In machine learning, model training refers to the process of feeding data into a machine learning algorithm to learn the underlying patterns and relationships. The goal is to create a model that can make accurate predictions or decisions without being explicitly programmed to do so. This is inspired by the process of human learning, where we are exposed to different situations, learn from them, and then apply the learned knowledge to similar future situations.
Model training is an iterative process, where the algorithm's performance is continually evaluated, then tweaked and optimized to improve the model's accuracy and efficiency. This process continues until the model achieves satisfactory performance, which means it's ready to be deployed for prediction or decision-making tasks.
The success of the training largely depends on the quality and quantity of the data used, the choice of the algorithm, and the fine-tuning of the model's parameters. The type of model training used—supervised, unsupervised, reinforcement learning, or transfer learning—will depend on the nature of the problem at hand and the available data.
In this article:
How Long Does It Take to Train a Machine Learning Model?
The duration of the machine learning model training process can vary greatly and depends on several factors. These include the complexity of the model, the size and quality of the training data, the computational power available, and the chosen algorithm and its parameters.
Simple models with small datasets can be trained in a few seconds or minutes. More complex models such as deep neural networks with large datasets might require hours, days, or weeks to train, especially if computational resources are limited. However, keep in mind that graphical processing units (GPUs) can significantly speed up training for most machine learning models, and when multiple GPUs are available, it is possible to parallelize the effort.
It's worth noting that the process involves not just the actual training but also pre-processing the data, selecting and tuning the algorithm, and evaluating the model's performance. Therefore, patience, along with a deep understanding of the underlying concepts and meticulous planning, is necessary for training machine learning models.
3 Types of Machine Learning Training
Supervised Learning
Supervised learning is a type of model training where the algorithm learns from labeled data, i.e., data with known outcomes. The algorithm uses this data to learn a function that maps inputs to outputs. Once trained, the model can then predict the output for new, unseen data. Examples of supervised learning algorithms include linear regression, decision trees, and support vector machines. Common use cases include object detection and document classification.
Unsupervised Learning
Unsupervised learning involves training the algorithm with unlabeled data. The goal here is to find hidden structures or patterns in the data. Examples of unsupervised learning algorithms include clustering algorithms such as K-means and hierarchical clustering, and dimensionality reduction techniques like Principal Component Analysis (PCA). Common use cases include recommendation systems and anomaly detection.
Semi-Supervised Learning
Semi-supervised learning falls between supervised and unsupervised learning, utilizing both labeled and unlabeled data for training. This approach is particularly useful when acquiring a fully labeled dataset is expensive or impractical, but unlabeled data is abundant. The basic premise is to use a small amount of labeled data to guide the learning process with a large pool of unlabeled data, improving the model's ability to generalize from limited information.
Semi-supervised learning techniques often involve methods like pseudo-labeling, where the model uses its own predictions on unlabeled data as if they were true labels to further train itself, or consistency regularization, which encourages the model to produce the same output for an unlabeled input even after it has been slightly altered.
Reinforcement Learning
Reinforcement learning is a type of model training where an agent learns to make decisions by interacting with its environment. The agent takes actions, receives feedback (rewards or punishments), and uses this feedback to update its knowledge and improve its future decisions.
The training process involves trial and error, where the agent gradually learns the optimal policy (sequence of actions) that maximizes its cumulative reward over time. Reinforcement learning has been used successfully in various areas such as game playing, robotics, and resource management. More recently, it was used to dramatically improve the performance of large language models (LLMs).
Transfer Learning
Transfer learning is a training technique where a pre-trained model is used as a starting point for a related task. The idea is to leverage the knowledge gained from the initial task to improve the performance on the new task, especially when the data for the new task is limited.
For instance, a neural network trained on a large image dataset (like ImageNet) can be fine-tuned to perform well on a specific image recognition task with a much smaller dataset. This approach saves time and computational resources as compared to training a model from scratch.
How Are Different AI Models Trained? Inputs, Flow, and End Results
The specific training approach may differ depending on the type of ML models being used. Let’s look at how these different models can be trained.
