AI Explained: The Critical Phases of Training and Inference

Artificial Intelligence, machine learning, and more recently, Generative AI, have now become part of the technological and methodological toolkit for all companies engaged in digital innovation. AI includes a wide range of technologies capable of performing tasks that typically require human intelligence, such as real-time language translation, facial recognition, voice assistants, personalized recommendation systems or fraud detection, and computer-assisted medical diagnostics to identify diseases from radiographic images.

Let's discuss AI training and inference processes to gain a better understanding of how models(*) function. 

Note: Terms marked with a (*) are defined in the "Glossary" section at the conclusion of this article. 

AI Training

In a nutshell, AI training is a process by which a machine learning model is developed on the basis of a large set of test data.

It involves feeding a model with a dataset(*), enabling it to learn and make predictions(*) or decisions(*) based on the information it has processed. Models acquire the knowledge and skills necessary to perform specific tasks during this phase. 

Whether interpreting natural language(*) or performing complex calculations, this step is fundamental. Indeed, it determines the accuracy, efficiency, and overall performance of a model, and thus, the applications that will use it.

The AI model training process involves several steps.

1. Data Preparation

This step involves collecting, cleaning, and organizing data in a format that allows efficient use. It’s very important to ensure the quality and reliability of the model's input data. 

2. Algorithm

The second step involves selecting the appropriate algorithm(*) or neural network(*) architecture best suited to address the problem we want to address. 

3. Refinement

Once the model is selected, the third step consists of iterative refinement. This involves training and testing the model multiple times to adjust its parameters based on performance, improving its accuracy, and reducing errors.

AI Training: Challenges

Training AI models presents real challenges, such as:

Data Quality

Models are only as good as the quality of their training data. Inaccurate, incomplete, or biased data sets can lead to poor predictions.

IT Resources

The computing resources necessary for training require high processing power and a significant amount of memory, especially for complex models such as deep learning networks(*). Phenomena such as overfitting(*), can degrade the quality of prediction or classification tasks.

To illustrate the computing resources necessary to train AI models, consider that training sophisticated deep learning networks like GPT-3 required massive computational power to incorporate its 175 billion parameters.

AI Inference

In this phase, a trained machine learning(*)  model is applied to new data to enable it to perform tasks such as prediction, classification, recommendation, or making decisions in real-world applications.

In other words, inference is the phase that enables AI models to provide the anticipated benefits, such as recognizing objects within images, translating languages, offering product recommendations, or guiding self-driving vehicles.

Distinguishing Training and Inference

The inference process is distinguished from AI training by two main criteria:

1. The importance of processing data in real-time
2. The demand for efficiency and low latency

In practice, autonomous driving or real-time fraud detection systems must have models that can quickly interpret new data and act very swiftly.

Challenges To Overcome

The inference phase requires attention to resource efficiency, maintaining consistent performance across various environments, and optimizing models in terms of speed. AI models must be adaptable while not sacrificing accuracy or reliability. This requires employing techniques such as model pruning(*) or quantization(*) to reduce computational load while avoiding degrading model performance.

Examples

Concrete examples illustrate the practical applications of inference are as follows:

Cybersecurity

Once trained on vast datasets of email interactions, applications can identify and flag potential spam or phishing attempts in incoming emails, protecting users from cybersecurity threats.

Autonomous Vehicles

Similarly, the field of autonomous vehicles relies heavily on the inference capabilities of AI. In this case, models trained from countless hours of driving data are applied in real-time to navigate roads, recognize traffic signs, and make split-second decisions.

Training vs. Inference: A Comparative Analysis

Training and inference are two crucial and complementary phases in the development of AI models, each addressing specific needs. The training phase allows the model to acquire knowledge from historical data. This is a step that demands significant computing capacity to adjust the model's parameters for accurate predictions.

Inference, on the other hand, applies the trained model to new data to make predictions or decisions in real time, highlighting the importance of efficiency and low latency.

Points To Remember

• Balancing model complexity, thorough training, and inference efficiency is crucial in developing AI systems.
• Complex models can better understand and predict but require more resources for training and inference.
• Developers must produce both a model that is complex enough to be accurate and efficient enough for real-time use.
• Techniques such as pruning, quantization, and transfer learning optimize models in terms of accuracy and efficiency.

Infrastructure Requirements

The infrastructure requirements of the training and inference phases lead to a significant dependence on hardware performance.

