AI Inference vs Training: A Guide to Performance and Scalability

AI inference and training are two crucial aspects of artificial intelligence that often get confused with each other. Inference is the process of using a trained model to make predictions or take actions, while training is the process of updating the model's parameters to improve its performance.

Inference is typically faster and more energy-efficient than training, with some models achieving speeds of up to 1000 times faster. This is because inference only requires a forward pass through the network, whereas training requires multiple passes through the network to adjust the weights.

The key difference between inference and training is the purpose of the computation. Inference is used to generate predictions or take actions, while training is used to improve the model's performance. This distinction is crucial for understanding the trade-offs between performance and scalability in AI systems.

AI inference is the process of using a pre-trained model to make predictions or take actions based on new, unseen data. This process is typically faster and more efficient than training a model from scratch.

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Inference involves running the pre-trained model through a series of calculations to generate a result, which can be a prediction, classification, or recommendation. The model is essentially "inferring" the correct answer based on its prior training.

The key difference between inference and training is that inference uses a pre-trained model, whereas training builds a model from scratch using a large dataset and computational resources. Training can take hours, days, or even weeks to complete, while inference can happen in a matter of milliseconds.

Inference is commonly used in applications such as image recognition, natural language processing, and recommender systems. These systems rely on pre-trained models to make predictions or take actions based on user input or new data.

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Understanding the unique demands of training and inference is critical to building a high-performance, cost-effective machine learning system.

Training and inference are very different processes. During training, the AI model is fed pictures with annotations telling it how to think about each piece of data.

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The training process can be thought of like Watson, still learning how to observe and draw conclusions through inference. Once it's trained, it's an inferring machine, a.k.a. Sherlock Holmes.

Inference can take over once the training is complete. The AI algorithm uses the training to make inferences from data, which means it's making predictions or decisions based on what it's learned.

The difference between training and inference can be summed up fairly simply: first you train an AI algorithm, then your algorithm uses that training to make inferences from data.

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Compute and resources are crucial aspects of machine learning, especially when it comes to AI inference vs training. Model training can be very computationally expensive, requiring large data sets and complex calculations.

Inference, on the other hand, incurs ongoing compute costs once a model is in production. This can become more expensive than training over time, especially for commercial models with high inference volume.

To manage these costs, many organizations build their machine learning infrastructure on cloud platforms, which offer scalability, flexibility, and access to specialized hardware required for efficient training and inference.

Intensive computations consume a great deal of energy, which not only results in higher operational costs, but also raises environmental concerns.

Using more energy-efficient hardware, such as tensor processing units and field-programmable gate arrays, can reduce the environmental footprint of AI systems.

Specialized accelerators like tensor processing units and field-programmable gate arrays offer more energy-efficient alternatives to more common, general-purpose GPUs.

To manage energy consumption, organizations can build their machine learning infrastructure on cloud platforms to take advantage of their scalability and flexibility.

Cloud platforms might also offer access to the specialized hardware required for efficient training and inference.

Controlling inference costs is typically simpler because each request uses relatively few resources, but cost-control measures often include throttling the number of inferences a user can request in a given time window.

Real-time applications like augmented reality or generative AI demand very fast responses, requiring production models to be optimized for low latency or run on specialized hardware to meet performance needs.

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Latency is generally less important during training unless frequent, intensive retraining is necessary, such as in specialized scenarios like pharmaceutical research.

Some use cases can tolerate higher latency, such as big data analytics, which can run these analyses in batches based on the frequency of inference queries.

Mission-critical applications often require real-time inference, such as autonomous navigation, critical material handling, and medical equipment.

Training is an experimental process that involves presenting a model with data, adjusting its parameters to minimize prediction errors, and iterating until developers are happy with the results.

Developers might present an image recognition model with millions of labeled photos of cats and dogs to learn distinctive features like ear shapes, body outlines, and facial patterns.

The training process involves validating a model's performance and iterating until it improves and adapts to make fewer errors.

Inference occurs after a model has been deployed into production, where it responds to real-time user queries based on its training.

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A recommendation system for an e-commerce site might be built by feeding a model a detailed history of user behavior, such as clicks, purchases, and ratings.

During inference, a model is presented with new data and responds to real-time user queries, like suggesting a product or answering a question.

Donald Farmer, a principal of TreeHive Strategy, advises software vendors and enterprises on data and advanced analytics strategy, and has worked on leading data technologies in the market.

Scaling machine learning inference workloads is crucial for data scientists, and Run:ai makes it possible to automate resource management and orchestration for machine learning infrastructure.

Run:ai's advanced visibility feature allows for efficient resource sharing by pooling GPU compute resources, creating an efficient pipeline of resource sharing.

