5 AI Training Steps & Best Practices in 2025

AI enhances business efficiency, with leaders adopting pre-built solutions or developing their own. However, almost 80% of AI projects underdeliver or fail.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, explore 5 steps and best practices for effectively training AI models.

5 steps to effective AI training

1. Dataset preparation

Data collection and preparation are prerequisites for training AI and machine learning algorithms. Without quality data, machine and deep learning models cannot perform the required tasks and mimic human behavior. Hence, this stage of the training process is of the 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. The following best practices can help successfully execute this process:

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 (NLP) model, crowdsourcing might be more effective than other methods since 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:

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

Check data collection services to find the right partner for your data needs.

Figure 1: Data collection methods.

Regardless of your method to obtain the data, collecting or generating high-quality data can be challenging. Here are some risks businesses should look out for:

• Dataset availability: Datasets may be incomplete, misaligned with project goals, or fail to represent real-world conditions, making them unsuitable for accurate model training.
• Data bias: Biased data can lead to AI models with flawed predictions and unfair outcomes.
• Data quality: Raw data often requires cleaning, preprocessing, and annotation, which can be time-consuming and prone to errors.
• Data protection and legal concerns: Sensitive or regulated data may be subject to legal restrictions and ethical concerns, and non-compliance risks fines or causes damage to trust.

Check out data collection challenges and solutions for more.

Data collection best practices

Data collection best practices include:

• Understanding the problem and goals of the AI/ML project early: Clearly defining the objectives ensures the data collected aligns with the AI’s purpose.
• Establishing data pipelines and leveraging DataOps: Creating efficient workflows to collect, process, and manage data improves reliability and scalability.
• Establishing storage mechanisms: Ensuring secure and scalable storage helps prevent data loss and facilitates easy access.
• Determining a collection method that best suits your project: Choosing the right tools and techniques ensures accurate data collection.
• Evaluating collected data to ensure quality: Verifying data consistency and accuracy helps reduce errors in training AI models.
• Collecting concise data to ensure relevancy: Focusing on the directly related data avoids unnecessary noise.
• Adding fresh data to the training dataset: Incorporating new data ensures the AI remains updated and effective over time.
• Establishing an AI inventory to promote AI for compliance in the data collection process: Keeping a record of AI tools and data sources ensures accountability and regulatory adherence.

Explore more on these best practices by checking out DataOps vs MLOps and MLOps.

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.

• Leveraging MLOps tools can streamline these processes, ensuring efficient data pipelines and high-quality inputs for training.

Data modeling can help prepare datasets for training machine learning models by identifying the relevant variables, relationships, and constraints that must 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.

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 essential to ensuring the overall quality of the training data and reducing AI bias, which can arise from poorly labeled or unrepresentative data.

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

To annotate a large-scale image dataset, you can also leverage crowdsourcing. Check out 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, indicating accurate and high performing models.

The model selection process generally starts with defining the problem and the available data type. Various types of models, such as decision trees, random forests, neural networks, deep learning, support vector machines, and more, are 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.

Tips: For NLP tasks, LLMOps tools can be particularly useful in managing, deploying, and maintaining large language models effectively. AI governance tools can help ensure that these models are performant and compliant with ethical standards and regulations.

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. Implementing responsible AI best practices and ethical AI practices during training helps ensure that models generalize well and do not propagate biases or unethical decisions.

For example, a self-driving car system with computer vision and trained on clear weather and well-maintained roads may perform well in those conditions but struggle in rain, snow, or poorly-maintained roads. This happens because the system is overfitted to its training data and cannot adapt to new or varied scenarios.

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 overfit 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 machine learning model’s performance 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 containing 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 a machine learning model:

The minimum validation framework

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

Figure 2: The minimum validation framework.²

Cross-validation framework

A cross-validation framework (Figure 4) 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 3: Cross-validation framework³

5. Testing the model

Purpose

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

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.

Generalization

Testing gauges the model’s ability to generalize. A model that performs well on training data but poorly on test data is likely overfitting, meaning it has memorized the training data rather than learned the underlying patterns. Regularly monitoring for model drift and performing model retraining can help maintain the model’s accuracy and performance over time.

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.

FAQs

Further reading

• Sentiment Analysis: How it Works & Best Practices
• Top 5 Open Source Sentiment Analysis Tools
• 3 Ways to Apply a Data-Centric Approach to AI Development

External Links

• 1. Error | PMI.
• 2. The Comprehensive Guide to Model Validation Framework: What is a Robust Machine Learning Model? | by ODSC - Open Data Science | Medium. Medium
• 3. A Gentle Introduction to k-fold Cross-Validation - MachineLearningMastery.com. Machine Learning Mastery