What is Inference in Machine Learning & Why Does It Matter?

Imagine you’ve just built a machine learning model to predict customer churn for your business. Your model is accurate, with a high score on both the training and test datasets. But when you deploy the model to a production environment, it performs poorly. What went wrong? In this scenario, you likely rushed the inference process, neglecting to test how the model would perform on new data. In short, the issue was not with the model itself, but with inference.

What is inference in machine learning? This article will define it, explain its significance, and explore how to improve it to deploy better AI models. Inference’s AI inference APIs can help you achieve your objectives by providing the tools to manage AI inference so you can build smarter, faster, and more impactful AI-driven systems.

What is Inference in Machine Learning?

Inference in machine learning refers to using a trained model to make predictions or decisions based on new input data. Inference can be considered the operationalisation of an ML model or putting an ML model into production. An ML model running in production is often described as artificial intelligence (AI) since it performs functions similar to:

• Human thinking
• Analysis

Inference in machine learning entails deploying a software application into a production environment. The ML model is typically just software code that implements a mathematical algorithm. That algorithm makes calculations based on the characteristics of the data, known as feature in the ML vernacular.

Lifecycle Stages

An ML lifecycle can be divided into two distinct parts. The first is the training phase, in which an ML model is created or trained by running a specified subset of data through it. The second is ML inference, in which the model is put into action on live data to produce actionable output.

The data processing by the ML model is often referred to as scoring, so the ML model scores the data, and the output is a score.

Deployment Roles

DevOps engineers or data engineers generally deploy:

• ML
• AI inference

Collaborative Deployment

Sometimes, the data scientists training the models are asked to own the ML inference process. This latter situation often causes significant obstacles to the ML inference stage since data scientists are not necessarily skilled at deploying systems.

Successful ML deployments are often the result of tight coordination between different teams, and newer software technologies are also often deployed to try to simplify the process. An emerging discipline known as MLOp is starting to put more structure and resources around getting ML models into production and maintaining those models when changes are needed.

How Does AI Inference in Machine Learning Work?

Training the Model

Trained models are the products of rigorous learning from historical data. They encapsulate the knowledge acquired during the training phase, storing information about the relationships between:

• Inputs
• Outputs

The quality of the model directly impacts the accuracy and reliability of AI inference. The journey of AI inference begins with training a machine learning model.

Model learning

During this phase, the model is exposed to a vast amount of labeled data, allowing it to:

• Recognize patterns
• Establish connections between inputs and outputs

This is akin to providing the model with a comprehensive textbook to learn from.

Model Architecture

The architecture of the model, often a neural network, plays a crucial role. It consists of layers of interconnected nodes, each contributing to extracting features and patterns from the input data. The complexity of the architecture depends on the nature of the task for which the AI system is designed.

Feature Extraction

Once trained, the model can extract relevant features from new, unseen data. These features are the distinctive characteristics that the model has learned to associate with specific outcomes.

Input Data

The input data serves as the fuel for the AI inference engine. The model processes this data, extracting relevant features and patterns to generate predictions. The diversity and representativeness of the input data are crucial for the model to generalize well to new, unseen situations.

When presented with new data, the model processes it through its layers of nodes. Depending on the application, this input data could be anything from an image to a piece of text or a set of sensor readings.

Forward Pass

The forward pass is the process where input data is fed into the model, layer by layer, to generate an output. Each layer contributes to the extraction of features, and the weighted connections between nodes determine the output. The forward pass allows the model to make predictions in real time.

The input data traverses through the model's layers during the forward pass. At each layer, the model applies weights to the input features, producing an output that becomes the input for the next layer. This iterative process continues until the data reaches the output layer, resulting in a prediction or decision.

Output Prediction

The final output represents the AI system's prediction or decision based on the input data. This could be identifying objects in an image, transcribing spoken words, or predicting the next word in a sentence.

The Backward Pass

The backward pass is integral to the training phase but still relevant to understanding AI inference. It involves updating the model based on the feedback obtained from the predictions. If there are discrepancies between the predicted output and the actual outcome, the model adjusts its internal parameters during the backward pass, improving its future predictions.

