Artificial intelligence (AI) inference is when a model’s training is applied to new data, enabling new predictions. AI can identify a factory’s production-line bottlenecks before they happen, convert speech into text, or tell a self-driving car about pedestrians—and AI inference can perform these tasks with real data, not only the historical data on which the AI model was trained. This article explores AI inference, its types, its roles in various industries, and how it works.
AI inference is the second stage in a two-part machine learning process, where a trained machine learning model applies its knowledge to previously unseen data.
Before inference, AI models learn from vast datasets of labeled information, such as images, texts, or sounds, which AI uses to learn patterns, relationships, and predictive behaviors. For example, a model trained to recognize different types of vehicles could analyze thousands of vehicle images, learning to identify features like shape, size, and type in order to output the make and model.
Once trained and validated on a test dataset—a portion of the initial data not used in training—the model enters the inference step. Here, it applies its learned knowledge to new images, recognizing vehicles it hasn’t seen before. Toll booths can use AI inference to determine car makes and models in order to classify vehicles and charge them appropriately, even though the AI wasn’t trained on the specific cars passing through the toll zone. This ability to generalize from learned data to new scenarios without writing hard-code algorithms is what makes AI inference so powerful.
AI includes two primary types of inference:
- Probabilistic inference: Also known as statistical inference, this type of AI inference uses probability theory to analyze past patterns and make educated guesses about future events. For example, in gaming, AI drives dynamic difficulty-adjustment systems to adjust the game’s difficulty level in real time, ensuring an engaging and challenging experience tailored to the player.
- Classical inference: Classical or rule-based inference makes decisions based on established rules derived from past data. In the context of gaming, this approach is exemplified by the ghosts in Pac-Man. Each ghost follows a specific set of rules: they primarily aim to chase Pac-Man, scatter when Pac-Man enters scatter mode, and avoid colliding with one another. These simple yet effective rules dictate the ghosts’ movements, creating a consistent and logical behavior pattern within the game’s environment.
In business, AI inference is key for making money because it helps make processes more efficient, accurate, and predictable by turning detailed data into useful advice and business actions. AI inference is also used in other areas, like government and healthcare, where its application helps manage resources better, improve patient care, and develop future-forward policies. Let’s explore the specific benefits that AI inference offers organizations across industries.
AI inference uses training results to process data in new, varied scenarios without explicit programming. For example, in construction, AI uses computer vision and convolutional neural networks—models designed to mimic human vision in processing and analyzing images—to identify potential safety hazards or inefficiencies. This automates decision-making processes that traditionally require human intervention.
For example, a new safety hazard that humans aren’t primed to watch for could be identified thanks to AI inference. A new stage of construction or a previously unused material could be flagged as having potential safety concerns without needing to teach the AI model about the specifics of the relevant construction project.
AI inference can understand complex situations and offer guidance or take action accordingly. In finance, AI models use inference to analyze new financial transactions by referencing patterns learned from extensive datasets of past transactions, both individual and generalized. This inference process enables AI to identify patterns that may indicate fraudulent activity in new transactions—a highly complex situation that might otherwise go unnoticed.
AI inference can make informed decisions while removing the risk of human error and overcoming human limitations in data learning and retention. In healthcare, AI inference can help diagnose diseases and tailor patient care by running inference on vast medical datasets. This facilitates diagnoses and the development of customized treatment plans, helping to make informed decisions so medical practitioners can deliver the best possible medical care.
For example, a specific hospital might lack an expert in diagnosing Alzheimer’s based on PET scans, or might be diagnosing slowly due to a lack of specialist staff. AI inference could interpret scans, drawing on global medical expertise. This frees up precious hospital financial and human resources for other tasks, like increased patient-doctor consultation time.
In retail, AI inference on edge devices provides tailored recommendations and manages stock levels effectively on-site. For transportation, it helps optimize route planning by processing real-time data at local network nodes. In government operations, the rapid analysis of surveillance data facilitates swift decision-making for emergency responses.
How Does AI Inference Work?
AI inferencing is the final part of the AI process. Before turning to how it works, let’s understand the prerequisites to a successful AI inference process.
Every AI inference process involves four essential components:
- The data source: Initial information like logs, unstructured data, or database content for AI analysis
- The machine learning model: Trained on labeled data to enhance accuracy; it processes and learns from data to make predictions
- The inference engine: Software that applies the trained model to new data, managing data loading, processing, and output
- The deployment platform: Hosts the system for real-world AI applications
When setting up an AI inference system, several factors need to be considered:
- Location and latency: The physical location of data centers can significantly impact latency. If data has to travel long distances, it can lead to delays that degrade the user experience. Choosing data center locations that minimize data travel times is, therefore, crucial for efficient AI inference.
- Resource management: Optimal performance requires the effective management of resources such as the CPU, memory, and specialized hardware like GPUs and IPUs. As AI applications advance from development to full-scale production, dynamic resource management requires constant adjustments based on real-time performance metrics, such as scaling up processing power during high-demand periods or reallocating memory based on the application’s current needs as it moves from development to full-scale production.
- Financial planning: Operational efficiency is closely tied to financial sustainability. Effective financial planning ensures the long-term viability and scalability of AI inference systems. Good financial planning for AI inference involves anticipating computational needs and allocating budget accordingly. This means forecasting resource usage based on expected user activity, preparing for spikes in demand, and investing in scalable infrastructure. Regularly reviewing and adjusting the budget as the AI system evolves ensures that it remains financially sustainable and can adapt to changing requirements.
