Polishing Up Your AI Model: Optimization That Drives Success

When the honeymoon stage is over, you may find your AI model under-deliver on its promises. Model drift, operational inefficiency, and other hitches creep into the system, nullifying the initial gains. That’s why it’s crucial to pour an equal amount of resources into the development and AI model optimization to maximize the performance of your smart solution and prevent it from going south.

In this article, our AI engineers have curated practical advice about the selection of artificial intelligence optimization techniques along with real-world examples of optimization in artificial intelligence.

What is optimization in AI? Turning your models from good to great

Artificial intelligence optimization is a process of refining the performance and efficiency of existing machine learning models. Although fine-tuning is considered to be an optional, after-the-fact step, it’s the brushstroke that turns your pre-trained AI model from a generalist into a niche expert.

More importantly, optimization algorithms allow your model to bypass data, time, and computational limitations, improving its abilities without eating into your resources.

What problems does AI optimization address?

On a high level, AI optimization aims to slash computational load and memory usage along with improving the model’s performance. Beyond this overarching objective, companies can leverage this technique to tackle a raft of other challenges inherent to AI initiatives.

High costs of training and running AI models

The power of large and complex deep learning models usually comes at a cost as their sophisticated architecture and formidable data requirements make them expensive to train and run. Optimization techniques can slim down the necessary computational resources, helping companies save money — especially on cloud deployments based on pay-as-you-go models.

Slow performance and latency

Low latency is an essential prerequisite for real-time data analysis solutions, such as autonomous vehicles and real-time fraud detection systems. However, increasing prediction accuracy often leads to longer prediction times. AI optimization techniques can enhance model speed and reduce inference time, maintaining both low latency and high prediction precision without compromising either.

Overfitting

Overfitting often plagues AI models, hindering their ability to perform well when faced with an unseen set of data. The reasons for that are manifold, including high model complexity, small training data size, and dominance of noisy data. 

Optimization techniques such as data augmentation (creating more diverse training data), early stopping (preventing overtraining), and others can offset this defect, improving the model’s ability to generalize to new data. This, in turn, enhances the model’s accuracy for real-world scenarios.

Lack of interpretability

The more advanced your model is, the harder it will be to trace its decision-making process. The black-box nature of sophisticated algorithms also makes models hard to debug and undermines trust in their results.

Although AI model optimization isn’t a cure-all solution, this exercise can usher in more transparency into the opaque nature of artificial intelligence. For example, companies can apply rule extraction techniques, gaining more insight into the behind-the-scenes of the model. Combined with explainable AI, these techniques can peer deep into the model and rationalize specific predictions.

Deployment challenges

Large deep learning models guzzle a truckload of processing power and device memory, which makes their deployment challenging for resource-constrained environments such as those involving IoT devices. AI fine-tuning makes smart models leaner by trimming away unnecessary parameters and refining the model’s architecture. 

Some techniques also enable the model to use lower-precision data types instead of the bulky high-precision formats. Unlike their heavyweight counterparts, lightweight AI models have more deployment options and can integrate with new applications and devices.

Key techniques for AI model optimization

To choose the right optimization technique for your model, you need to analyze several factors, including the model’s type, the deployment environment, and your optimization goals. Below, our data science team has broken down the key optimization techniques along with their ideal use cases.

Model architecture optimization

Whenever your model is facing resource constraints, like limited memory or computational power limitations, it’s in for an architecture touchup. Architecture optimization is also beneficial for the deployment efficiency and the accuracy of predictions.

Pruning

Similar to tree pruning, this technique optimizes the model by selectively removing redundant or unimportant parameters, artificial neurons, weights, or deep learning network layers, thus improving the model’s core structure. A proper prune is an investment in the speed and nimbleness of the model. Pruning can also be used as a regularization technique to prevent overfitting.

When to use? Use pruning when you need to reduce computational complexity and inference time. This is typically the case with tight deployment constraints, such as limited memory or processing power on edge devices. For example, mobile phone manufacturers apply pruning to enable on-device image recognition, reducing battery consumption and improving responsiveness.

Keep in mind that this optimization technique can lead to a slight drop in accuracy.

Quantization

Quantization allows you to substitute floating-point weights and/or activations with low precision compact representations. In simple words, this technique transforms a heavyweight AI model, which employs a lot of numbers to make predictions, into a more agile model with a reduced number of bits used for those numbers. 

By paring down the model, quantization makes it faster while also reducing memory footprint. However, the boost in speed may come at the expense of prediction accuracy so you should select this optimization technique for tasks where you can keep the accuracy loss to the minimum.

When to use? Quantization is the go-to option for on-device machine learning tasks, resource-constrained environments, like embedded systems and IoT devices, and cloud-based AI.

For instance, quantization is the secret behind real-time voice interactions on smart speakers that leverage the technique to operate efficiently on low-power hardware.

Knowledge distillation

Knowledge distillation is one of the general-purpose optimization algorithms in artificial intelligence popular for model compression. This technique condenses knowledge from a pre-trained, complex ‘teacher’ model into a simpler and smaller ‘student’ model to improve the performance of the latter. By building on the teacher’s wisdom, the optimized model can achieve comparable performance on the same task, but at less cost.

