AI Inference: Guide and Best Practices
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As AI moves from a niche solution to an everyday tool, inference is quickly becoming a focal point. AI inference is the process where trained machine learning models analyze new data and generate real-time insights. AI inferencing is a valuable step that turns a “trained” model into something that is actually functional in the real world; this is the stage where AI delivers tangible business outcomes, powering automation, personalization, or operational efficiency across a variety of industries.
In this blog, we’ll dive into AI inference, how it differs from AI training, its key benefits and challenges, and best practices to optimize workloads for efficiency and scalability.
Key highlights:
Artificial intelligence (AI) inference is the real-time decision engine behind applications like fraud detection, autonomous vehicles, and personalized recommendations.
Understanding the difference between AI training and inference is key to building efficient, scalable machine learning pipelines.
Optimizing inference requires the right mix of hardware, model compression, and deployment strategies tailored to workload demands.
Mirantis k0rdent AI simplifies AI inference at scale with Kubernetes-native automation and centralized workload management.
What Is AI Inference?
AI inference is the process of applying a pre-trained machine learning model to analyze new data and generate real-time predictions. Unlike AI training, which involves processing large data sets to learn patterns, inference uses this acquired knowledge to classify or interpret fresh inputs instantly.
This stage is critical for AI-driven applications, including natural language processing, autonomous systems, and real-time fraud detection, where fast and accurate decision-making is essential.
What Is the Difference Between AI Training and Inference?
Training and AI inference are distinct yet interdependent: training teaches the model, while inference applies that knowledge for real-time predictions.
If this concept seems complex, think of it this way: AI training is like a student preparing for an exam—spending hours reading textbooks, taking notes, and practicing problems to understand key concepts. Inferencing, on the other hand, is like taking the actual test—applying that learned knowledge to answer new questions without having to re-study everything from scratch.
Both training and inference are essential for effective AI deployment. Let’s take a closer look:
| Feature | AI Training | AI Inference |
| Purpose | Learning from data | Making predictions on new data |
| Computational Power | High (requires GPUs/TPUs) | Lower, but optimized for speed |
| Time Required | Long (hours to days) | Short (milliseconds to seconds) |
| Use Case | Model development | Real-time applications |
| Dataset Size | Uses large datasets to teach a model patterns and relationships | Uses the trained model to make real-time predictions on new data |
| Resource Usage | Computationally intensive, often requiring GPUs or AI accelerators | Lower overall resource demands, but can still be costly at scale |
| Frequency | Typically performed once or periodically for model updates | Runs continuously in production environments |
| Latency Requirements | Not time-sensitive; can run offline | Usually needs to be highly optimized for low latency |
Types of AI Inference
AI Inference is a very powerful and important part of AI models. Even under this umbrella, there are multiple different types of AI inference, each with its own strengths and requirements.
Batch Inference
Instead of processing data as soon as it arrives, batch inference processes chunks of data in scheduled intervals. This is very cost-efficient for large datasets because powerful compute resources can be used during off-hours.
Batch inference is often used when real-time results are not necessary, as it can lead to stale predictions if data changes significantly between batches.
Online Inference
Online inference, on the other hand, produces predictions for a single data point or a small batch immediately after receiving it. For time-sensitive applications like fraud detection or real-time personalization, this is extremely useful due to instant feedback and decision-making capabilities.
Notably, online inference requires highly optimized infrastructure to maintain low latency and is more susceptible to scaling challenges if request volume spikes.
Streaming Inference
Streaming inference takes this a step further by processing continuous flows of incoming data in real time, typically from event streams or sensor feeds. Streaming inference is useful for detecting anomalies or changes instantly and is used for IoT monitoring, live analytics dashboards, or video stream analysis.
It requires a robust infrastructure to handle high throughput and constant input without downtime.
How Artificial Intelligence Inference Works
AI inferencing is where a trained model goes from theory to action, building off of training to make real-time predictions based on new data. Here’s a breakdown of the AI inference lifecycle and the stages of the deployment process:
1. Model Deployment
The trained AI model is packaged and deployed into the target environment, ready to process live data. The model also needs to be integrated with existing APIs, applications, or workflows. It is important to select the right deployment framework and manage version control during this process so that everything runs smoothly.
2. Data Processing
Once data is captured from the source, it must be cleaned, structured, and formatted to ensure accurate predictions. This is where feature engineering or transformation pipelines are applied to normalize and transform data. Missing or corrupted data also has to be taken care of to avoid inference errors.
