What is AI Inference?

Published: 18 June 2024
Contributors: Mesh Flinders, Ian Smalley

What is AI inference?

Artificial intelligence (AI) inference is the ability of trained AI models to recognize patterns and draw conclusions from information that they haven’t seen before.

AI inference is critical to the advancement of AI technologies and underpins its most exciting applications, such as generative AI, the capability that powers the popular ChatGPT application. AI models rely on AI inference to imitate the way people think, reason and respond to prompts.

AI inference starts by training an AI model on a large data set with decision-making algorithms. AI models consist of decision-making algorithms that are trained on neural networks—large language models (LLMs) that are constructed like a human brain. For example, an AI model that is designed for facial recognition might be trained on millions of images of the human face. Eventually, it learns to accurately identify features like eye color, nose shape and hair color, and it can then use them to recognize an individual in an image.

The difference between AI inference and machine learning

Though closely related, AI inference and machine learning (ML) are two different steps in the AI model lifecycle.

Machine learning is the process of using training data and algorithms, through the process of supervised learning, to enable AI to imitate the way humans learn, gradually improving its accuracy.
AI inference is the process of applying what the AI model learned through ML to decide, predict or conclude from data.

How to choose the right AI foundation model

While most organizations are clear about the outcomes they expect from generative AI, what’s not so well understood is the way to go about realizing these outcomes. Choosing the wrong model can severely impact your business.

Benefits of AI inference

If AI models aren’t trained on a robust dataset that’s appropriate to their application, they simply aren’t effective. Given the sensitive nature of the technology and how closely it's scrutinized in the press¹, enterprises need to be cautious. But with applications that span industries and offer the potential of digital transformation and scalable innovation, its benefits are many:

Precise and accurate results: AI models are becoming more precise and accurate as the technology advances. For example, the newest LLMs can choose words, sentences and grammar in ways to mimic the tone of a particular author. In the art and video space, they can do the same, selecting colors and styles to convey a precise mood, tone or artistic style.

Improved quality control: One of the newest and potentially most exciting expansions of AI are in the field of systems monitoring and inspections. AI models that are trained on datasets that range from water quality to weather patterns are being used to monitor the health of industrial equipment in the field.

Robotic learning: Robots and robotics with AI inference capabilities are being deployed for various tasks to add business value. Perhaps the most popular application of robotic learning is driverless cars. AI inference is widely used by driverless car companies like Tesla, Waymo and Cruz to teach neural networks to recognize and obey traffic rules.

Directionless learning: AI inference trains on data without being programmed, reducing human input and resources required to run effectively. For example, an AI model trained on images of agricultural settings can be used to help farmers identify and mitigate weeds and unhealthy crops.

Informed guidance and decision-making: One of the most exciting applications of AI inference is AI’s ability to understand nuance and complexity and offer advice based on the datasets it’s learned on. For example, AI models that are trained on financial principles can offer sound investment advice and identify potentially fraudulent activity. Similarly, AI can take the potential of human-error out of risky procedures like the diagnosis of a disease or the piloting of an aircraft.

Edge computing capabilities: AI inferencing and edge computing deliver all the benefits of AI in real-time, without the need to move data to a data center to process it. The potential of AI inference at the edge has wide-ranging repercussions, from the management and monitoring of stock levels in a warehouse to the millisecond-speed reactions required for the safe operation of an autonomous vehicle.

Challenges of AI inference

While the benefits of AI inference are many, as a young, fast-growing technology, it is not without its challenges, too. Here are some of the problems facing the industry that businesses considering investing in AI should consider:

Compliance: The task of regulating AI applications and AI inference is arduous and constantly changing. One example of this is the area of data sovereignty, the concept that data is subject to the laws of the country or region where it was generated. Global enterprises that gather, store and process data for AI purposes in more than one territory find it challenging to stay in compliance with laws across multiple territories while still innovating in ways that will benefit their business.

Quality: In the training of AI models, the quality of data that the models are trained on is critical to their success. Just like humans learning from a poor teacher, an AI model trained on a bad dataset will perform poorly. Datasets need to be labeled clearly and be hyperrelevant to the skill the AI model is trying to learn. A key challenge of AI (and especially the accuracy of AI inference) is the selection of the right model to train on.

Complexity: Just like with data quality, data complexity can cause problems with AI models, as well. By using the analogy of a human student again, the simpler the thing the AI is being trained for, the easier it is to learn. AI models tackling simple problems, such as a customer service chatbot or a virtual travel agent, are relatively easy to train compared to models designed for more complex problems, like medical imaging or financial advice.

Upskilling: As thrilling as it can be to imagine the possibilities of a new and rapidly growing field like AI, the expertise required to create functioning AI applications and accurate AI inference takes time and resources. Until the pipeline of talent catches up with the pace of innovation, experts in this field with remain in high demand and expensive to hire.

Reliance on Taiwan: Fully 60% of the world’s semiconductors and 90% of its advanced chips (including the AI accelerators needed for AI inference) are manufactured on the island of Taiwan.² Additionally, the world’s largest AI hardware and software company, Nvidia, relies almost exclusively on a single company, the Taiwan Semiconductor Manufacturing Corporation (TSMC), for its AI accelerators. Natural disasters or other unseen incidents might threaten the manufacturing and distribution of the chips needed to power AI inference and its many applications.

