What is an Inference Engine & Why it’s Essential for Scalable AI

In a recent survey, 68 percent of data professionals named improving the performance of their machine-learning models as a top priority. Fast, cost-effective, and precise real-time predictions can help organizations stay competitive and meet business goals. However, achieving this level of accuracy can be a challenge. As the number of forecasts increases, many machine learning models can experience performance bottlenecks that slow response times and negatively affect prediction accuracy. This is where the inference engine comes in. This article will explore the significance of inference engines in the context of large language models (LLMs) and explain how they can help you deploy AI models that scale effortlessly, delivering fast, cost-effective, and accurate real-time predictions without bottlenecks.

AI inference APIs are a valuable tool for improving inference speed and performance. These application programming interfaces can help you achieve your objectives by seamlessly integrating with your existing model to optimize predictions and reduce costly downtime.

The Challenges in Building an AI Inference Engine for Real-Time Applications

One of the biggest challenges in building an AI inference engine for real-time applications is that, despite the latest advancements in AI processing chipsets, improvements in inference speed can only go so far. A significant part of the AI inferencing time is often associated with bringing reference data into the AI processing engine.

The reference data can include information stored in various databases that need to be queried and updated before the actual AI processing can occur. Accelerating the AI processing portion of an application transaction is helpful, but it doesn’t address the larger problem of high latency.

Run Your AI Inference Platform Where Your Data Lives

As most of the reference data of a latency-sensitive application is stored in a database, it makes sense to run the AI inference engine where the data lives, in the database. That being said, there are a few challenges with this approach:

• In cases where the application data is scattered across multiple databases, which database should the AI inference engine run on? Even if we ignore the deployment complexity and decide to run a copy of the AI inference engine on every database, how do we deal with a situation where a single application transaction requires bringing the reference data from multiple databases?

A recent survey from Percona nicely demonstrated how multiple databases represent the deployment architecture of most applications.

• To achieve the low-latency AI inferencing requirements, reference data should be stored in memory. By adding a caching layer on top of existing databases, this problem can easily be solved. But caching has its limitations. For instance, what happens in cache misses events where the application doesn’t find the data in the cache, is forced to query the data from a disk-based database, and then update the cache with the latest data?

Caching Challenges

In this scenario, the probability of infringing your end-to-end response time SLA is very high. And how do you make sure your database updates are in sync with your cache and immune to consistency problems? Finally, how do you ensure that your caching system has the same level of resiliency as your databases?

The In-Memory Solution

Your application uptime and your SLA will be driven by the weakest link in the chain, your caching system. To overcome the need for maintaining a separate caching layer, the right architecture choice to deploy the AI inference engine is an in-memory database.

Streamlined Data Management

This avoids problems during cache misses and overcomes data-synchronization issues. The in-memory database should be able to support multiple data models, allowing the AI inference engine to be as close as possible to each type of reference data and avoid building high-resiliency across various databases and a caching system.

Use a Purpose-Built, In-Database, Serverless Platform

It is easy to imagine how latency-sensitive applications can benefit from running the AI inference engine in an in-memory database with multiple data models for solving these performance challenges.

The Data Orchestrator

One thing is still missing in this puzzle: Even if everything sits together in the same cluster with fast access to shared memory, who will be responsible for collecting the reference data from multiple data sources, processing it, and serving it to the AI inference engine, while minimizing end-to-end latency?

The Serverless Latency Problem

Serverless platforms, like AWS Lambda, are often used for manipulating data from multiple data sources. The problem with a generic serverless platform for AI inferencing is that users have no control over where the code is executing. This leads to a key design flaw: Your AI inference engine is deployed as close as possible to where your data resides, in your database.

Nevertheless, the serverless platform that prepares the data for AI inference runs outside your database.

The Co-located Serverless Solution

This breaks the concept of serving AI closer to your data, leading to the same latency problems discussed earlier when the AI inference engine is deployed outside your database. There’s only one way to solve this problem: a purpose-built serverless platform that is part of your database architecture and runs on the same shared cluster memory where your data and your AI inference engine live.

