AI inference vs. training: Key differences and tradeoffs

Training and inference in practice

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

For example, when training an image recognition model, developers might present the algorithm with millions of labeled photos of cats and dogs. The model learns distinctive features like ear shapes, body outlines and facial patterns. With each training iteration, the model improves and might adapt to make fewer errors, such as mistaking a fox for a dog.

Similarly, to build a recommendation system for an e-commerce site, developers might feed the model a detailed history of user behavior, such as clicks, purchases and ratings. The model then learns to identify similarities in user preferences, enabling it to make more accurate suggestions when deployed in real-world scenarios.

Unlike training, inference occurs after a model has been deployed into production. During inference, a model is presented with new data and responds to real-time user queries. When an e-commerce site suggests a product, ChatGPT answers a question or Midjourney generates an image, the underlying model is performing inference based on its training.

Key differences between training and inference

Training and inference are very different processes. Understanding the unique demands of each is critical to building a high-performance, cost-effective machine learning system.

Compute costs

Compute costs are a significant consideration in machine learning, especially for advanced or large-scale models. While data science teams might focus on optimizing model accuracy, data engineers -- and the CFO -- are often more worried about the expense of AI in production.

Model training can be very computationally expensive, requiring large data sets and complex calculations. Inference, although typically less resource-intensive than training, incurs ongoing compute costs once a model is in production.

Over time, inference can therefore become more expensive than training. Whereas training takes place in distinct, intensive phases, inference costs are continuous after deployment. Commercial models, especially those deployed for public use, can have very high inference volume. Such models are typically optimized for more efficient inference, even at the expense of increased training costs.

Resources and latency

One important component of machine learning costs is energy consumption. 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, or improving existing hardware's energy usage, 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 these costs, many organizations 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.

The most common complaint about the use of cloud services for AI is the difficulty of controlling costs, a problem compounded by inadequate administration and governance tools. For instance, training costs can escalate unexpectedly if a development process leads to unusually intensive computations.

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

However, inference entails important cost considerations, too, often related to latency -- the speed at which a model can return results. Real-time applications like augmented reality or generative AI demand very fast responses. In such cases, production models might need to be optimized for low latency or run on specialized hardware to meet performance needs. Latency is generally less important during training unless frequent, intensive retraining is necessary -- for example, in specialized scenarios such as pharmaceutical research.

Making the tradeoff

With finite resources, organizations need to balance the differing demands of training and inference. Improving model performance often involves strategic tradeoffs.

For example, increasing compute resources during inference can improve performance, spreading costs over time and potentially reducing the need for intensive training. But the opposite can be true as well; prioritizing compute for training can yield a very efficient model, which then requires fewer computational resources during inference. There are advantages and drawbacks to each approach.

Overtraining can result in overfitting, where a model learns not only useful patterns, but also noise and other irrelevant fluctuations in the training data. At inference time, this can result in high accuracy on the original training data, but poor generalization to new, real-world data. Overfitted models also tend to suffer from model drift, where accuracy degrades over time.

Model optimization techniques can be useful to curb these issues. Pruning reduces the size of a model after training, which can then reduce the computation required for inference. In some cases, pruning can reduce the disadvantages of overfitting.

In general, consider two key factors when deciding how to prioritize training vs. inference:

• Performance. If performance is critical -- for example, for real-time inference -- organizations might choose to optimize total compute by adjusting resources across both training and inference phases.
• Scale. For large-scale, public-facing models with high inference demand, reducing inference costs takes priority. Choose techniques that reduce inference computing costs, even if this approach requires more training compute.

As hardware and software advance, the difference in resource needs for training vs. inference might diminish. However, the best approach will still need to effectively balance both machine learning processes.

Donald Farmer is the principal of TreeHive Strategy, which advises software vendors, enterprises and investors on data and advanced analytics strategy. He has worked on some of the leading data technologies in the market and in award-winning startups. He previously led design and innovation teams at Microsoft and Qlik.