Deep Neural Networks
Deep neural networks (DNNs) are inspired by the structure of the human brain. They consist of multiple layers of artificial neurons or nodes, each performing a simple computation. The "deep" in DNN refers to the number of layers in the network. Large DNNs can have up to thousands of layers.
Training a DNN involves feeding it input data, which it processes through its layers to produce an output. Initially, the network makes many errors because its weights (parameters) are not yet tuned. However, as it is exposed to more data, it adjusts its weights using a technique called backpropagation, which minimizes the difference between the network's predictions and the actual values.
The end result of this training process is a network that can accurately classify data or make predictions. DNNs have been successful in various applications including image recognition, speech recognition, and natural language processing.
Generative Adversarial Networks
Generative Adversarial Networks (GANs) are a type of neural network trained to generate new images that resembles images from a training dataset. A GAN consists of two parts: a generator, which produces fake images, and a discriminator, which tries to distinguish the fake images from the real ones.
The training process involves a game-like scenario where the generator and the discriminator compete against each other. The generator tries to fool the discriminator, generating new data samples that are indistinguishable from real ones, and the discriminator tries to catch the generator's bluff. Over time, both get better at their tasks, resulting in the generator producing very realistic data.
Large Language Models
Large language models (LLMs) based on transformer architectures are designed to understand, generate, and interpret human language with a high degree of fluency. Transformers use self-attention mechanisms to weigh the importance of different words in a sentence, allowing the model to consider the context more effectively than previous architectures.
At the beginning of the training process, each word or token in the input text is converted into an embedding. This transformation enables the model to process the input text in a numerically meaningful way, facilitating the understanding of language nuances and contexts. As the training progresses, the model learns to adjust the embeddings based on the context in which words appear. These contextual embeddings are generated by the transformer layers within LLMs.
The process of training an LLM involves adjusting the parameters of the model (including the embeddings) to minimize the difference between the model's predictions and the actual outcomes. This is typically done using a large corpus of text, allowing the model to learn from a wide variety of linguistic contexts.
Basic Process to Train a Machine Learning Model
While there are several approaches to training machine learning models, and some models require different processes, typically machine learning model training includes the following steps.
1. Split the Dataset
The training dataset is typically divided into three parts: a training set, a validation set, and a test set. The training set is used to teach the model to make predictions while the validation set helps in tuning model parameters and selecting the best-performing model. The test set is used to evaluate the model's final performance.
The splitting ratio depends on the size of the dataset and the complexity of the problem. A common practice is to split the data into 70% for training, 15% for validation, and 15% for testing (known as a 70/15/15 split) or 80% for training and 10% for validation and testing (80/10/10).
2. Select Algorithms to Test
The choice of algorithms depends on the nature of the data and the problem at hand. For instance, linear regression might be a good choice for a problem with a continuous target variable, while classification algorithms like decision trees or neural networks might be suitable for problems with categorical target variables.
When selecting algorithms, it's important to consider the assumptions they make about the data. For example, linear regression assumes that the relationship between the predictors and the target variable is linear, and this might not hold true for all datasets.
3. Tune the Hyperparameters
Hyperparameters are parameters that are not learned from the data but are set before the learning process begins. They determine the structure of the machine learning model and the way it learns from the data.
Tuning hyperparameters can be a time-consuming process as it involves training several models with different combinations of hyperparameters and selecting the one that performs best on the validation set. However, it is a crucial step as it can drastically improve the performance of the model.
4. Train and Tune the Model
The actual training of a machine learning model involves adjusting the model's parameters so that it can accurately predict outcomes based on its input data. This step uses the training set to expose the model to a wide variety of examples.
During this phase, the model makes predictions based on the input data, and adjustments are made to the model's parameters through a process called optimization. The optimization process, often involving algorithms like gradient descent, aims to minimize the difference between the model's predictions and the actual outcomes (known as the loss or error).