Going back to the transactions scoring example, this is how fast (and simple) the solution can look if we apply these principles:

AI in Production

Taking AI to production creates new challenges that did not exist during the training phase. Solving these problems requires many architectural decisions, especially when a latency-sensitive application needs to integrate AI capabilities in every transaction flow.

In conversations with customers who are already running AI in production, we found that in many cases, a significant portion of the transaction time is spent on bringing and preparing the reference data for the AI inference engine, rather than on the AI processing itself.

A Novel AI Inference Architecture

We therefore propose a new AI inference engine architecture that aims to solve this problem by running the system in an in-memory database with built-in support for multiple data models. This architecture utilizes a purpose-built, low-latency, in-database serverless platform to query, prepare, and then bring the data to the AI inference engine.

Once these ingredients are in place, a latency-sensitive application can benefit from running the AI on a dedicated inference chipset, as the AI processing takes up a more significant portion of the entire transaction time.

Cautious AI Integration

Adding AI to your production deployment stack should be done with extreme care. Businesses relying on latency-sensitive applications should follow these suggestions to prevent degradation of the user experience due to slowness in the AI inference engine. In the early days of AI, the slow performance of general-purpose CPUs created headwinds for developers and researchers during the training phase.

As we look toward deploying more AI applications into production, architecting a robust AI inference engine will ultimately separate the winners from the losers in the pending AI boom.

What is an Inference Engine in Machine Learning?

An inference engine is a key component of an expert system, one of the earliest types of artificial intelligence. An expert system applies logical rules to data to deduce new information. The primary function of an inference engine is to infer information based on a set of rules and data. It is the core of an expert system, which applies the rules to a knowledge base to make decisions.

An inference engine can reason and interpret the data, draw conclusions, and make predictions. It is a critical component in many automated decision-making processes, as it helps computers understand complex patterns and relationships within data. Expert systems are still commonly used in:

• Cyber security
• Project management
• Clinical decision support

Newer machine learning architectures have replaced inference engines in many fields, such as decision trees or neural networks. However, they are sometimes used in diagnostic, recommendation, and natural language processing (NLP) processes.

The Core Components of an Inference Engine

An inference engine consists of three core components: a knowledge base, a set of reasoning algorithms, and a set of heuristics.

Knowledge Base

The knowledge base is typically a database that stores all the information the inference engine uses to make decisions. This information can include:

• Facts
• Rules
• Data

About the problem domain. The inference engine uses the knowledge base to infer new information, make predictions, and make decisions.

The knowledge base is a dynamic entity continuously evolving as new data is added or existing data is modified. The inference engine uses this information to make intelligent decisions. The more comprehensive and accurate the knowledge base, the better the inference engine can make informed decisions.

Set of Reasoning Algorithms

Reasoning algorithms are the inference engine's logic to analyze the data and make decisions. The algorithms take the data from the knowledge base and apply logical rules to infer new information.

The type of reasoning algorithms an inference engine uses can vary based on the problem domain and the system's specific requirements. Some common types of reasoning algorithms include deductive, inductive, and abductive reasoning.

Set of Heuristics

Heuristics are rules of thumb or guidelines the inference engine uses to make decisions. They guide the reasoning process and help the inference engine make more efficient and effective decisions.

Heuristics can be based on past experiences, expert knowledge, or other types of information. They simplify the decision-making process and help the inference engine make better decisions.

Functions of an Inference Engine

Rule Interpretation: The Basics

Rule interpretation is a key function of an inference engine, involving the application of predefined rules to input data. These rules guide the decision-making process by specifying conditions and corresponding actions. In rule-based decision-making, the inference engine evaluates whether certain conditions are met and then executes the associated actions.

This process helps automate decisions that would otherwise require human judgment, making it essential in systems such as:

• Expert systems
• Automated troubleshooting tools

Fact Handling: Collecting and Storing Information

Fact handling refers to the process of collecting, storing, and utilizing factual information within an inference engine. Facts are the pieces of data that the engine uses to make decisions or draw conclusions. The collection of facts can come from various sources, including:

• Databases
• User inputs
• External sensors

Once collected, these facts are processed to determine their relevance and usefulness in the decision-making process, enabling the engine to generate accurate and timely outputs.