When to use? Knowledge distillation can benefit multiple AI solutions, including natural language processing, speech recognition, image recognition and object detection. You can use this optimization technique for one of these tasks, provided you have a pre-trained, high-performance model that can then power a smaller model for real-time applications.

For example, knowledge distillation enables a small chatbot model to increase response time and refine its conversation flow by using a large, pre-trained language model as a ‘teacher’.

Data optimization

In a perfect world, all AI models are fed on accurate, high-quality, and balanced data that lays the ground for impeccable performance. In reality, training data often falls short, causing insufficient model adaptability and subpar performance. Data optimization techniques can alleviate issues caused by poor training data while also promoting better results.

Data augmentation

Data augmentation allows data science teams to artificially ramp up the training set by creating new training data from existing data samples. This technique increases the volume, equality, and diversity of training data, allowing the model to bypass the limitations caused by small or imbalanced datasets. Expanded sets of training data, in turn, mitigate overfitting and reduce data dependency.

When to use? Data augmentation helps your model fare well against the backdrop of limited training data, which is a common issue in tasks like image recognition.

For example, data augmentation makes up for limited data variations, which is a well-known hurdle for medical imaging models targeted at rare disease detection. 

Autonomous vehicles also rely on augmented datasets to overcome limitations in real-world data and, ultimately, navigate in diverse weather conditions.

Data distillation

Akin to knowledge distillation for models, dataset distillation helps a smaller model pick the brains of a larger model trained on a massive dataset. This optimization technique allows the smaller model to transfer specific data points, relationships, and internal representations of the data from the larger one, making the smaller model faster and less computationally expensive.

When to use? Data distillation comes to the rescue when you have limited access to a large dataset due to privacy concerns or storage constraints, but you can leverage a pre-trained model, already enriched by a vast dataset on a similar task.  

For example, dataset distillation is a game-changer in the development of customer service chatbots that often lack training data due to the dynamic nature of customer interactions. 

Training optimization

As our experience shows, companies often look for ways to train a model on a dime due to the limited computational resources and budget considerations. Training optimization techniques help strike a balance between the cost and quality of training, without performance trade-offs.

Hyperparameter tuning

Hyperparameter tuning allows you to train your model sequentially with different sets of hyperparameters to increase its training speed, convergence, and generalization capabilities. During this process, the team selects the optimal values for a machine learning model’s hyperparameters, such as learning rate, batch size, number of epochs, and others.

Hyperparameter tuning is an iterative process where data scientists experiment with different hyperparameter settings, assess the model performance, and iterate accordingly.

When to use? You can resort to hyperparameter tuning whenever you need to squeeze out the best possible performance or solve complex optimization problems in artificial intelligence. This optimization technique also allows teams to work around limited datasets and facilitate model adaptation when working with a new dataset. 

Hyperparameter tuning proves beneficial for different ML tasks, including stock price forecasting, fraud detection, drug response prediction, and more. 

Early stopping

This optimization algorithm helps avoid overfitting that typically occurs when training a learner with an iterative method such as gradient descent. Early stopping monitors the model’s performance on a validation set and shuts down training when the validation accuracy starts to degrade. 

When to use? Early stopping helps data science teams deal with complex models or limited training data — when it becomes critical to prevent overfitting.

For example, this fine-tuning technique helps optimize training time for computationally expensive models.

Gradient clipping

During training, the model adjusts its weights based on the calculated gradients. Gradient clipping ‘clips’ the size of the slopes and sets a threshold — maximum value — for them to tackle the exploding gradients problem and improve the stability of the training process. In some cases, this technique can also keep the training process from oscillating and potentially result in faster convergence.

When to use? Gradient clipping is a linchpin technique for any neural architecture that is prone to grow too big.

The application area of gradient clipping is massive, ranging from NLP tasks to reinforcement learning environments and other machine learning techniques. For example, by stabilizing the training process, gradient clipping smooths training for NLP models, enabling them to achieve a nuanced understanding and generation of natural language.

How much does it cost to optimize an AI model?

It’s hard to pin down an exact figure when talking about optimization cost as the final pricing varies greatly based on several variables, including: 

• The complexity of the AI model — sophisticated deep learning models demand more effort and resources to optimize.
• Optimization techniques — some techniques like data distillation require significant computational resources and specific hardware.
• Deployment scale — large-scale deployments increase optimization costs.
• Data storage — large datasets require cloud or on-premise storage solutions.
• Data labeling — manual labeling can increase your optimization costs.

Instinctools’ data science team employs automation tools within our MLOps approach to reduce the time and effort needed for tasks like data pre-processing or hyperparameter search. We also recommend optimizing for the most important metrics, instead of spreading the optimization efforts too thin, to make the entire process easier on the budget.

AI optimization is a balancing act

While artificial intelligence and optimization make a powerful duo, excessive fine-tuning can lead to diminishing returns and drops in model performance. To get AI model optimization right, you also have to strike the right balance between accuracy and computational demand — or else you’ll end up with longer training times, higher costs, and limited deployment options.