5. Repeat for Multiple Models or Hyperparameter Combinations
After initial training and tuning, it's common practice to train multiple models or variations of a model with different hyperparameter settings. This iterative process helps identify the model configuration that performs the best on the validation set.
Techniques such as grid search or random search are often used to systematically explore a range of hyperparameter values. More sophisticated methods like Bayesian optimization can also be employed to efficiently navigate the hyperparameter space based on previous results. This step is crucial for refining the model's accuracy and generalizability.
6. Choose the Best Model
Choosing the best model involves comparing the performance of all the models or configurations that have been trained and tuned.
This decision is based on how well each model performs on the validation set, using metrics appropriate to the problem, such as accuracy, precision, recall, or F1 score for classification tasks, and mean squared error or mean absolute error for regression tasks.
The model that best balances performance on the validation set with considerations like complexity and computational efficiency is typically selected. This chosen model is then subjected to a final evaluation on the test set to estimate its performance on unseen data, providing an indication of how it will perform in real-world applications.
Challenges in AI Model Training
Let’s look at some of the main obstacles to successful model training.
Computing Power and Infrastructure Requirements
Training a model, especially a complex one, can be computationally intensive. It requires powerful processors and a considerable amount of memory. Additionally, the infrastructure for storing and processing data also needs to be robust.
For organizations with limited resources, this can pose a significant barrier. However, cloud-based data science solutions can help overcome this challenge. Cloud platforms offer scalable computing power and storage, allowing organizations to access the resources they need for model training without investing in expensive hardware.
Data Bias
Bias in data can lead to inaccurate or unfair results. The machine learning model is only as good as the data it is trained on. Bias can be introduced in various ways—through the collection method, the sample size, or even the way the data is processed.
For instance, if a model is trained on data that is primarily from one demographic, its predictions are likely to favor that group, leading to bias. Similarly, if the data collected is not representative of the population or the situation it is meant to predict, the model's output will be skewed.
To overcome data bias, it is crucial to use diverse and representative datasets for training. Data preprocessing methods can also be used to identify and reduce bias, such as resampling techniques or feature selection methods.
Learn more in our detailed guide to machine learning inductive bias
Overfitting
Overfitting is a common problem in model training. It occurs when the model learns the training data too well, to the point where it starts ‘memorizing’ the training data, and finds it difficult to generalize to new situations. As a result, while the model may perform very well on the training data, it performs poorly on new, unseen data.
To prevent overfitting, techniques like regularization and early stopping can be employed. Regularization adds a penalty term to the loss function, discouraging the model from learning complex patterns that might be just noise. Early stopping involves stopping the training process before the model starts overfitting. Methods like cross-validation can also be used to detect overfitting.
Explainability
Often, machine learning models, especially complex ones like neural networks, are seen as black boxes. They can make accurate predictions, but understanding why they made a particular prediction can be challenging. This lack of transparency can be a problem, especially in sensitive areas like healthcare or finance, where accountability is crucial.
Explainability in model training can be improved by using simpler models, which are easier to interpret. Techniques like feature importance and partial dependence plots can also be used to understand the relationship between the input variables and the model's predictions. Additionally, new areas of research, like explainable AI (XAI), are focusing on developing methods to make complex models more interpretable.
Model Drift
Model drift (together with similar concepts like data drift and concept drift), refers to the phenomenon where the statistical properties of the target variable, which the model is trying to predict, change over time. This can lead to a decrease in model performance because the assumptions about the data, on which the model was trained, no longer hold. Model drift is a common challenge in dynamic environments where patterns, trends, and relationships in the data can evolve due to changes in behavior, preferences, or external factors.
To address model drift, continuous monitoring of the model's performance is essential. When a significant drop in performance is detected, the model may need to be retrained with more recent data that reflects the current state of the environment. Techniques such as rolling windows or incremental learning, where the model is periodically updated on a subset of more recent data, can be effective in adapting to changes.
Model Training Best Practices
Let’s look at some of the best practices for training machine learning models and ensuring their accuracy.