Inference Making: How Engines Make Decisions

Inference making involves deriving new information or conclusions from existing facts and rules. The inference engine applies logical reasoning to the known data to generate insights or make decisions.

This process is crucial for scenarios where the system must predict outcomes or suggest actions based on the available information. It enables the engine to provide recommendations, solve problems, and dynamically adapt to new data.

Resolution of Uncertainty: Managing Incomplete Data

Handling incomplete or ambiguous data is a significant aspect of inference engines. In real-world scenarios, data may be incomplete or uncertain, and the engine must use various techniques to manage these situations.

Handling Uncertainty

Inference engines employ methods such as probabilistic reasoning or fuzzy logic to address uncertainty and provide the most accurate conclusions possible, despite the limitations of the data. This capability is essential for making informed decisions even when faced with incomplete information.

Explanation and Justification: Providing Transparency

Providing reasoning behind decisions is another critical function of inference engines. Users and systems often need to understand the rationale behind a decision or conclusion. Inference engines offer explanations by detailing the rules and facts that led to a particular outcome.

This transparency helps build trust in the system, allowing users to verify and understand the reasoning behind the decisions made by the engine.

Benefits of Using Inference Engines

Inference engines provide several benefits, especially in decision-making applications.

Enhanced Decision-Making

Inference engines help make informed decisions by systematically analyzing the data and applying pre-set rules. This leads to more accurate and consistent decisions, especially in areas where human judgment might vary or be prone to error.

Efficiency

These engines can process information and make decisions much faster than humans, especially when dealing with large amounts of data. This speed and efficiency can be crucial in time-sensitive environments like healthcare or financial trading.

Cost-Effectiveness

By automating decision-making processes, inference engines reduce the need for continuous human oversight, lowering labor costs and decreasing the likelihood of costly errors.

Consistency

They provide consistent outputs based on the rules defined, regardless of the number of times a process is run or the amount of data processed. This consistency ensures reliability and fairness in decision-making processes.

Handling of Complexity

Inference engines can manage and reason through complex scenarios and data relationships that might be difficult or impossible for humans to analyze quickly and accurately.

Related Reading

• vLLM

How Do Inference Engines Work?

The operation of an inference engine can be segmented into two primary phases:

• The matching phase
• The execution phase

Matching Phase: Scanning for Relevant Knowledge

During the matching phase, the system scans its database to find relevant rules based on its current set of facts or data. This process involves checking the conditions of each rule against the known facts to identify potential matches. If the conditions of a rule align with the facts, that rule is considered applicable. This step is crucial because it determines which rules the inference engine will apply in the execution phase to derive new facts or make decisions. It effectively sets the stage for the engine’s reasoning process during the execution phase.

Execution Phase: Applying Rules to Make Decisions

In the execution phase, the system actively applies the selected rules to the available data. The actual reasoning occurs in this step, transforming the input data into conclusions or actions. The engine processes each rule identified during the matching phase as applicable, using them to infer new facts or resolve specific queries. This logical application of rules facilitates the engine’s ability to make informed decisions, mimicking human reasoning processes. In this phase, the engine demonstrates its capacity to analyze, deduce, and generate outputs based on its predefined logic. The engine considers what it knows in the matching phase and applies that knowledge to make decisions in the execution phase. However, different kinds of inference engines run through this process differently.

Types of Inference Engines: Forward Chaining vs. Backward Chaining

Rule-based inference engines can be broadly categorized into two types:

• Forward chaining
• Backward chaining

Forward Chaining: Data-Driven Problem Solving

A forward-chaining inference engine begins with known facts and progressively applies logical rules to generate new facts. It operates data-driven, systematically examining the regulations to see which ones can be triggered by the initial data set. As each rule is applied, new information is generated, which can then trigger additional regulations. This process continues until no further rules apply or a specific goal is reached. Forward chaining is particularly effective in scenarios where all relevant data is available from the start. This setup makes it ideal for comprehensive problem-solving and decision-making tasks.