The life cycle of an AI model encompasses eight different stages, the seventh of which is AI inferencing, also known as the deployment stage. For an in-depth review of how the entire process works, read Gcore’s introductory guide to AI.
The inference stage, the final and most extensive phase in an AI model’s life cycle, is when the model applies its training to process and interpret new data to make predictions or decisions. While it might seem straightforward—simply running the model on new data—this stage encompasses several intricate tasks.
By implementing advanced techniques like efficient resource use, dynamic scaling, and strategic server selection to support sophisticated business operations, AI inference enhances the complexity of AI’s internal mechanisms. It manages all of this while presenting a simple interface to the user.
Here’s what this stage in the AI process entails through the eyes of a retail business example:
- Efficient resource use: The model must utilize computational resources like memory and processing power efficiently, balancing performance with resource consumption. In an online retail setting, this means that the AI model must manage vast quantities of customer data to analyze shopping trends without causing server overload or incurring unnecessary expenses.
- Model scaling: Depending on the volume of data and the demand, the model may need to be scaled up, requiring more resources to handle increased loads. So, during a Black Friday sale, the AI model must scale up to handle the surge in online shoppers’ data efficiently. This scaling involves dynamically increasing computing power to process more customer information, ensuring that the website remains fast and responsive during these high-traffic periods.
- Batching: The AI system uses batching, which, in a retail setting, involves processing customer data in groups for greater efficiency. This method enhances the system’s ability to handle large volumes of data, such as during sales or promotional events, ensuring performance isn’t compromised. To give another example, a bank might batch process transaction information overnight.
- Monitoring and adjustment: Continuous monitoring of the AI system helps the AI understand and react to shifts in data patterns. In retail, this means tracking changes in shopping patterns to understand the evolution of consumer behavior and trends. If a specific product were to gain sudden popularity, the AI system would notice this trend and adjust its recommendations and stock management strategies to cater to this new demand.
- Geographical deployment: Choosing the right geographical region(s) for deploying the model is essential. Positioning the model closer to the data source or end users significantly reduces latency, improving the speed and responsiveness of the model’s output. For a global retailer, deploying the AI model in regions close to its customer base helps to speed up the processing of customer preferences. As a result, the responsiveness of product recommendations improves, promoting positive user experiences.
- Model server selection: Various servers for inference models offer different capabilities and can be selected based on each model’s specific needs. In the context of choosing servers for an AI model in retail, selecting the right server is much like picking the best location for a physical store. Triton, for example, is ideal for high-traffic periods like holiday sales, capable of handling multiple tasks with its GPU acceleration for rapid data processing. This ensures a seamless online shopping experience during peak times. On the other hand, KServe is a fit for retailers with large-scale online operations in Kubernetes environments, as it streamlines the management of AI applications by automating deployment and scaling, thus improving the efficiency and responsiveness of the retail platform.
AI inference is already enhancing our interaction with technology, as seen in AI-driven customer service chatbots and virtual assistants like Alexa and Siri. These tools, powered by complex AI, simplify user experiences and everyday life, demonstrating the current capabilities of AI inference. Right now, the deployment of AI models in the cloud is becoming more common, offering easier access and usability for a wider range of users.
In the near future, it will become even easier and more common to deploy AI models. AI inference is likely to become a standard service offered in the cloud. Users won’t need in-depth knowledge of machine learning operations to deploy AI models. Instead, they’ll simply upload trained models to cloud-based inference services, specifying resources like CPU and GPU and setting scaling strategies for data flow. This approach will automatically optimize for factors like latency and data center proximity, reducing the need for big (and expensive) AI teams and simplifying project implementation.
The trend towards using proprietary data for creating specific inference models is emerging as businesses seek to use AI to gain or maintain a competitive edge. An example of this is the Gcore-powered collaboration between Pienso and Sky, which utilizes Sky’s own customer service data to develop tailored AI solutions. The result speaks for itself: Sky has become the top-rated provider in its industry when it comes to customer service.
As we look further into the future, we anticipate that the focus will shift toward creating highly specific AI models, optimized to be less resource-intensive (due to the use of smaller but more specialized data sets) and simultaneously more effective. This will be particularly important in inference applications like the Internet of Things (IoT,) where AI could enable smart sensors in homes or cities to process data locally. This local processing will reduce the delay in decision-making (latency), enabling real-time responses in areas where that’s crucial, such as energy management and public safety. AI inference stands to drive innovation and the development of unique, non-replicable AI solutions.
AI inferencing uses a trained AI model to analyze and generate insights on new data, thereby playing an essential role in enhancing industry efficiency and adding value to real-world tasks and experiences. It’s rapidly becoming a must-adopt for organizations across industries that aim for agility and competitiveness. AI inference has an exciting future, with the potential for specialized applications to enhance business efficiency and improve everyday life.
Gcore offers various AI GPU and AI IPU configurations. Our powerful, easy-to-deploy AI infrastructure provides the low latency and high performance required to support the entire AI lifecycle. For a deeper understanding of how Gcore is shaping the AI ecosystem, explore our AI infrastructure documentation.