Critical components for AI inference

AI inference is a complex process that involves training an AI model on appropriate datasets until it can infer accurate responses. This is a highly compute-intensive process, requiring specialized hardware and software. Before looking at the process of training AI models for AI inference, let’s explore some of the specialized hardware that enables it:

Central processing unit

The central processing unit (CPU) is the primary functional component of a computer. In AI training and inference, the CPU runs the operating system and helps manage compute resources required for training purposes.

Graphics processing unit

Graphics processing units (GPUs), or electronic circuits built for high-performance computer graphics and image processing, are used in various devices, including video cards, motherboards and mobile phones. However, due to their parallel processing capabilities, they are also increasingly being used in the training of AI models. One method is to connect many GPUs to a single AI system to increase that system’s processing power.

Field-programmable gate arrays

Field-programmable gate arrays (FPGAs) are highly customizable AI accelerators that depend on specialized knowledge to be reprogrammed for a specific purpose. Unlike other AI accelerators, FPGAs have a unique design that suits a specific function, often having to do with the processing of data in real-time, which is critical to AI inference. FPGAs are reprogrammable on a hardware level, enabling a higher level of customization.

Application-specific integrated circuits

ASICs are AI accelerators designed with a specific purpose or workload in mind, like deep learning in the case of the WSE-3 ASICs accelerator produced by Cerebras. ASICs help data scientists speed AI inference capabilities and lower the cost. Unlike FPGAs, ASICs cannot be reprogrammed, but since they are constructed with a singular purpose, they typically outperform other, more general-purpose accelerators. One example of these is Google’s Tensor Processing Unit (TPU), developed for neural network machine learning using Google's own TensorFlow software.

How AI inference works

Enterprises interested in investing in AI applications as part of their digital transformation journey should educate themselves about the benefits and challenges of AI inference. For those who have thoroughly investigated its various applications and are ready to put it to use, here are five steps to establishing effective AI inference:

Prepare data

Preparing data is critical to creating effective AI models and applications. Enterprises can create datasets for AI models to train on using data from within their organization or from without. For optimal results, it's typical to use a combination of both. Another key part of assembling data your AI will train on is the cleansing of data—the removing of any duplicate entries and the resolution of any formatting problems.

Choose a training model

Once a dataset has been assembled, the next step is the selection of the right AI model for your application. Models come in a range from simple to complex, with the more complex ones able to accommodate more inputs and infer at a subtler level than the less complex ones. During this step, it’s important to be clear about your needs, as training more complex models can require more time, money and other resources than training simpler ones.

Train your model

To get the wanted outputs from an AI application, businesses will usually need to go through many, rigorous rounds of AI training. As models train, the accuracy of their inferences will get sharper and the amount of compute resources required to reach those inferences, such as compute power and latency, will lessen. As the model matures, it shifts into a new phase where it can start to make inferences about new data from the data it's learned on. This is an exciting step because you can see your model begin to operate in the way it was designed to.

Monitor output

Before your model is deemed operational, it’s important you check and monitor its outputs for any inaccuracies, biases or data privacy issues. Postprocessing, as this phase is sometimes called, is where you create a step-by-step process for ensuring your model’s accuracy. The postprocessing phase is the moment to create a methodology that will ensure that your AI is giving you the answers you want and functioning the way it’s intended to.

Deployment

After rigorous monitoring and postprocessing, your AI model is ready to be deployed for business use. This last step includes the implementation of the architecture and data systems that will enable your AI model to function, as well as the creation of any change management procedures to educate stakeholders on how to use your AI application in their day-to-day roles.

Types of AI inference

Depending on the kind of AI application enterprises require, there are different types of AI inference they can choose from. If a business is looking to build an AI model to be used with an Internet of Things (IoT) application, streaming inference (with its measurement capabilities) is likely the most appropriate choice. However, if an AI model is designed to interact with humans, online inference (with its LLM capabilities) would be a better fit. Here are the three types of AI inference and the characteristics that make them unique.

1. Dynamic inference

Dynamic inference, also known as online inference, is the fastest kind of AI inference and is used in the most popular LLM AI applications, such as OpenAI’s ChatGPT. Dynamic inference makes outputs and predictions the instant it’s asked for them and, after, requires low latency and speedy access to data to function. Another characteristic of dynamic inference is that outputs can come so quickly that there isn’t time to review them before they reach an end user. This causes some enterprises to add a layer of monitoring between output and the end user to ensure quality control.

2. Batch inference

Batch inference generates AI predictions offline by using large batches of data. With a batch inference approach, data that’s been previously collected is then applied to ML algorithms. While not ideal for situations where outputs are required in a few seconds or less, batch inference is a good fit for AI predictions that are updated regularly throughout the day or over the course of a week, like sales or marketing dashboards or risk assessments.

3. Streaming inference

Streaming inference uses a pipeline of data, usually supplied through regular measurements from sensors, and feeds it into an algorithm that uses the data to continually make calculations and predictions. IoT applications, such as AI used to monitor a power plant or traffic in a city via sensors connected to the internet, rely on streaming inference to make their decisions.

Footnotes