Backward Chaining: Goal-Driven Reasoning

In contrast, backward-chaining inference engines start with a desired outcome or goal and work backward to determine actual facts to reach that goal. It’s goal-driven, applying rules in reverse to deduce the conditions or data needed for the conclusion. This approach is beneficial when the goal is known, but the path to achieving it is unclear. Backward chaining systematically checks each rule to see if it supports the goal and, if so, what other facts need to be established. This process makes it highly efficient for solving specific problems where the solution requires targeted reasoning. Many fields can benefit from using both inference engines in different scenarios. Let’s look at some broader ways inference engines can be used.

Optimizing Open-Source LLM Deployment with Inference

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.

Related Reading

• SGlang
• Ollama

Applications of Inference Engines

Expert systems mimic the decision-making ability of human specialists, and inference engines are at their cores. By following complex rules to solve problems, they appear to “think” like humans. However, instead of simply retrieving information from a database, they make logical deductions that allow them to reach conclusions that aren't explicitly stated in their programmed knowledge.

One key advantage of these systems is their ability to handle uncertainty and make informed decisions even when complete information isn't available. In medicine, for example, an expert system can analyze patient information and conclude (or set of possible conclusions) to help a human expert diagnose, even if not all of the data is present.

Diagnostic Systems: Accelerating Medical Diagnosis

Inference engines are also extensively used in diagnostic systems, particularly in medicine. These systems use the inference engine to analyze symptoms, compare them with known diseases, and infer possible diagnoses. The benefit of using an inference engine in diagnostic systems is its ability to process vast amounts of data rapidly and accurately.

It outperforms human capability in speed and precision, making it a valuable tool in medical diagnostics. An inference engine can sift through thousands of medical records, identify patterns, and suggest potential diagnoses. However, it is limited to straightforward logical reasoning and cannot exhibit creativity or identify patterns outside predefined rules.

Recommendation Systems: Tailored Content for Users

Recommendation systems are widely used in online platforms like:

• Amazon
• Netflix
• Spotify

To provide personalized recommendations to users. Some recommendation systems use inference engines to analyze user behavior, identify patterns, and make recommendations based on these patterns.

An inference engine processes the collected data, infers user preferences, and predicts future behavior. Modern recommendation systems augment or replace inference engines with machine learning algorithms like neural networks.

Natural Language Processing: Understanding Human Language

Inference engines also find applications in natural language processing (NLP), where they are used to understand and generate human language. Inference engines were critical in:

• Machine translation
• Sentiment analysis
• Language generation

However, they are quickly replaced by more advanced techniques based on recurrent neural networks and their successor, Transformer architectures.

Best Practices for Using Inference Engines in AI

When using an inference engine, the first step to improving performance is to optimize it for speed and memory usage. This process involves streamlining the data processing pipeline, reducing the complexity of the model, and optimizing the code for efficient execution. Optimization is crucial in real-time applications. For example, a model that classifies images may need to make predictions in milliseconds to deliver a good user experience. If the model has not been optimized for inference, it may take too long to return results, leading to lag and performance issues.

Enhancing Inference Performance with Model Optimization Techniques

Techniques such as quantization and pruning help reduce the model's size to improve inference performance. Various ways can be found to optimize the inference engine to speed up execution.

Hardware acceleration techniques can be particularly beneficial in applications that involve processing large amounts of data or complex computations.

Save Resources by Leveraging Pre-Existing Inference Models

Pre-existing models are inference models that have been created for an existing use case and include a large number of rules and heuristics. By leveraging pre-existing models, you can save time and resources, as you won't have to prepare your model from scratch.

For example, a cybersecurity company analyzing suspicious web traffic can use an existing inference engine with thousands of rules to identify known attacks. This approach enables the organization to get up and running quickly on an essential task without creating a custom model that may take weeks or months to develop.

Audit Inference Outputs for Bias

Bias in machine learning is a serious issue that can lead to inaccurate predictions and unfair outcomes. Therefore, auditing for bias in inference outputs is crucial when using an inference engine in machine learning.

Bias can creep into your inference engine through:

• Biased data
• Rules or heuristics
• Biased decision-making processes

By regularly auditing your system, you can identify and mitigate these biases, ensuring that your system delivers fair and accurate results.

Related Reading

• Gemma LLM
• Llama.cpp

Start Building with $10 in Free API Credits Today!

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.