure that deep learning will operate with high precision, you’ll need more quantitative and qualitative datasets.

4 Ensure that your AI model is not skewed

It is impossible to create an AI model that can provide 100% accurate results in a variety of scenarios. Machines, like humans, can be biassed due to a variety of factors such as age, gender, orientation, and income level, among others, which can influence the results in one way or another. As a result, you must use statistical analysis to determine how each personal factor affects the data and AI training data in progress.

5 Independently relying on AI model learning

However, you may need experts to train your AI model using a large number of training datasets. However, if AI is using a repetitive machine learning algorithm, this must be taken into account when training such models.

As a machine learning engineer, you must ensure that your AI model is learning using the appropriate strategy. To achieve this, you must review the AI training process and its performance on a regular basis to ensure the best results.

However, you must keep asking yourself important questions when designing the machine learning AI, such as: Is your data sourced from a trustworthy credible source? Is your AI capable of detecting a wide range of demographics, and is there anything else influencing the results?

6 Using Datasets That Aren’t Properly Labeled

A well-defined strategy is needed to achieve a winning streak when designing an AI model using machine learning. This will not only assist you in achieving the best results, but it will also increase the trustworthiness of machine learning models among end-users.

The main points to bear in mind when training your model are those mentioned above. However, accurately training data with the highest level of precision is critical for AI to be competitive and perform with the highest level of accuracy in different scenarios. The model’s output will be harmed if your data is not properly labelled.

If your machine learning model is based on computer vision, image annotation is the ideal technique for generating the required training data. While training the model, AI companies face another challenge: obtaining the appropriate labelled data. However, several companies provide data labelling services for machine learning and AI.


  •  
  •  
  •  
  •  
  •  
  •