Related Reading

• Neural Network vs. Machine Learning
• What is a Token in AI

Machine Learning Inference vs Training

The first thing to remember is that machine learning inference and machine learning training are different, and each concept is applied in two distinct phases of any machine learning project. This section provides an intuitive explanation to highlight their differences through Cassie Kozyrkov's restaurant analogy.

She mentioned that making a good pizza (a valuable product) requires a recipe (model or formula) that tells how to prepare the ingredients (quality data) with what appliances (algorithms).

Team Synergy

There would be no service if no food came from the kitchen. Also, the kitchen (Data Science Team) will not be valuable if clients don’t constantly appreciate the food.

Both teams work together for a good customer experience and better return on investment.

Machine Learning Training: The Kitchen Side to Making Predictions

Training a machine learning model requires the use of training and validation data. The training data is used to develop the model, whereas the validation data is used to fine-tune the model’s parameters and make it as robust as possible.

This means that at the end of the training phase, the model should be able to predict new data with fewer errors. We can consider this phase as the kitchen side.

Machine Learning Inference: Serving Dishes to Customers

Dishes can only be served when ready to be consumed, just as the machine learning model needs to be trained and validated before it can be used to make predictions. Machine learning inference is similar to the scenario of a restaurant. Both require attention for better, more accurate results and customer and business satisfaction.

Why the Differences Between Machine Learning Inference and Training Matter

Knowing the difference between machine learning inference and training is crucial because it can help better allocate the computation resources for both training and inference once deployed into the production environment.

Model performance usually decreases in the production environment. Proper understanding of this difference can help in adopting the right industrialization strategies for the models and maintaining them over time.

Key Considerations When Choosing Between Inference And Training

Using an inference model or training a brand new model depends on:

• The type of problem
• The end goal, and the existing resources

The key considerations include, but are not limited to, time to:

• Market
• Resource constraints
• Development cost
• Model performance
• Team expertise

Time To Market

It is important to consider the available resources when choosing between training and using an existing model. Using a pre-trained model requires less time and may give a business team a competitive advantage.

Resources Constraints or Development Cost

Depending on the use case, training a model can require a significant amount of data and training resources. However, using an inference model requires fewer resources, which makes it easier to obtain even better performance in a short amount of time.

Model Performance

Training a machine learning model is an iterative process that does not always guarantee a robust model. Using an inference model can provide better performance than an in-house model. Nowadays, model explainability and bias mitigation are crucial, and inference models may need to be updated to consider those capabilities.

Team Expertise

Building a robust machine learning model requires strong expertise in model training and industrialization. Having that expertise available can be challenging, and relying on inference models can be the best alternative.

Related Reading

• What is Quantization in Machine Learning
• Batch Learning vs. Online Learning
• Feature Scaling in Machine Learning

The Role of AI Inference in Decision-making

AI inference helps to make sense of data. AI systems use trained models to analyze the data and produce actionable insights when new information is collected. These insights can help human decision-makers:

• Optimize operations
• Personalize customer experiences
• Detect fraud
• Uncover critical patterns to boost performance across various business functions

AI inference can help financial institutions uncover risks and improve loan approval decision-making. When assessing a loan application, an AI model can analyze the applicant’s data and provide insights on their likelihood to default based on patterns learned from historical data.

Objective Decisions

Instead of relying solely on human judgment, which could be biased or miss critical details, the model’s data-driven approach can help the financial institution make a more objective decision.

The Importance of Real-Time Analysis in Dynamic Environments

One of the most significant advantages of AI inference is its ability to process information in real-time. This capability is crucial in dynamic environments where timely decisions can differentiate between:

• Success
• Failure

From financial trading to autonomous vehicles navigating traffic, AI inference ensures rapid:

• Analysis
• Response

Real-time Impact

In algorithmic trading, AI inference can analyze market conditions and execute trades in mere milliseconds, far outperforming human traders. In health care, AI inference can help doctors detect anomalies in imaging scans and alert them to potential health risks in real time, allowing for quicker diagnosis and treatment.

