What Is AI Inference?

Inference, to a lay person, is a conclusion based on evidence and reasoning. In artificial intelligence, inference is the ability of AI, after much training on curated data sets, to reason and draw conclusions from data it hasn’t seen before.

Understanding AI inference is an important step in understanding how artificial intelligence works. We’ll cover the steps involved, challenges, use cases, and the future outlook for how AI systems come to their conclusions.

What Is AI Inference?

AI inference is when an AI model that has been trained to see patterns in curated data sets begins to recognize those patterns in data it has never seen before. As a result, the AI model can reason and make predictions in a way that mimics human abilities.

An AI model is made up of decision-making algorithms that are trained on a neural network—that is, a language model structured like the human brain—to perform a specific task. In a simple example, data scientists might show the AI model a data set with images of thousands or millions of cars with the makes and models noted. After a while, the algorithm begins to accurately identify cars in the training data set. AI inference is when the model is shown a random data set and figures out, or infers, the make and model of a car with acceptable accuracy. An AI model trained in this way might be used at a border crossing or a bridge toll gate to match license plates to car makes in a lightning quick assessment. Similar processes can derive AI inference with more subtle reasoning and predictions to work in healthcare, banking, retail, and many other sectors.

Key Takeaways

• AI inference is the ability of an AI model to infer, or extrapolate, conclusions from data that’s new to it.
• AI models depend on inference for their uncanny ability to mimic human reasoning and language.
• AI inference is the end goal of a process that uses a mix of technologies and techniques to train an AI model using curated data sets.
• Success requires a robust data architecture, clean data, and many GPU cycles to train and run AI in production environments.

AI Inference Explained

AI inference is a phase in the AI model lifecycle that follows the AI training phase. Think of AI model training as machine learning (ML) algorithms doing their homework and AI inference as acing a test.

AI training involves presenting large, curated data sets to the model so it can learn about the topic at hand. The training data’s job is to teach the model to do a certain task, so the data sets vary. They might include images of cats or bridges, recorded customer service calls, or medical imaging. The AI model can analyze live data, recognize patterns, and make accurate predictions about what comes next in the data set.

With large language models (LLMs), for example, the model can infer what word comes next and produce sentences and paragraphs with uncanny accuracy and fluidity.

Why Is AI Inference Important?

AI inference is important because that recognition is how a trained AI model analyzes and generates insights on brand new data. Without the ability to make predictions or solve tasks in real time, AI will struggle to expand to new roles, including in teaching, engineering, medical discoveries, and space exploration, and take on an expanding list of use cases in every industry.

In fact, inference is the meat and potatoes of any AI program. A model’s ability to recognize patterns in a data set and infer accurate conclusions and predictions is at the heart of the value of AI. That is, an AI model that can accurately read an X-ray in seconds or spot fraud amid thousands or millions of credit card transactions is well worth investing in.

Types of Inference

Do you need an AI system that can make highly accurate decisions in near-real-time, such as whether a large transaction might be fraud? Or is it more important that it be able to use the data it’s already seen to predict the future, as with a sensor that’s tuned to call for maintenance before something breaks? Understanding the approaches to AI inference will help you settle on the best model for your project.

• Batch Inference
  Batch inference is when AI predictions are generated offline using batches of data. In this approach, data is collected over time and run through ML algorithms at regular intervals. Batch inference is a good choice when AI outputs aren’t needed immediately. It works well for bringing AI predictions to a business analytics dashboard that updates hourly or daily.
• Online Inference
  Online inference, sometimes called “dynamic inference,” is a way to provide AI predictions the instant they’re requested. Online inference can be more challenging than batch inference due to its low latency requirements.
  
  Building a system for online inference requires different upfront decisions. For example, commonly used data might need to be cached for quick access, or a simpler AI model might need to be found that requires fewer operations to arrive at predictions. Because there’s no time to review AI outputs before end users see them, online inferences might also need another layer of real-time monitoring to ensure predictions fall within acceptable norms. Popular large language models (LLMs), such as OpenAI’s ChatGPT and Google’s Bard, are examples of online inference.
• Streaming Inference
  Streaming inference is often used in Internet of Things systems. It’s not set up to interact with people in the way an LLM is. Instead, a pipeline of data, such as regular measurements from machine sensors, flows into an ML algorithm that then continually makes predictions. Patterns in the sensor readings can indicate the machine being monitored is working optimally, or the pattern can indicate trouble ahead, triggering an alert or maintenance or repair request.

What’s the Difference Between Deep Learning Training and Inference?

Deep learning training and AI inference are two parts of the same process for getting useful outputs from an AI model. Deep learning training comes first. It’s how an AI model is trained to process data in a way that’s inspired by the human brain. As a model is trained, it gains the ability to recognize deeper levels of information from data. For example, it can go from recognizing shapes in an image to recognizing possible themes or activities in the image. AI inference takes place after the training, when the AI model is asked to recognize these elements in new data.