With Run:ai, you can set up guaranteed quotas of GPU resources to avoid bottlenecks and optimize billing, no more worrying about running out of resources in the middle of a critical project.

Run:ai enables dynamic resource allocation, ensuring each job gets the resources it needs at any given time, giving you a higher level of control over your machine learning infrastructure pipelines.

Here are some key benefits of using Run:ai for performance and scalability:

Latency is a crucial aspect of machine learning performance, especially when it comes to inference systems. It's the speed at which a model can return results, and it's essential to consider it when building production models.

In mission-critical applications, real-time inference is often required. This includes autonomous navigation, critical material handling, and medical equipment, where delays can have serious consequences.

Some use cases, like big data analytics, can tolerate higher latency. These analyses can be run in batches based on the frequency of inference queries, allowing for more flexibility.

The acceptable latency for an inference system depends on the specific use case. For example, autonomous navigation requires a maximal latency of milliseconds, while big data analytics can tolerate seconds or even minutes.

To manage latency, production models may need to be optimized for low latency or run on specialized hardware. This can be especially important for real-time applications like augmented reality or generative AI.

Here are some common latency requirements for different use cases:

You can automatically run inference workloads at any scale, on any type of computing infrastructure, whether on-premises or in the cloud with Run:ai.

Run:ai automates resource management and orchestration for machine learning infrastructure, making it easy to set up and manage complex pipelines.

By pooling GPU compute resources, you can create an efficient pipeline of resource sharing, eliminating bottlenecks and optimizing billing.

Run:ai enables you to dynamically change resource allocation, ensuring each job gets the resources it needs at any given time.

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With Run:ai, you can set up guaranteed quotas of GPU resources, avoiding bottlenecks and optimizing billing.

When selecting hardware for AI inference, consider using Amazon Web Services (AWS) EC2 instances, which are specifically designed for machine learning workloads.

AWS EC2 instances offer various configurations to suit different needs, but selecting the right one can be overwhelming.

To run machine learning and deep learning inference workloads, common hardware systems include those from Amazon Web Services (AWS) and other providers.

Some popular options include AWS EC2 instancesOther hardware systems, which can be chosen based on the specific requirements of the project.

For example, AWS EC2 instances come in different sizes and can be selected based on the amount of memory and processing power needed for the workload.

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A server is essentially a computer program that performs a specific task, like executing a machine learning model and returning an output. This is known as a machine learning inference server.

These servers work by accepting input data, passing it to a trained model, executing the model, and returning the inference output. This process is crucial in many applications.

The Apple Core ML inference server is a specific example of a machine learning inference server, and it can only read models stored in the .mlmodel file format. This file format is specific to the Apple Core ML environment.

Using the Open Neural Network Exchange Format (ONNX) can improve file format interoperability between various ML inference servers and model training environments. This makes it easier to move models between different systems.

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AWS EC2 instances are a popular choice for machine learning workloads. You can select the right instance for your needs, as explained in the article "Selecting an AWS EC2 instance for machine learning workloads" by Ernesto Marquez.

AI inference is a key concept to understand when working with machine learning models. According to Sean Kerner, AI inference is the process of using a trained model to make predictions or take actions on new, unseen data.

Amazon Bedrock and SageMaker JumpStart are two options for building AI apps. Ernesto Marquez compares these two services in the article "Amazon Bedrock vs. SageMaker JumpStart for AI apps".

High-bandwidth memory is in high demand due to the increasing need for AI model training. Adam Armstrong explains why this is the case in the article "AI model training drives up demand for high-bandwidth memory".

Here's a brief overview of some key technologies mentioned in the article sections:

Interoperability is a crucial aspect of deploying ML models in production. Different teams use various frameworks like Tensorflow, Pytorch, and Keras to develop their models.

Containerization has become a common practice that can ease deployment of models to production. This is especially true for large-scale deployments where Kubernetes is often used to organize models into clusters.

Kubernetes makes it possible to deploy multiple instances of inference servers and scale them up and down as needed across public clouds and local data centers.

GPUs are specialized hardware components that can perform numerous simple operations simultaneously. They're like super powerful calculators that can do many things at the same time.

GPUs have a similar structure to CPUs, but they're designed for parallel execution, which makes them ideal for deep learning execution. This means they can handle complex tasks much faster than traditional CPUs.

GPUs include thousands of Arithmetic Logic Units (ALUs) that enable the parallel execution of many simple operations. This is a key feature that sets GPUs apart from CPUs.

GPUs consume a large amount of energy, which can be a problem for running them on edge devices. This is why standard GPUs might not be suitable for use on many edge devices.

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