How to Train AI Models Efficiently with 5 Pain-free Steps

In a world increasingly embracing artificial intelligence (AI), the efficiency of training AI models has never been more critical. These models have become the backbone of various industries, from healthcare to education and beyond. They empower us to tackle complex problems and make accurate predictions.

That’s why understanding the intricacies of training AI models has become a critical pursuit for researchers, engineers, and enthusiasts alike.

Still, the training process involves more than just feeding data into algorithms. Moreover, staying current with the latest advancements in artificial intelligence research and frameworks is essential for achieving optimal results.

In this article, we’ll take you through a comprehensive step-by-step guide to efficiently train AI models. Whether you’re a seasoned AI practitioner seeking to refine your expertise or a newcomer eager to embark on a journey into the artificial intelligence world, this guide will demystify the complexities and provide you with the knowledge and tools necessary to unlock the potential of artificial intelligence.

Now, let’s dive right into it!

Overview of AI Models

An AI model is essentially a program or algorithm designed to uncover patterns within datasets. These patterns enable the model to draw conclusions and make predictions when given new data.

AI models excel at solving intricate problems that would be impractical for the human brain to tackle manually. The accuracy of these predictions improves as the model receives more relevant information.

AI model training is a pivotal process in the AI lifecycle, where the AI system learns to understand, interpret, and derive insights from data. Successful artificial intelligence training relies on three fundamental components: a well-designed AI model, ample high-quality and accurate data annotation, and a robust computing infrastructure.

The Efficient Process on How to Train AI Model

Step 1: Prepare the Dataset

This is the pre-training phase, which is extremely important and necessitates gathering accurate and reliable data. Without this data, AI models won’t be able to complete the necessary tasks and imitate human behavior.

To successfully finish this stage, you should examine the following three best practices:

#1 Gather Relevant Data

First, you must collect or generate relevant data when you train a model, including image data, text data, etc.

Different data collection methods exist, and you should choose the best one based on your project’s scope. Consider the following data collection strategies:

• Custom crowdsourcing
• Private collection or in-house data collection
• Precleaned and prepackaged data sets
• Automated data collection

For instance, crowdsourcing could be a better option for collecting training data for natural language processing (NLP) models since it allows for the rapid collection of large-scale and varied datasets.

Having said that, be aware of several difficulties you can encounter when gathering and producing high-quality data, regardless of which method you use to access the data. Among them are:

• Data accessibility
• Data bias
• Data quality
• Data protection & legal challenges
• Cost challenges
• Data drift

#2 Preprocess Data

Once gathering the appropriate data, data scientists must preprocess it because it may still be disorganized. You go through two sub-phases:

• Data processing entails enriching and cleaning the data to increase the overall quality and relevance of the entire dataset.
• Data modeling enables you to determine the relationships, dependent variable, independent variables, and restrictions that need represented in the dataset.

#3 Annotate Data

The preprocessed data will then be annotated. Accurate data annotation includes labeling the data to make it machine-readable, which can be carried out manually or automatically using an intricate algorithm. Images should be labeled, for instance, during the training of computer vision models.

Step 2: Choose a Model

The next step is choosing the architecture or algorithm that best solves the ultimate problem.

AI models come in a variety of types such as neural network, random forests, decision trees, deep learning models, support vector machines, etc.

You should take the following variables into account while choosing the best AI model:

• The issue and the complexity of it.
• The scope and structure of the available data.
• The required level of accuracy.
• The available computing resources.

A convolutional neural network (CNN), for instance, might be a good option if it is picture classification. Meanwhile, an anomaly object detection technique proves to be a preferable option for outlier detection in a dataset.

Step 3: Start Initial Training

Now that the data is collected and annotated and the model is chosen, you can begin training by feeding the prepared data into the model to find any potential flaws.

This stage involves asking the model to make decisions based on the input data. AI models may make mistakes at this early stage of learning, and all of these mistakes must be corrected to improve the model’s accuracy.

During this step, to train artificial intelligence effectively, it’s critical to avoid overfitting. This issue occurs when the model gets biased and constrained to the training data to solve particular situations, becoming incapable of generalization to adapt to new conditions.

A computer vision-enabled self-driving car system is a good example of this case. If it’s trained on a particular set of driving conditions, such as clear skies and well-maintained roads, and performs well in those conditions but falls short when faced with other driving conditions, such as bad weather like rain or snow or poorly maintained roads.

This is due to the system’s inability to generalize and adjust to novel and diverse driving conditions since it has grown too specialized and overfitted to specific training data.

