Improving Model Inference for Better Speed, Accuracy, & Scalability

Imagine you have a top-notch AI model that can detect cancer cells in medical imagery with 99% accuracy. That's great! Now, imagine deploying that model into an application used by doctors to assist them with diagnosing patients. It won't be beneficial if the model takes 10 minutes to deliver predictions. Inference, or extracting insights from a trained AI model, is crucial for the model to be practical and valuable. The faster and more accurately the model can deliver predictions, the better. In this post, we will talk about model inference and its importance within AI inference. We will also touch upon the difference between AI Inference vs Training.

Model inference is the process of using machine learning to make predictions. Learn how AI inference APIs enable blazing-fast, accurate, and scalable inference while minimizing costs and latency.

What is Model Inference in Machine Learning?

Model inference in machine learning refers to the operationalization of a trained ML model, i.e., using an ML model to generate predictions on unseen real-world data in a production environment.

The inference process includes processing incoming data and producing results based on the patterns and relationships learned during the machine learning training phase.

Optimized Model Inference for Performance

The final output could be a classification label, a regression value, or a probability distribution over different classes. An inference-ready model is optimized for performance, efficiency, scalability, latency, and resource utilization.

The model must be optimized to run efficiently on the target platform to handle large volumes of incoming data and promptly generate predictions. This requires selecting appropriate hardware or cloud infrastructure for deployment, typically an ML inference server.

Two Common Ways of Performing Inference

There are two common ways of performing inference:

• Batch inference: Model predictions are generated on a chunk of observations after specific intervals. It is best suited for low-latency tasks, such as analyzing historical data.
• Real-time inference: Predictions are generated instantly when new data becomes available. This method is best suited for real-time decision-making in mission-critical applications.

To illustrate model inference in machine learning, consider an animal image classification task, i.e., a trained convolutional neural network (CNN) to classify animal images into various categories (e.g., cats, dogs, birds, and horses). When a new image is fed into the model, it extracts and learns relevant features, such as:

• Edges
• Textures
• Shapes

Applications of Machine Learning Model Inference

The final layer of the model provides the probability scores for each category. The category with the highest probability is the model's prediction for that image, indicating whether it is a cat, dog, bird, or horse.

Such a model can be valuable for various applications, including the following:

• Wildlife monitoring
• Pet identification
• Content recommendation systems

Some other common examples of machine learning model inference include:

• Predicting whether an email is spam
• Identifying objects in images
• Determining sentiment in customer reviews

Benefits of ML Model Inference

Decisions create value – not data. Model inference facilitates real-time decision-making across several verticals, which is especially vital in critical applications such as:

• Autonomous vehicles
• Fraud detection
• Healthcare

These scenarios demand immediate and accurate predictions to ensure safety, security, and timely action.

Examples of ML Model Inference in Decision-Making

Real-time model inference for weather forecasting based on sensor data enables geologists, meteorologists, and hydrologists to accurately predict environmental catastrophes like:

• Floods
• Storms
• Earthquakes

In cybersecurity, ML models can accurately infer malicious activity, enabling network intrusion detection systems to respond to threats and actively block unauthorized access.

Automation & Efficiency

Model inference significantly reduces the need for manual intervention and streamlines operations across various domains. It allows businesses to take immediate actions based on real-time insights. For instance, in customer support, chatbots powered by ML model inference provide automated responses to user queries, resolving issues promptly and improving customer satisfaction.

In enterprise environments, ML model inference powers automated anomaly detection systems to identify, rank, and group outliers based on large-scale metric monitoring. In supply chain management, real-time model inference helps optimize inventory levels, ensuring the right products are available at the right time, thus reducing costs and minimizing stockouts.

Personalization

Model inference enables businesses to deliver personalized user experiences, catering to individual preferences and needs. For instance, ML-based recommendation systems, such as those used by streaming platforms, e-commerce websites, and social media platforms, analyze user behavior in real time to offer tailored content and product recommendations.