3. Prediction Generation
The model analyzes the data and applies the patterns and relationships it learned during training to transform the input into a raw output. These operations are usually executed through a series of layers that progressively refine the representation of the data until the model can generate an output.
4. Interpretation
The raw output is translated into a meaningful and interpretable result. For example, a probability score or a classification ID must be expressed as a human-readable output. Situational logic often has to be applied here to handle borderline cases in a way that balances accuracy, efficiency, and security.
5. Decision-Making
Finally, the system acts on the processed inference result. The output is now used to draw conclusions, automate processes, assist human decision-making, or feed into another system. Feedback loops can also capture this information for future model retraining. Here, there
AI Inference Benefits for Enterprises
Inferencing AI is what brings machine learning models to life, enabling them to make fast, intelligent decisions in real-world scenarios.
Here’s why it’s so valuable across industries:
Real-Time Decision Making
AI can analyze and process data instantly, leading to immediate insights. This reduction in response time improves operational responsiveness and agility. Real-time processing is also critical for powering applications like self-driving cars, fraud detection, and personalized recommendations.
Cost Efficiency
Unlike training, which is resource-intensive, inference requires less computing power and energy. Optimization techniques such as model compression further reduce infrastructure loads. All of this allows organizations to run inference at scale without incurring significant costs.
Scalability
Advances in AI inference support the deployment of machine learning models at scale without overwhelming their systems or budgets. Inference also adapts to growing data volumes without overwhelming existing infrastructure. Additionally, Kubernetes and container orchestration make it easy to manage workloads across multiple environments.
Low Latency
AI inference processes inputs and delivers predictions in milliseconds, making it essential for time-sensitive applications like cybersecurity monitoring, healthcare diagnostics, financial transactions, and fraud detection. The instant feedback and actions greatly improve users’ experiences as well.
Top Use Cases for Inference in AI
Inference in AI is already transforming key industries, driving significant advancements in efficiency and decision-making, and its impact is only growing. Soon, it will be a critical component across nearly every sector, enabling smarter, faster decision-making.
Here are some areas where inference is making a difference today:
Healthcare: Assists in medical diagnoses by analyzing imaging data (e.g., X-rays, MRIs) and detecting anomalies faster than human experts.
Finance: Strengthens fraud detection by analyzing transactions in real time and identifying suspicious activity before it causes harm.
Retail: Powers recommendation engines that personalize shopping experiences, helping businesses boost sales and customer engagement.
Autonomous Vehicles: Processes sensor data instantly to recognize obstacles, traffic signals, and pedestrians, ensuring safer driving decisions.
As AI technology advances, inference is set to become a game-changer across nearly every industry—from manufacturing and logistics to education and entertainment—reshaping the way we work, learn, and innovate.
Hardware Requirements for Inferencing AI
The success of inferencing AI depends on the right hardware. For models to run efficiently, meet latency targets, and scale correctly, having the proper hardware is essential. Hardware requirements vary depending on the model size, complexity, and deployment environment (cloud, on-prem, or edge).
In addition, factors such as processing power and memory capacity also impact inference speed and accuracy.
| Aspects of AI Inference | Types of Hardware Required |
| Processing Power | GPUs, TPUs, and AI accelerators speed up model execution. |
| Memory | Adequate RAM ensures smooth model processing |
| Edge Devices | Inference can run on edge devices for real-time, low-latency predictions |
Top Enterprise AI Inference Tools
Choosing the right AI inference tools is critical, especially for enterprises looking to up their game. It is key to achieving low latency, high scalability, and operational efficiency in production. The best solutions combine powerful performance and smooth management with flexible deployment.
Mirantis k0rdent AI stands out as the leading option, delivering enterprise-grade AI deployment and management with unmatched automation and scalability.
| Tool | Key Features |
| Mirantis k0rdent AI | Kubernetes-native AI infrastructure management, dynamic scaling, composable, declarative automation, optimized for AI/ML workloads |
| NVIDIA Triton Inference Server | Multi-framework support, GPU optimization, model ensemble execution |
| TensorFlow Serving | Flexible deployment for TensorFlow models, REST/gRPC APIs |
| ONNX Runtime | Cross-platform, optimized for multiple hardware backends |
| TorchServe | Native PyTorch model serving, custom handlers, batch processing |
| OpenVINO Toolkit | Optimized for Intel hardware, edge deployment, and low-latency performance |
| AWS SageMaker Endpoint | Fully managed cloud inference, auto-scaling, integration with the AWS ecosystem |
Mirantis k0rdent AI
Mirantis k0rdent AI is a Kubernetes-native solution for managing AI/ML workloads across cloud, hybrid, and edge environments with centralized control. k0rdent AI supports dynamic scaling for inference, multi-tenant GPU isolation for high-performance sharing, intelligent routing to ensure data sovereignty, and self-service model deployment.
