What Is Inferencing?

Inferencing is the process of an artificial intelligence model generating educated predictions or outputs from new data. After going through an extensive model training process, an AI model will have a set of learned parameters and representations. When a model receives a new data input, it can use these parameters and representations to generate an output. Inference is what happens during an artificial intelligence (AI) application’s run-time or deployment phase, enabling a range of real-world use cases.

AI inference is the process of an AI model generating outputs from unseen data inputs. After model training, in which the model learns generalized patterns from training datasets, an AI model uses inference to apply these generalized patterns to new data.

For example, an AI model could train on medical image scans annotated by medical professionals. During the AI training process, a model learns visual clues and associations within the data. Afterwards, the trained model can identify potential tumors or abnormalities on new scans through inference. You can check the inference process’s accuracy by asking medical professionals to draw conclusions based on the same scans.

Compared to traditional analytics techniques, where humans define rules and choose statistical techniques, model training allows the model to discover complex patterns that are almost impossible for a human to code. These patterns emerge from billions of model parameters. Inference applies these recognized patterns to new data.

There are several different types of machine learning (ML) inference.

Batch inferencing

Batch inference is when an AI makes predictions using batches of multiple data inputs. Batch inferencing is useful in non-time-critical applications, where you can wait for inference outputs. For example, a business analytics generative AI application might process updates once every hour instead of in real time.

Edge inferencing

Edge AI inference is where an AI model is run on a user’s device or near the source of the data. Without the need to transfer data to a central hub for processing, edge inferencing results in little or no network latency. Edge inference is especially useful for remote locations with low connectivity or for devices where inferencing is vital; however, processing speeds depend on the device and model latency.

Real-time inferencing

Real-time inferencing is the process of generating AI inference instantly based on input requests. AI models receive data either by user input or from connected data sources, triggering the need for immediate inference output. Real-time inference relies on extremely low-latency AI systems and is commonly used in time-critical systems. For example, autonomous vehicles use real-time inference, where extremely low-latency inference enables real-time decision-making.

Streaming inferencing

Streaming inference is when a data pipeline continuously delivers information to your AI model, which delivers streaming inference outputs in real-time. Although similar to real-time inference, streaming inference continuously generates outputs based on the input stream. Real-time inference responds to user input or live requests, whereas streaming inference is a continual stream of new AI predictions.

There are several technologies and data systems that work together to supply the infrastructure for AI inference.

Machine learning model infrastructure

AI inference is only available after an extensive machine learning model training process. An AI model must move through several phases of AI training, fine-tuning, validation, and inference optimization before moving to deployment.

A trained AI model in production can require significant compute power with graphics processing units (GPUs), central processing units (CPUs), and specialized AI accelerators, such as AWS Inferentia. The compute power also needs support with storage, networking, and data pipelines.

User input and data preprocessing

To make sure the model can perform AI inference, the data that you feed into it requires preprocessing. Data transforms by techniques such as normalization for vision models and tokenization for large language models (LLMs), and you can clean data to make sure that it is as high-quality as possible.

The inference process

The inference process takes the clean data and performs what’s known as a forward pass, moving the preprocessed data through the model’s layers to process data and generate an output. This is a series of complex calculations.

Output generation and post-processing

After moving through the forward pass, a model generates its output. The model output is generated based on learned patterns from AI training data, creating new predictions.

Post-processing involves scanning, filtering, and formatting any outputs the model has produced, looking for outputs that do not meet set guardrails and policies. For example, guardrails can help to prevent expletives or harmful content, and formatting can produce an output that matches a business’s tone of voice.

There are several challenges that you must address when building an AI model’s capacity to infer.

Model drift

Model drift is when the final version of a model becomes less effective over time as new, real-world data becomes different from the data it was trained on. Data drift occurs when the specific patterns in input data shift over time. If a model’s weights remain fixed based on previous AI training data, then this shift in the new data means that the model will produce inaccuracies in outputs.

Additionally, concept drift is where the relationship between the model inputs and outputs changes over time. A relationship changes or becomes more complex, resulting in concept drift. For example, if an AI model were looking to infer whether an incoming email was a phishing attack, then it would use learned patterns to determine its output. However, if new technologies emerge that a phishing group uses, then this difference means that the model’s output becomes less accurate. Retraining is required to account for the model’s complexity changes.

In both of these cases, model drift means that a successful model now produces answers that are less accurate or simply incorrect.

Speed and performance

Deploying an AI model requires a specific set of infrastructure to deliver outputs at scale. Organizations will need graphics processors, low latency, fast network speeds, and additional heavy-duty processing units for complex models.

Other speed and performance tweaks can include enhanced infrastructure architecture design, specialized hardware, and inference optimization techniques, such as quantization to reduce the size of the inputs.

Opting for a different style of AI inference, like batch inference, will help improve performance in specific use cases.

Cost

AI models that can accurately infer based on new data can lead to unexpected costs for running inference workloads. If an organization wants to run an AI model at scale or apply it to numerous different contexts, then the processing power and volume of inference requests needed can create increasing expenses. Organizations can cost-optimize by applying various strategies such as reducing the size of the model, parallelizing processing, and using auto-scaling cloud infrastructure for online inference.

Improving AI inference and its ability to come to accurate conclusions mainly relies on enhancing the training and optimization processes. Here are some best practices to improve AI inference and enhance its accuracy.

Invest in data quality

The quality of the datasets that you use to train an AI model directly impacts its ability to pass judgment on novel data and produce helpful outputs. High-quality data that is well-labeled, consistent, and free of noise will improve a model’s ability to spot patterns. Establish rigorous processes for data preparation, including cleaning, validating, standardizing, and eliminating inconsistencies from training data.

Use skilled personnel

Where possible, employ individuals with extensive expertise in model training and architecture in the technology stack you wish to use. Annotating data with help from domain experts will mean that the data your model ingests is both high quality and extremely comprehensive. An AI model trained on high-quality data results in more accurate outputs.

Include explainability for compliance

The output that an AI arrives at through inference should have explainability behind it. By applying AI explainability techniques to your model, you can understand which input features influenced a specific output. Different types of ML models use different explainability techniques, such as Shapley Additive exPlanations (SHAP), which can be used on any model but is best suited to tree-based models, linear models, and neural networks. AI explainability also helps organizations align with AI compliance frameworks, such as the GDPR’s right to explanation for decisions from AI.

AWS is creating ways to streamline every step of the machine learning and AI model lifecycle with the most comprehensive set of services and purpose-built infrastructure.

Amazon SageMaker AI provides a complete machine learning environment where you can build, train, optimize, test, and govern your customized machine learning models.

Amazon SageMaker AI is a fully managed service that brings together a broad set of tools to enable high-performance, low-cost machine learning (ML) for any use case. With SageMaker AI, you can build, train, and deploy ML models at scale using tools like notebooks, debuggers, profilers, pipelines, MLOps, and more—all in one integrated development environment (IDE).

SageMaker AI supports governance requirements across training and inference with simplified access control and transparency over your ML projects. In addition, you can build your own foundation models (FMs), large models that train on massive datasets, with purpose-built tools to fine-tune, experiment, retrain, and deploy FMs.

AWS offers a large range of cloud infrastructure for AI inference, including specialized GPUs on Amazon EC2 or machine learning chips like AWS Inferentia.

Get started with ML model inference on AWS by creating a free account today.