Training deep learning models is particularly computation-intensive, requiring dedicated resources for brute computing power. This phase often requires high-performance GPUs to manage large datasets, upon which the model's accuracy and efficiency depend.

Conversely, the inference phase is less demanding in terms of computing power but requires low-latency, high-throughput performance. Its infrastructure demands efficiency and responsiveness to enable real-time data processing close to the data generation source, as in the case of autonomous cars or our email server, but also in healthcare diagnostics to introduce a new example.

Conclusion

Understanding the subtleties of AI training and inference reveals the complexity between acquiring knowledge by AI models and deploying this knowledge in concrete applications.

AI needs to be not only powerful but also adaptable. To achieve this, a balance must be struck between the use of significant resources for training and the need for fast, efficient inference. As AI progresses in fields such as healthcare, finance, and industry, these training and inference stages are crucial, as they support the creation of AI applied to concrete business cases.

One more thing...

What About the Carbon Footprint?

To advance machine learning and artificial intelligence, it's clear that focusing on developing more efficient AI models, optimizing hardware infrastructures, and, more broadly, adopting innovative strategies is necessary. At the same time, perhaps it's also essential to consider the ecological footprint of AI.

“Energy breakthrough is necessary for future artificial intelligence, which will consume vastly more power than people have expected.”
- OpenAI's CEO Sam Altman
DAVOS, Switzerland; Jan 16, 2024 

Indeed, sustainability becomes a significant issue as the environmental impact of training AI models is closely scrutinized. More electricity and larger quantities of water are needed to power and cool the equipment platforms of tech giants, as companies and the public adopt them. For instance, researchers have estimated that creating GPT-3 consumed 1,287 megawatt-hours of electricity and generated 552 tons of carbon dioxide equivalent, the same as 123 gasoline passenger vehicles driven for a year.

Striving toward a more sustainable future where technological advancement coexists harmoniously with ecological responsibility might be the ultimate goal of AI evolution.

(*) Glossary

• Algorithm: A set of defined, step-by-step computational procedures or rules designed to perform a specific task or solve a particular problem
• Dataset: A collection of data points or records, often in tabular form, used for training, testing, or validating machine learning models, comprising features (independent variables) and, in supervised learning(*), labels (dependent variables or outcomes).
• Decision: In machine learning, this refers to the conclusion reached by a model after analyzing data, such as a spam filter deciding whether an email is spam (and moving it to the spam folder) or not spam (leaving it in the inbox).
• Deep Learning: A subset of machine learning that involves models called neural networks with many layers, enabling the automatic learning of complex patterns and representations from large amounts of data
• Labeled data: This refers to datasets where each instance is tagged with an outcome or category, providing explicit guidance for machine learning models during the training process. 
• Machine Learning: A branch of artificial intelligence that involves training algorithms to recognize patterns and make decisions based on data, without being explicitly programmed for each specific task
• Model: A mathematical and computational representation, trained on a dataset, that is capable of making predictions and classifications on new, unseen data by learning from patterns and relationships within the training data
• Model Pruning: A technique in federated learning that reduces a model's size by adaptively trimming parameters during training to decrease both computation and communication demands on client devices, without significantly impacting the model's accuracy
• Natural language: The way humans communicate with each other, either spoken or written, encompassing the complexities, nuances, and rules inherent to human linguistic expression
• Neural network: A computational model inspired by the human brain's structure, consisting of interconnected nodes or neurons that process and transmit signals to solve complex tasks, such as pattern recognition and decision-making, through learning from data
• Overfitting: When a machine learning model learns the training data too closely, making it unable to generalize and accurately predict outcomes on unseen data
• Pattern: (In the context of machine learning) A discernible regularity in the data that a model learns to identify, which can be used to make predictions or decisions on new, unseen data
• Prediction: (In machine learning) The process of using a trained model to estimate the most likely outcome or value for a new, unseen instance based on the patterns learned during the training phase
• Quantization: (In deep learning) The process of reducing the precision of the weights and activations in a model to 2-, 3-, or 4-bits, enabling the model to run more efficiently at inference time with minimal loss in accuracy
• Supervised/Unsupervised: The difference between supervised and unsupervised learning lies in the presence of labeled data(*) for training in supervised learning, guiding the model to learn a mapping from inputs to outputs, whereas unsupervised learning involves finding patterns or structures in data without explicit outcome labels.