Expanding the training dataset and using data augmentation are two ways to prevent AI overfitting. Simplifying the model can also assist in avoiding overfitting. This is because, although the dataset can be vast, the model might occasionally overfit due to its complexity.

Step 4: Conduct Training Validation

Once the first training phase is done, proceed to the validation stage.

During the validation period, you will use a fresh dataset known as the validation dataset to confirm your hypotheses on how well the machine learning model performs.

To find any flaws, you should thoroughly examine the new dataset’s findings. Any unknown factors or gaps will become apparent at this point, the overfitting issue included.

Take an NLP model as an example. Imagine that you are trying to create a model that can determine if movie reviews are negative or positive. While validating, we put the model to the test on the validation set, which consists of only new data.

We can evaluate the model’s performance by relying on criteria such as accuracy, precision, recall, and F1 score.

Step 5: Test the Model

We finally go to the last stage of training an AI model, testing. This step aims to assess the model’s performance on a real-world, unstructured data, and unlabeled dataset, which is also known as the “test set”.

If the model yields accurate results, it is suitable for usage. In case the model does not provide the targeted accuracy, it must go through the training stage again.

Below is a rundown of how you test a model in action:

• Data preparation – Process the test set the same way you would for the training data.
• Test the mode – Apply the trained model to the test data.
• Compare results – Review the model’s predictions compared to the actual values.
• Calculate performance metrics – Determine relevant performance metrics (e.g., accuracy for classification based problems and MAE for regression).
• Error analysis – Look into instances when the model made mistakes.
• Benchmarking – Compare to other models or baselines.
• Document results – Keep track of test results and lessons learned for reference later on.

Bonus Tips to Best Train AI Models

Bear in mind some useful bonus tips to further enhance your AI model training process.

#1 Input More Data

Adding new data is one of the most prevalent and effective methods to increase your AI model’s accuracy. The accuracy of a machine learning model rises as the size of the training datasets increases, according to a study by Telstra Purple (2021).

Frankly, it’s essential to keep in mind that increasing the size of the training dataset is not necessarily the best way to enhance the predictive capabilities of the model. And that’s when the second tip below comes in handy.

#2 Enhance the Data

Using Data Augmentation

Data augmentation methods produce several copies of an actual dataset to increase its size artificially. Since it addresses data scarcity and lack of input and output variables, this is especially beneficial for computer vision and NLP models.

What’s more, a study done by AIM in 2020 revealed that a deep learning model with image augmentation improves training and validation accuracy compared to a deep learning model without augmentation for an image classification task. So, next time, try applying data augmentation techniques in computer vision, natural language models, and audio data.

Employing Active Learning

Active learning strategy allows AI models to “ask” for the information they require to perform better. This approach ensures that a model is trained exclusively on the data that is most likely to improve its performance, thereby significantly enhancing the speed and efficiency of a machine learning model.

To apply active learning, start selecting training datasets depending on where the model is least confident following its most recent training session. This enables you to achieve model performance using only 10% to 50% less training data.

Plus, with less data to label for each iteration, the resources required to label training data will be significantly reduced. These resources can then be dedicated to guarantee that the labels produced are of excellent quality.

Evaluating and comprehending model performance after each iteration are also crucial components of active learning. It’s hard to efficiently curate the following training dataset without initially identifying low-confidence areas and edge cases. In order to more effectively measure progress, you must track performance metrics in a single location.

When you have these metrics in place to analyze model errors quickly and straightforwardly, you can prioritize assets that best represent the classes and edge cases that the model needs to improve when building the next batch of training data.

This method will guarantee that models achieve high levels of confidence considerably more quickly than a normal procedure employing sizable datasets and/or datasets produced using random sampling methods.

#3 Upgrade the Architecture

Pay attention to improving an algorithm’s architecture. One approach is to use contemporary hardware features such as SIMD6 instructions or GPUs.

Moreover, you can try employing cache-friendly data layouts and efficient machine learning algorithms. Finally, algorithm designers can make use of current advancements in machine learning and optimization methods.

Ready to Train Your AI Models Today?

Efficient AI model training is a multifaceted process that encompasses precise data collection, thoughtful model selection, and meticulous validation. It’s a journey where data quality, model architecture, and guarding against overfitting play vital roles.

By following these steps and incorporating valuable bonus tips, you can streamline the AI model training process and harness the full potential of artificial intelligence for solving complex problems.

At Neurond, not only do we provide you with AI model training services, but we also consult AI models that best suit your business. We offer an end-to-end AI consulting service from artificial intelligence and machine learning strategy development to product launch to solve your specific problems.

So, are you ready to embark on your AI model training journey with us today? Drop a line at contact@neurond.com now!