NVIDIA Triton Inference Server
NVIDIA Triton Inference Server is optimized for GPU acceleration. It also enables model ensemble and concurrent execution. TensorFlow, PyTorch, ONNX, and more are all supported.
TensorFlow Serving
TensorFlow Serving is a production-ready serving for TensorFlow models. It provides REST and gRPC API support, along with versioning and hot-swapping for model updates.
ONNX Runtime
ONNX Runtime is ideal for cross-platform AI deployment and can run models across frameworks and platforms. It can also be augmented with hardware acceleration from CUDA, TensorRT, or DirectML.
TorchServe
TorchServe is the native serving solution for PyTorch models. It supports custom inference handlers and includes built-in batch processing and metrics logging.
OpenVINO Toolkit
OpenVINO toolkit is Intel-optimized for edge and embedded AI. It is ideal for computer vision and low-latency edge workloads. OpenVINO toolkit delivers high performance on CPUs, VPUs, and FPGAs.
AWS SageMaker Endpoint
AWS SageMaker Endpoint enables fully managed model deployment in AWS. It has deep integration with AWS data and ML services, and supports auto-scaling based on demand.
How to Select the Right Infrastructure for AI Model Inference
Selecting the right AI infrastructure solutions is critical to ensure AI models perform optimally in production environments. To make optimal decisions, teams need to evaluate the following:
Model Complexity: The size, architecture, and computational demands of the model are critical to consider. Large, deep neural networks need high-performance compute, while lightweight models can run on less powerful hardware. It’s important to choose hardware that can support the model’s memory and processing requirements.
Latency Requirements: Teams need to understand the acceptable delays between getting an input and returning an output. Applications like autonomous vehicles and fraud detection need outputs in milliseconds, but non-critical cases can rely on batch processing.
Hardware Acceleration: Speeding up inference workloads by using specialized processors is critical. Selecting the right accelerators is dependent on the workload type, performance needs, and resource availability.
Deployment Environment: Where the inference workload will actually run (cloud, on-prem, or edge) matters. The chosen environment should also support security and compliance requirements.
Scalability: The model’s ability to expand inference capacity as the data volume increases is non-negotiable. Container orchestration platforms, like Kubernetes, can be used to automate scaling.
Challenges in AI Inference
Although AI inferencing is a game-changer that will revolutionize how we operate at scale, organizations still face significant challenges in successfully adopting and optimizing it. Enterprise expectations for speed, accuracy, and reliability are continuously increasing, while real-world conditions can introduce hurdles that were not anticipated in testing or training.
The complexity of diverse environments and the direct impact on business outcomes or user experience only serve to complicate matters further. Key challenges include:
Keeping Latency Low: Real-time applications, like self-driving cars and fraud detection, need instant responses. Delays can make AI less effective or even unusable.
Scaling Efficiently: As AI adoption grows, inference workloads must scale without overwhelming infrastructure or driving up costs.
Managing Computational Costs: While inference is generally less resource-intensive than training, running large-scale AI models continuously can still be expensive.
Optimizing Models: Striking a balance between efficiency and accuracy is tricky—simplifying models speeds up inference but may impact performance.
Best Practices for AI Inference Optimization
The goal for AI inference is to improve speed, accuracy, and cost-efficiency. To get the best performance out of inference in AI, efficiency and scalability are key. Models must be optimized in order to meet latency and accuracy targets at scale.
Here’s how to optimize your AI workloads in five steps:
1. Streamline Your Model
Use techniques like quantization and pruning to reduce model complexity without sacrificing accuracy. Quantization converts continuous or high-precision data into discrete or lower-precision data; this reduces storage space, processing time, or computational complexity in exchange for minor reductions in precision.
Pruning removes unnecessary or less important weights from neural networks, which reduces model size and computational complexity by cutting down on steps.