How Does AI Inference Work?

For AI inference to provide value in a specific use case, many processes must be followed and many decisions must be made around technology architecture, model complexity, and data.

• Data Preparation
  Assemble training material from data within your organization or by identifying outside data sets, possibly including an open source data set. Often, internal and external data sets are combined. Once the data sets have been decided, the data needs to be cleansed to remove duplicates, unneeded data, and formatting issues.
• Model Selection
  Identify an open source, general enterprise, or specialized model that’s designed to provide the kind of AI outputs you need. Keep in mind that models come in varying levels of complexity. More complex algorithms can take in a wider set of inputs and make subtler inferences, but they need a larger number of operations to arrive at a desired output. Find a model that fits your needs in terms of its complexity and hunger for computing resources.
• Model Optimization
  Optimize the model by iterating on your AI training regime. The goal of each round of training is to get closer to the desired output accuracy while reducing the amount of memory and compute power needed to get there. Model optimization is about improving the usefulness of AI inference while lowering costs and minimizing latency.
• Model Inference
  This is when your AI model moves from the training phase to the operational phase, where it’s extrapolating from new data. As your model nears production, review the inferences and predictions in its output. This is when you can check for accuracy, bias, and any data privacy issues.
• Post-Processing
  In AI, post-processing is a set of methods for checking the model’s output. The post-processing phase may include routines for filtering, combining, and integrating data to help prune unfriendly or unhelpful outputs.
• Deployment
  Deployment is when the architecture and data systems that support the AI model are formalized, scaled up, and secured for use in a regular business process. This is also the time for education and change management, where people in the broader organization are taught to accept and use AI outputs in their work.

Hardware Requirements for AI Inference

AI inference is the result of a compute-intensive process of running an AI model through successive training regimes using large data sets. It requires integration of many data sources and an architecture that allows the AI model to run efficiently. Here are key technologies that enable the process.

• Central Processing Unit (CPU)
  A CPU is the central brain of a computer. It’s a chip with complex circuitry that resides on the computer’s motherboard and runs the operating system and applications. A CPU helps manage computing resources needed for AI training and inference, such as data storage and graphics cards.
• Graphics Processing Unit (GPU)
  GPUs are a key hardware component for AI inference. Like a CPU, a GPU is a chip with complex circuitry. Unlike the CPU, it’s specially designed to perform mathematical calculations very quickly to support graphics and image processing. This calculating power is what makes compute-hungry AI training and inference possible.
• Field-Programmable Gate Array (FPGA)
  An FPGA is an integrated circuit that can be programmed by an end user to work in a specific way. In AI inference, an FPGA can be configured to provide the right mix of hardware speed or parallelism, which breaks up data processing work to run on different hardware in parallel. This enables the AI model to make predictions on a certain type of data, whether that’s text, graphics, or video.
• Application-Specific Integrated Circuit (ASIC)
  ASICs are yet another tool that IT teams and data scientists use to derive AI inferences at the speed, cost, and accuracy they need. An ASIC is a computer chip that combines several circuits on a single chip. The chip then can be optimized for a particular workload, whether that’s voice recognition, image manipulation, anomaly detection, or any other AI-driven process.

Challenges with AI Inference Deployment

Designing or choosing an AI model and then training it are just the beginning. Deploying the AI model to carry out inference in the real world comes with its own set of challenges. These can include providing the model with quality data and later explaining its outputs. Here’s a list of challenges to keep in mind.

• Data Quality
  The “garbage in, garbage out” adage is as true in AI inference as anywhere else. Data that trains AI models must be vetted for applicability and formatting and cleansed of duplicate or extraneous data that slows the training process.
• Model Complexity
  AI models come in differing levels of complexity, allowing them to infer or predict in a range of situations, from simple, like identifying a car make and model, to complex and critical, as in the case of AI systems that double-check a radiologist’s reading of a CT scan or MRI. A key challenge of AI training in general and inference in particular is building or choosing the right model for your needs.
• Hardware Requirements
  AI inference training is a data-intensive endeavor. It requires servers for data storage and data analysis, graphics processors, fast networks, and possibly field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs), which can be tailored to your AI inference use case.
• Interpretability
  When AI inference is interpretable, or explainable, it means that human trainers understand how the AI arrived at its conclusions. They can follow the reasoning the AI used to arrive at its answer or prediction. Interpretability is a growing requirement in AI governance and is important for spotting bias in AI outputs, yet as AI systems become more complex, the underlying algorithms and data processes may become too intricate for humans to fully comprehend.
• Regulation and Compliance
  The regulation of AI is a moving target. It’s important to build in data security, explainability, and a robust reporting structure for your AI inferences. This will help you to meet compliance requirements more easily with regulations around privacy, data security, and AI bias as they evolve.
• Lack of Skilled Personnel
  The expertise needed to design, train, and optimize systems for AI inference takes time, education, and experience to develop. As a result, people with this expertise are hard to find and expensive to hire.

