6 AI Model Optimization Techniques

Artificial intelligence and machine learning models are extraordinarily expensive to develop and operate, and data engineers, AI/ML engineers, and data scientists face the challenge of deciding which data or features they can prune back without negatively affecting model performance or accuracy. This guide discusses six AI model optimization techniques, with a special emphasis on maximizing the value of AI/ML datasets. 

6 AI model optimization techniques

The following strategies can help optimize data for AI applications to improve performance and accuracy.

Noise reduction

Low-relevance, duplicate, and inaccurate information – or “noise” – can negatively affect model performance. Noise reduction tools and techniques help data teams identify and select the most relevant, informative, and valuable data samples for training. 

Taking a more targeted approach to data selection helps reduce training time and costs while optimizing models for the specific tasks they’re designed to perform. Noise reduction results in more accurate and reliable predictions, decisions, and outcomes from AI & ML models.

Rebalancing datasets

An imbalanced dataset is one that contains a disproportionately large number of points for a particular class. An example would be if 80% of the training dataset for an AI hiring application was made up of resumes from white men, even though they only represent around 30% of the working population. Another potential source of imbalance is outliers, or singular data points that are significantly different from the rest. 

Tools like Granica Signal help ensure model accuracy and fairness by automatically detecting and correcting class imbalances in datasets. Rebalancing datasets not only results in fair and unbiased AI outcomes, it also improves accuracy and performance for decisions you can trust. 

Feature ablation

AI and machine learning models comprise multiple parts and features that contribute to their inference capabilities. As a model continues developing, individual components may become less effective or necessary, essentially turning into dead weight that hinders model performance. 

Feature ablation involves measuring the importance of each element to the model’s decision-making capabilities by hiding or replacing them one at a time, and then removing (or “ablating”) those revealed as unnecessary. This process helps streamline AI & ML models to maximize performance without reducing accuracy.

Data imputation

• Mean/median/mode - Filling in missing values using statistical averages.
• Next or previous value - Filling in missing time-series or ordered data with the next or previous value.
• Maximum or minimum value - Replacing missing values with the minimum or maximum of the range.
• K nearest neighbors - Finding the group of nearest examples that don’t contain missing values and using the value that occurs most frequently in that group to replace missing data.
• Missing value prediction - Using a machine learning algorithm to determine the missing final imputation value for a characteristic.
• Fixed value - Replacing a missing datapoint with a fixed value

Synthetic data

Another way to avoid gaps in your data after redacting sensitive or harmful information is with synthetic data generation. This technique replaces redacted information with realistic fake data that protects privacy without negatively affecting model performance or accuracy. 

For instance, Granica Screen automatically identifies personally identifiable information (PII) and other sensitive information in training data, inputs, and outputs, then fills in the blanks with realistic synthetic information. Synthetic data provides the model with all the necessary information for making accurate predictions while mitigating the risk of data leaks.

As an example, the AI-powered hiring application mentioned above would intake personally identifiable information like full names, phone numbers, and addresses that could be extremely damaging if leaked, so it’s an AI security best practice to redact that information. A synthetic data generation tool could replace that info with similar names, numbers, and addresses to protect personal privacy while still giving the model the correct context.

Data combination

• Using application programming interfaces (APIs) to feed data from multiple sources into the AI/ML training pipeline rather than using one large dataset in a single data lake or lakehouse. This can be an effective data cost optimization strategy that still provides models with complete information for accurate inferences.
• Using ensemble learning to combine multiple independent sub-models with different training datasets into one system. This technique can improve AI security by reducing the impact on overall performance if one dataset is compromised in an attack.
• Using retrieval-augmented generation (RAG), which allows models to supplement the information in the training dataset with information from outside sources. RAG ensures that models can answer questions or make predictions in scenarios that weren’t explicitly covered in the training data, reducing the size (and cost) of training datasets while improving performance.

AI model optimization with Granica

Granica is an AI data readiness platform that helps organizations optimize large-scale datasets for data science and machine learning (DSML) workflows. Granica Signal is a model-aware data selection and refinement solution that improves model performance by automatically reducing noise, improving relevance, and correcting imbalances in AI/ML training datasets. The Granica Screen “Safe Room for AI” detects sensitive and unwanted information in tabular and natural language processing (NLP) data during training, fine-tuning, inference, and RAG. It generates realistic synthetic data to protect privacy and aid in compliance while maximizing model performance.