Flag Mislabelled Data
Mislabelled data can lead to inaccurate outputs and poor predictive performance. As a data scientist, it's important to ensure your model is fed with clean, accurate data.
The first step in flagging mislabelled data is understanding your dataset. Spend time getting to know the nature of your data, its attributes, and how they relate to each other. This will help you identify anomalies that might suggest mislabeling. You can also apply simple logical rules to identify mislabeled or corrupted data.
Once you've identified potential mislabelled data, verify your suspicions. This could involve cross-referencing with other data sources or consulting domain experts. If you confirm that the data is indeed mislabelled, flag it and either rectify it or remove the mislabelled data from your dataset. In some cases you will need to replace some or all of the data to ensure representative sampling.
Augment Data with Transformations Where Possible
Data augmentation is the practice of creating new data from existing data through transformations. It can help increase the size and diversity of your dataset, which can in turn improve your model's ability to generalize and make accurate predictions.
One common technique for data augmentation is geometric transformations for images. This includes actions like flipping, rotating, or zooming in on an image. These transformations can help your model better recognize objects in varied orientations or perspectives.
Another popular technique is statistical transformations. This involves generating synthetic data points based on the statistical distribution of your existing data. For instance, if you're working with time series data, you could generate more data points by interpolating between existing data points.
However, while data augmentation can be a powerful tool for improving model performance, it must be used judiciously. Over-augmentation can lead to overfitting, where your model performs well on the training data but poorly on unseen data.
Invest Time in Feature Engineering
Feature engineering involves creating new features from existing data to improve your model's predictive performance. It can lead to more informative, non-redundant features that can help your model make better predictions.
When it comes to feature engineering, it's essential to think creatively and critically about your data. Consider what additional information could be derived from your existing data and how it might be relevant to your model's predictions. For instance, if you're training a model to predict house prices, you could create a new feature that represents the distance to the nearest school or shopping center.
It's also essential to avoid redundancy in your features. Redundant features can lead to overfitting and can make your model unnecessarily complex. A good rule of thumb is to remove features that are highly correlated with each other, as they are likely to provide similar information to your model.
Use Cross-Validation Techniques
Cross-validation involves partitioning your data into subsets, training your model on some of these subsets, and then testing it on the remaining subsets.
One popular cross-validation technique is k-fold cross-validation. In this method, the data is divided into k subsets. The model is then trained on k-1 subsets and tested on the remaining subset. This process is repeated k times, with each subset serving as the test set once.
Cross-validation provides a more accurate estimate of your model's performance than a simple train/test split. It can help you identify overfitting, underfitting, and other potential issues before they become a problem.
Use Regularization When Applicable to Limit Model Complexity
Regularization helps prevent overfitting by discouraging overly complex models that fit the training data too closely. There are several types of regularization techniques, but they all work by adding a penalty to the loss function that the model is trying to minimize. This penalty increases as the complexity of the model increases, discouraging unnecessary complexity.
For instance, L1 and L2 regularization add a penalty proportional to the absolute value and the square of the model's coefficients, respectively. These techniques can help your model generalize better to unseen data by discouraging overfitting.
Testing and Evaluating ML Models with Kolena
We built Kolena to make robust and systematic ML testing easy and accessible for all organizations. With Kolena, machine learning engineers and data scientists can uncover hidden machine learning model behaviors, easily identify gaps in the test data coverage, and truly learn where and why a model is underperforming, all in minutes not weeks. Kolena’s AI / ML model testing and validation solution helps developers build safe, reliable, and fair systems by allowing companies to instantly stitch together razor-sharp test cases from their data sets, enabling them to scrutinize AI/ML models in the precise scenarios those models will be unleashed upon the real world. Kolena platform transforms the current nature of AI development from experimental into an engineering discipline that can be trusted and automated.
Among its many capabilities, Kolena also helps with feature importance evaluation, and allows auto-tagging features. It can also display the distribution of various features in your datasets.
Reach out to us to learn how the Kolena platform can help build a culture of AI quality for your team.
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