Complex Pattern Recognition: How AI Inference Surpasses Human Abilities

Humans have limitations in processing complex patterns and large data sets swiftly. AI inference excels in this domain, offering a pattern recognition and analysis level that can surpass human capacities. This capability is evident in applications such as medical diagnostics and fraud detection, where nuanced patterns may be subtle and easily overlooked by human observers.

In medical imaging, AI can help radiologists detect tumors or lesions in X-rays, CT scans, or MRIs by identifying patterns in the imaging data that correlate with certain types of cancer. Even the most experienced doctors may overlook these anomalies, which could delay patient treatment.

Fraud Prevention

In fraud detection, AI inference can analyze historical transaction data to identify behaviors that correlate with fraudulent activity. By continuously monitoring transactions in real time, AI can flag potentially fraudulent activity for human review, helping organizations:

• Reduce losses
• Improve compliance with regulatory requirements

Consistent and Unbiased Decision-Making with AI Inference

AI inference operates consistently without succumbing to fatigue or bias, two factors that can affect human decision-makers. This consistency ensures that external factors do not influence decisions, leading to more objective and impartial outcomes.

AI inference can help remove bias from hiring decisions. When a business receives applications for open roles, an AI model can analyze the data on candidates and make recommendations on who to interview. Because the model processes data, it can ignore potentially biased attributes, such as names or addresses, indicating a candidate’s demographic background.

The Benefits of Relying on AI Inference to Make Decisions

AI inference offers several benefits that can enhance decision-making processes across industries. Here are a few of the most notable advantages:

Efficiency

AI inference operates at incredible speeds, enabling efficient processing of large data sets and swift decision-making. This efficiency can:

• Optimize workflows
• Enhance overall productivity

Accuracy

When provided with quality data, trained models can achieve high levels of accuracy. This accuracy is especially valuable in domains where precision is paramount, such as:

• Medical diagnoses
• Quality control in manufacturing

Scalability

AI inference can scale effortlessly to handle large volumes of data. As the volume of data increases, AI systems can adapt and continue to provide valuable insights without a proportional increase in resources.

The Limitations of AI Inference in Decision-Making

Despite its many advantages, organizations must also recognize the limitations of using AI inference to make decisions. Here are a few notable drawbacks to consider before implementation:

Lack of Context Understanding

AI systems may struggle with understanding the broader context of a situation, relying solely on the patterns present in the data they were trained on. This limitation can lead to misinterpretation in situations where context is critical.

Overreliance and Blind Spots

Overreliance on AI inference without human oversight can result in blind spots. AI systems may not adapt well to novel situations or unexpected events, highlighting the importance of balancing:

• Automated decision-making
• Human intervention

Ethical Concerns

The use of AI inference introduces ethical considerations, including issues related to:

• Bias
• Fairness
• Accountability

If the training data contains biases, the AI system may perpetuate and amplify these biases in decision-making.

Bias and Fairness

The training data used to develop AI models may contain biases. These biases can lead to discriminatory outcomes, disadvantaging certain groups if not addressed. Ethical AI inference requires continuous efforts to identify and mitigate bias in algorithms.

Transparency

AI models, especially complex neural networks, can be viewed as black boxes. The lack of transparency in how these systems arrive at decisions raises concerns. Ethical decision-making with AI inference involves striving for openness and explainability to build trust among users and stakeholders.

Accountability

Determining accountability in the event of AI-driven decision errors poses a challenge. Establishing clear lines of responsibility and accountability is crucial for ethical AI inference. Developers, organizations, and regulatory bodies all play roles in ensuring responsible AI use.

Human Oversight

Ethical decision-making demands human oversight in AI systems. While AI inference can provide valuable insights, the final decision-making authority should rest with humans, ensuring that moral considerations are considered and decisions align with societal values.

Related Reading

• LLM Embeddings
• Domain Adaptation

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