Applications of AI Inference

With their ability to infer conclusions or predictions from available data, AI models are taking on more tasks all the time. Popular large language models, such as ChatGPT, use inference to choose words and sentences with uncanny linguistic precision. Inference is also what allows AI to infer what graphic art or video it should build based on verbal prompts.

AI inference is becoming an important part of training industrial systems as well. For example, AI can be used for fast-paced visual inspection on a manufacturing line, freeing human inspectors to focus on flaws or anomalies identified by AI while lowering costs and improving quality control. In industrial systems where robots work alongside humans on production lines, AI inference enables the perception, prediction, and planning needed to sense objects and make subtle motion decisions.

Another common use of AI inference is robotic learning, popularized by the many attempts to perfect driverless cars. As seen from the years of training by companies such as Waymo, Tesla, and Cruz, robotic learning takes a lot of trial and error as neural networks learn to recognize and react properly to exceptions to the written rules of the road.

AI inference is also assisting researchers and physicians. AI models are being trained to find cures by sifting through masses of chemical or epidemiological data, and they’re helping diagnose diseases by reading subtle clues in medical imaging.

The Future of AI Inference

The next step for AI inference will be to break out of large cloud or data center environments and be possible on local computers and devices. While initial training of AI systems using deep learning architectures will continue to run in large data centers, a new generation of techniques and hardware is bringing “last mile” AI inference into smaller devices, closer to where the data is being generated.

This will enable more customization and control. Devices and robots will gain better object detection, face and behavior recognition, and predictive decision-making. If this sounds to you like the underpinnings for general-purpose robots, you’re not alone. In coming years, innovators are looking to deploy this “inference at the edge” technology into a wide range of devices in new markets and industries.

Accelerate Your Real-Time AI Inference with Oracle

Oracle provides the expertise and the computing power to train and deploy AI models at scale. Specifically, Oracle Cloud Infrastructure (OCI) is a platform where businesspeople, IT teams, and data scientists can collaborate and put AI inference to work in any industry.

Oracle’s fully managed AI platform lets teams build, train, deploy, and monitor machine learning models using Python and their favorite open source tools. With a next-generation JupyterLab-based environment, companies can experiment, develop models, and scale up training with NVIDIA GPUs and distributed training. Oracle also makes it easy to access generative AI models based on Cohere’s state-of-the-art LLMs.

With OCI, you can take models into production and keep them healthy with machine learning operations capabilities, such as automated pipelines, model deployments, and model monitoring. In addition to model training and deployment, OCI provides a range of SaaS applications with built-in ML models and available AI services.

When you interact with AI, you’re seeing AI inference at work. That’s true whether you’re using anomaly detection, image recognition, AI-generated text, or almost any other AI output. Results are the culmination of a long, technically complex, and resource-hungry process of model building, training, optimization, and deployment that set the stage for your interaction with AI.

AI Inference FAQs

What is an example of inference in AI?

A good example of inference in AI is when an AI model detects an anomaly in financial transactions and can understand from context what kind of fraud it might represent. From there, the AI model can generate an alert to the card company and the account holder.

What is training and inference in AI?

Training is when curated sets of data are shown to an AI model so it can begin to see and understand patterns. Inference is when that AI model is shown data outside the curated data sets, locates those same patterns, and makes predictions based on them.

What does inference mean in machine learning?

Inference means that a machine learning algorithm or set of algorithms has learned to recognize patterns in curated data sets and can later see those patterns in new data.

What does inference mean in deep learning?

Deep learning is training machine learning algorithms using a neural network that mimics the human brain. This allows the recognition and extrapolation of subtle concepts and abstractions seen, for example, in natural language generation.

Can AI inference be used on edge devices?

AI inference training has traditionally been a data-intensive and computing-hungry process. As AI inference becomes better understood, however, it’s being accomplished by less powerful devices that reside at the edge, away from large data centers. These edge devices for AI inference can bring image recognition, voice, and other capabilities into field operations.

How does AI inference differ from traditional statistical models?

Traditional statistical models are designed simply to infer the relationship between variables in a data set. AI inference is designed to take the inference a step further and make the most accurate prediction based on that data.

How do hyperparameters affect AI inference performance?

When building an AI model, data scientists sometimes assign parameters manually. Unlike standard parameters in the AI model, these hyperparameters aren’t determined by what the model infers from the data set. Hyperparameters can be thought of as guideposts that can be adjusted as needed to help with AI inferences and predictive performance.

How can organizations help ensure the accuracy and reliability of AI inference models?

One key is to know explicitly up front who your output is for and what problem it’s trying to solve. Make desired results specific and measurable. That way, you can establish benchmarks and continually measure your system’s performance against them.