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5 AI Training Steps & Best Practices in 2024

5 AI Training Steps & Best Practices in 20245 AI Training Steps & Best Practices in 2024

AI use cases are helping various business functions to become more efficient and effective. Business leaders are leveraging AI either by purchasing pre-built solutions or building their own. However most AI projects provide inadequate results or fail (Figure 1).

One of the biggest challenges in developing AI systems is training the models. To help businesses and developers improve the process of building AI, this article explores 5 steps and best practices to train your AI models effectively. You can also explore how to train large language models.

Figure 1. AI project failure rate range1

1. Dataset preparation

Data collection and preparation is a prerequisite for training AI and machine learning algorithms. Without quality data, machine & deep learning models cannot perform the required tasks and mimic human behavior. Hence, this stage of the training process is of utmost importance. Whether you work with an AI data collection service or prepare the datasets in-house, this part of the process must be done right.

Clickworker offers human-generated datasets for training and improving AI and machine learning models through a crowdsourcing platform. Its global team of over 4.5 million registered workers offers scalable data services for 4 out of 5 tech giants in the U.S.

The following best practices can help successfully execute this process:

1.1. Collect the right data

The first step of data preparation is collecting the actual data. This step involves gathering or generating relevant data and preparing it to train a machine-learning model. For instance, to gather training data for a natural language processing or NLP model, generating data through crowdsourcing might be more effective than other methods since, through crowdsourcing, large-scale and diverse datasets can be gathered in a shorter period of time.

There are different data collection methods to leverage, depending on the scope of the project. You can choose from:

To learn more about these data collection methods, check our article.

You can also check our data-driven list of data collection services to find the right partner for your data needs.

Figure 2. Data collection methods

An illustration listing the 4 data collection methods that can help gather relevant data for AI training.

Regardless of the method you use to obtain the data, collecting or generating high-quality data can be challenging.

Figure 3. Data collection challenges

A list of data collection challenges that cause barriers for AI training.

Click here to learn more about data collection challenges and their solutions.

Data collection best practices

Data collection best practices include:

  • Understanding the problem and goals of the AI/ML project early
  • Establishing data pipelines and leveraging DataOps
  • Establishing storage mechanisms
  • Determining a collection method that best suits your project
  • Evaluating collected data to ensure quality
  • Collecting concise data to ensure relevancy
  • Adding fresh data to the training dataset

To learn more about data collection best practices, check out this quick read.

For more on data collection, feel free to download our comprehensive whitepaper:

Get Data Collection Whitepaper

1.2. Data preprocessing

Data gathered to train machine learning models can be messy and needs preprocessing and data modeling to be prepared for training.

  • Data processing involves enhancing and cleaning the data to improve the overall quality and relevancy of the whole dataset.
  • Data modeling can help prepare datasets for training machine learning models by identifying the relevant variables, relationships, and constraints that need to be represented in the data. This can help ensure that the dataset is comprehensive, accurate, and appropriate for the specific AI/ML problem being addressed.

1.3. Accurate data annotation

After the data has been gathered, the next step is to annotate it. This involves labeling the data to make it machine-readable. Ensuring the annotation quality is paramount to ensuring the overall quality of the training data.

The best data annotation practices depend on the type of data being annotated. Click here to learn more about data annotation and why it matters.

If you wish to annotate a large-scale image dataset, you can also leverage crowdsourcing. Click here to learn more about crowdsourcing image annotation.

2. Model selection

Selecting the right model is one of the most crucial steps in training a machine learning model. It involves choosing the appropriate model architecture and algorithms to best solve the problem. The choice of the model is an important decision, as it determines the model’s performance and accuracy.
The model selection process generally starts with defining the problem and the type of data available. There are various types of models, such as decision trees, random forests, neural networks, deep learning, support vector machines, and more, each designed for specific types of data and problems.
Choosing the right model architecture and algorithm depends on several factors, such as:

  • The complexity of the problem
  • The size and structure of the data
  • The computational resources available
  • The desired level of accuracy

For instance, if the problem is image classification, a convolutional neural network (CNN) may be an appropriate choice. On the other hand, if the problem is identifying outliers in a dataset, an anomaly detection algorithm may be a better choice.

3. Initial training

After data collection and annotation, the training process can start by inputting the prepared data into the model to identify any errors that might surface. A best practice in the initial training phase is to avoid overfitting. Overfitting occurs when the model becomes biased and restricted to the training data.

For instance, if a self-driving car system, enabled with computer vision, is trained on a specific set of driving conditions, such as clear weather and well-maintained roads, and performs well in those conditions but fails to perform adequately when faced with different driving conditions, such as rain or snow, or poorly-maintained roads. This is because the system has become too specialized and overfitted to the specific training data and cannot generalize and adapt to new and different driving scenarios.

In other words, instead of learning from the data, the model memorizes it and can not function when there is a discrepancy in the data. AI overfitting can be avoided in the following ways:

  • Expanding the training dataset
  • Leveraging data augmentation
  • Simplifying the model can also help avoid overfitting. Sometimes the complexity of the model makes it overfitting even when the dataset is large.

4. Training validation

Once the initial training phase is complete, the model can move to the next stage: validation. In the validation phase, you will corroborate your assumptions about the performance of the machine learning model with a new dataset called the validation dataset.

The results obtained from the new dataset should be carefully analyzed to identify any shortcomings. Any unconsidered variables or gaps will surface at this stage. If the overfitting problem is present, it will also be visible at this stage.

Let’s consider a natural language processing (NLP) model as an example. Suppose we want to build a model that can identify the sentiment of movie reviews as positive or negative. We start by collecting a dataset of movie reviews labeled with their respective sentiments. Then split the dataset into training, validation, and testing sets.

During the training phase, we train the model using the training set, and the natural language processing model learns to classify each movie’s reviews as positive or negative based on their text features. In the validation phase, we test the model on the validation set, which contains new and unseen data. We assess the model’s performance based on metrics such as accuracy, precision, recall, and F1 score (machine learning evaluation metrics).

The following frameworks can be used to validate an machine learning model:

3.1 The minimum validation framework

When the dataset is large, the minimum validation framework works best since it only involves a single validation test.

Figure 4. The minimum validation framework2

A minimum validation framework diagram for AI training.

3.2 Cross-validation framework

A cross-validation framework (Figure 5) is similar to the minimum validation framework; the only difference is that the model is validated multiple times with a random dataset. This framework works best with simpler projects with smaller datasets.

Figure 5. Cross-validation framework3

A cross validation framework diagram for AI training.

5. Testing the model

5.1. Purpose

The primary purpose of testing a model is to evaluate its performance on a dataset it has never seen before. This helps in understanding how the model is likely to perform in real-world, practical applications.

5.2. Dataset

For testing, we use a “test set,” which is a subset of the entire dataset. This test set is kept separate and is not used during any part of the training process.

5.3. Generalization

Testing gauges the model’s ability to generalize. A model that performs well on training data but poorly on the test data is likely overfitting, meaning it memorized the training data rather than learned the underlying patterns.

How to test a model?

The following steps can be used to test a machine learning model:

  • Data preparation: Process the test set similarly to the training data.
  • Test the mode: Use the trained model on the test data.
  • Compare results: Evaluate the model’s predictions against actual values.
  • Compute metrics: Calculate relevant performance metrics (e.g., accuracy for classification, MAE for regression).
  • Error analysis: Investigate instances where the model made errors.
  • Benchmarking: Compare against other models or baselines.
  • Document results: Record test metrics and insights for future reference.

Further reading

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Cem Dilmegani
Principal Analyst
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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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