2. Leverage Specialized Hardware
Matching hardware to the specific workload is important because deploying on GPUs, TPUs, or dedicated AI accelerators can significantly speed up inference tasks. It is also useful to optimize batch sizes to fully utilize hardware without increasing latency.
3. Deploy at the Edge
Deploying at the edge refers to running models on or near the device where data is generated. Processing data closer to its origin reduces latency, improves real-time processing, and cuts down on bandwidth costs by avoiding frequent cloud data transfers. This is especially useful for IoT, autonomous systems, and real-time analytics.
4. Scale Smartly
As models take on increasing data volumes, the workload must be able to scale efficiently. Kubernetes cluster management helps automate and optimize inference workloads across distributed environments. It’s also a good idea to balance load clusters to avoid bottlenecks and implement horizontal and vertical scaling strategies.
5. Monitor and Refine
Continuously track model latency, accuracy, and throughput, and update as needed to maintain efficient performance. Additionally, regular monitoring helps detect and address model drift before performance degrades. Models should also be regularly updated with fresh data in order to make sure that outputs reflect real-world trends.
Leveraging Mirantis k0rdent AI for AI Inferencing
Managing AI inference optimization at scale requires a robust containerized infrastructure. Many organizations are dealing with fragmented tools, complex deployment, and the rising costs of scaling AI workloads; Mirantis k0rdent AI is designed to tackle these challenges head-on. Built with proven Kubernetes expertise, Mirantis k0rdent AI offers automation, scalability, and control over AI workloads.
K0rdent AI, an enterprise-grade AI infrastructure solution, simplifies AI deployment with:
Automated Scaling: Dynamically deploys additional pods based on workload demand
Edge Deployment: Carry out real-time AI processing close to data sources
Multi-Tenant GPU Isolation: Securely share GPU resources across workloads while maintaining high performance
Smart Routing: Uphold data sovereignty by directing AI inference workloads to the nearest available compute resource in the correct region
Self-Service Model Deployment: Choose from the Model Catalog for rapid provisioning of ML models
By leveraging Mirantis k0rdent AI as your AI inference platform, organizations can streamline inference operations, enhance workload efficiency, and reduce infrastructure complexity.
Book a demo today and see why Mirantis k0rdent AI is one of the best solutions for enterprise AI Inference.
Frequently Asked Questions
What is Inference in Machine Learning?
Machine learning inference is the process by which a trained AI model applies its learned patterns to new data, generating predictions or classifications in real time. When the model is faced with new data, it runs its learned computations on it and produces a new output. Unlike in training, inference focuses on speed, low latency, and efficiency.
ML Inference and AI Inference: What Is the Difference?
ML inference refers explicitly to a trained machine learning model generating outputs on new data. This must be a statistical or algorithmic model to qualify as machine learning. AI inference, on the other hand, is broader and includes other forms of reasoning along with ML inference. AI inference can also reference a model with multiple reasoning methods.
How Does Machine Learning Inference Work?
The first step is training the model to learn patterns from historical data. After that, the trained model is packaged and deployed into a production environment (cloud, on-prem, or edge). Once incoming data is cleaned, formatted, and structured to the model’s expectations, it can be fed to the model.
This is where inference happens: the model processes the new data using its pre-learned knowledge and outputs a prediction—whether it's recognizing an image, translating text, or detecting fraud.
What Kind of Hardware Is Needed for Inference in Machine Learning?
The hardware needed for ML inference depends on the workload. GPUs are ideal for deep learning models that need high parallel processing, while TPUs are optimized for tensor operations and large-scale deep learning inference. Meanwhile, edge devices help bring AI processing closer to where data is generated, reducing latency and bandwidth usage.
Does AI Inference Use More Computing Power Than Training?
In most cases, AI training demands far more computational power than inference because it involves processing massive datasets to learn patterns. However, inference isn’t always lightweight—especially when dealing with real-time data streams or running large-scale AI applications.
That’s why optimization is key. Techniques like model compression, quantization, and using specialized hardware (like GPUs or AI accelerators) can help reduce inference costs while maintaining speed and accuracy.
Can AI Inference Run at the Edge?
Yes, AI inference can run at the edge, and it is quickly growing in popularity. Running AI models directly on edge devices leads to lower latency, reduced bandwidth costs, increased security, and expanded offline capabilities. It is common in autonomous vehicles, predictive maintenance, and other real-time applications.
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