Understanding AI inference: Challenges and best practices

What is AI inference? 

AI inference involves applying machine learning models to new data to generate predictions or insights. Unlike the training phase, where models learn from data sets, inference uses models to analyze live user data. It is a real-time process that transforms input data into outcomes, like classifying images, predicting trends, or generating content based on user prompts. The AI system delivers results from its learning, enabling the use of machine learning models for practical applications.

The inference process relies on pre-trained models to infer results based on new data inputs. During inference, the model formulates insights based on its learned parameters. This is a critical part of deploying AI in real-world environments, where the goal is to output reliable predictions, meaningful insights, or high quality generative output.

This is part of a series of articles about AI infrastructure

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AI training vs inference: Understanding the difference 

The development of AI models involves two main phases: training and inference. Each has a distinct role in preparing models to make predictions or generate insights from new data.

Training phase

In the training phase, the model learns from a large, labeled dataset. Key steps include:

  • Data input: The model receives labeled data, where each input is paired with a known output.
  • Learning process: Using these labeled examples, the model adjusts its internal parameters to reduce errors in its predictions, refining its understanding of the data.
  • Iteration: This process repeats over many cycles, allowing the model to improve its accuracy in identifying patterns and relationships.

Training is resource-intensive, requiring substantial computational power and time. The objective is to create a model that has learned enough from the data to recognize similar patterns in new inputs.

Inference phase

Inference applies the trained model to new, unseen data, using its learned parameters to generate predictions. Important elements of inference include:

  • Data input: The model receives new data, often without any labels.
  • Prediction: Based on what it learned during training, the model provides an output, such as a classification or prediction, based on this input.
  • Efficiency: Inference is optimized for speed and uses fewer resources than training. It enables the model to respond quickly, supporting real-time or near-real-time applications.

Unlike training, inference is lightweight and efficient, allowing AI systems to deliver fast predictions for applications such as image recognition, language processing, or automated decision-making.

Key differences

The main differences between these two phases are:

  • Purpose: Training builds the model; inference uses the model to generate results.
  • Resource requirements: Training is computationally demanding; inference is optimized for speed and efficiency.
  • Data usage: Training uses labeled datasets to learn, while inference works with new, unlabeled data to predict outcomes.

How AI inference works 

AI inference involves applying a pre-trained neural network model to new data inputs, processing information similar to how the model was trained, but without altering weights or parameters. The model receives input data, processes it through its layers, and produces a prediction or classification. This operation is optimized for speed and accuracy, ensuring the model’s deployment can quickly respond to dynamic input data.

Inference involves executing a series of mathematical operations defined by the trained model on incoming data to derive outcomes. The pre-set model parameters guide these operations, preserving the learned patterns or relationships. By understanding how inference works, developers can tailor AI implementations to extract precise predictions from input data.

Types of AI inference 

AI inference can be implemented in real time or in batch processes.

Real-time inference

Real-time inference refers to processing data and generating predictions instantly or within a minimal delay. This approach is useful for applications requiring immediate response, such as fraud detection, autonomous vehicles, or live recommendation systems. It requires highly optimized models and hardware acceleration for swift computation.

Implementing real-time inference requires sophisticated algorithms and efficient data pipelines to minimize processing lag. This type of inference relies on highly responsive systems capable of handling changing data volumes with minimal latency.

Batch inference

Batch inference processes a large volume of data in increments or batches rather than real time. This method is suitable for use cases where instantaneous results aren’t critical, allowing for periodic processing of accumulated data. Batch inference efficiently handles large datasets, making it suitable for applications like offline analytics and data mining.

By using batch inference, organizations can manage computational costs more effectively by processing data during off-peak hours or within allocated time frames. This strategy provides scalability, accommodating significant data loads without compromising resource allocation.

Challenges in AI inference deployment 

There are several challenges that can arise when implementing AI inference.

Latency and performance issues

Latency and performance are critical challenges in AI inference, especially in applications requiring real-time predictions, such as autonomous vehicles or financial trading. High latency can lead to delayed responses. The performance bottlenecks often stem from the complexity of the model, the data throughput, and the computational limits of the hardware.

Scalability concerns

Scalability is essential for deploying AI inference in environments with variable data volumes, such as cloud-based applications and large-scale recommendation engines. As user demands and data increase, inference systems must handle a growing workload without sacrificing performance. Scaling up can be challenging due to the resource-intensive nature of inference, particularly with complex models that require substantial computational power.

Energy efficiency

AI inference can become energy inefficient as models become more complex, highlighting the environmental impact of AI infrastructure. High-power consumption can limit the feasibility of deploying AI inferences on edge devices or in large-scale data centers. Inefficient power usage can lead to increased operational costs and carbon footprint.

Best practices for efficient AI inference 

Organizations should implement the following practices to ensure a successful inference phase in their AI projects.

Optimize model architectures

Optimizing model architectures involves simplifying models without significantly compromising accuracy. Techniques such as model pruning—removing unnecessary weights or neurons—and quantization, which reduces the precision of calculations, can make models faster and lighter. 

Lightweight models like MobileNet and SqueezeNet are often preferred for applications requiring efficient inference, particularly on edge devices with limited resources. Additionally, choosing specialized model architectures can improve efficiency. For example, using smaller, task-specific models rather than a large, general-purpose model can reduce computational needs. 

Select appropriate hardware

Choosing the right hardware for inference is essential for balancing speed, efficiency, and cost. Inference can run on CPUs for lightweight models or GPUs for high-performance, parallel computations. For highly demanding tasks, specialized hardware like TPUs (tensor processing units) and ASICs (application-specific integrated circuits) provide accelerated performance for inference workloads, reducing latency and improving throughput.

For edge deployments where power and space are limited, using edge AI chips or low-power processors like ARM Cortex can offer efficient performance. By aligning hardware choices with model complexity and deployment requirements, organizations can reduce energy costs and ensure that AI systems perform optimally under operational constraints.

Monitor and profile performance metrics

Consistent monitoring and profiling of performance metrics allow for identifying and addressing bottlenecks in inference. Key metrics include latency, throughput, memory usage, and power consumption. Profiling tools like TensorFlow Profiler, NVIDIA Nsight, or PyTorch’s built-in profilers can provide detailed insights into each stage of the inference process.

By regularly evaluating these metrics, teams can make data-driven adjustments to improve model response times and resource efficiency. Continuous performance monitoring also helps ensure that inference applications remain responsive, especially as data loads or model requirements evolve.

Ensure security and compliance

In AI inference, maintaining data privacy and adhering to regulatory standards is crucial, especially for applications handling sensitive information. Techniques such as encryption, anonymization, and secure multiparty computation protect data during inference operations. 

Compliance with standards like GDPR or HIPAA is essential in sectors like healthcare and finance, where data handling is highly regulated. Security practices also include controlling access to inference endpoints and using secure communication protocols to prevent unauthorized access. 

Optimizing AI infrastructure with Spot by NetApp

Organizations need smarter solutions to handle growing AI workloads while keeping costs in check. That’s where Spot by NetApp comes in. It simplifies AI inferencing with scalable, efficient, and reliable infrastructure designed to save you time and money. By optimizing resources and reducing operational costs, Spot helps you get more out of your budget while making deployment and updates effortless.

With features like resource isolation, multi-tenancy, and Kubernetes cost strategies through Spot Ocean, managing AI projects becomes simpler and more efficient. The result? Seamless operations, significant savings, and more time to focus on what matters most—driving innovation. Ready to take your AI projects to the next level? Spot by NetApp is here to help.

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