This personalization enhances user engagement and retention, increasing customer loyalty and higher conversion rates. Personalized marketing campaigns based on ML inference yield better targeting and improved customer response rates.

Scalability & Cost-Efficiency

Organizations can deploy ML applications cost-efficiently by leveraging cloud infrastructure and hardware optimization. Cloud-based model inference with GPU support allows organizations to scale with rapid data growth and changing user demands.

Moreover, it eliminates the need for on-premises hardware maintenance, reducing capital expenditures and streamlining IT management.

Scalable Inference for Business Growth

Cloud providers also offer specialized hardware-optimized inference services at a low cost. Furthermore, on-demand serverless inference enables organizations to manage and scale workloads with low or inconsistent traffic automatically. With such flexibility, businesses can explore new opportunities and expand operations into previously untapped markets.

Real-time insights and accurate predictions empower organizations to enter new territories, informed by data-driven decisions confidently.

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AI Model Inference Servers and Frameworks

Models typically integrate into AI-enabled applications or products. They accept input and produce predictions as output. Depending on the specific use case or product design, there are three primary ways to serve models for inference:

• Online serving
• Streaming serving
• Batch serving

Let’s take a closer look at each of these options.

Online Model Serving

Online serving resembles a REST API endpoint. The endpoint provides input to the model, and immediate predictions are returned as output. It operates synchronously.

This approach can be costly as it requires handling multiple user requests in parallel. To manage this demand, inference servers usually run numerous copies of the model across the available GPU or AI GPU cloud. From an infrastructure standpoint, you need to have the option to scale in or scale out based on surges or declines in user requests.

Streaming Model Serving

Some application architectures follow asynchronous design principles. Here, services rely on a shared message bus (like Kafka or SQS) to receive signals for processing new data. In streaming deployment, the model can receive input data from the message bus and deliver predictions back to the message bus or storage servers.

This is a slightly flexible deployment model; there is no complex coupling between the systems in asynchronous systems. You can leverage different model options and have parallel consumers validate performance. The scaling principle remains the same as the other options.

Batch Model Serving

This option deploys a model as part of a batch pipeline. Input datasets are prepared beforehand, and the model generates predictions, which are stored on a storage server for later use by the product to enable specific features.

This option supports a more predictive deployment model so that you can manage the infrastructure upfront based on scale estimates. Instead of predicting for every request, the model predicts based on a batch of data in one request.

What Are Inference Servers and Why Do You Need One?

An inference server receives input data, typically in client requests. This data can include:

• Queries
• Images
• Text
• Other forms

The server processes input data through trained machine learning models to generate predictions, classifications, or other outputs.

Scalable Inference Servers for Optimal Performance

When used in a production environment, inference servers must be highly scalable to process large volumes of user requests with low latency. They should also optimize model execution for:

• Speed
• Memory usage
• Energy efficiency

Apart from performance metrics, teams also manage several operational aspects, such as model deployments and enabling customizations like adding business logic or preprocessing steps into the inference pipeline.

Native ML/DL Servers vs Specialized Inference Servers

The landscape of AI hardware is rapidly evolving. Much of the optimization in inference relies on effectively harnessing new hardware accelerators for GPU networking, storage access, and more.

As a result, many inference servers are currently available, with many new ones emerging. This discussion categorizes the existing offerings into two broad categories: native ML/DL servers and specialized inference servers. Let’s explore the value they provide.

What are Native ML/DL Servers?

We use AI frameworks or libraries like PyTorch or TensorFlow to build a machine learning (ML) or deep learning (DL) model. This category includes inference servers built by the same frameworks to serve the trained models:

• TorchServe for PyTorch
• TensorServing for Tensorflow models

Their sole purpose is to serve the models trained using the same framework. They provide the most required capabilities, such as:

• Batch inferencing
• Optimization support
• Service endpoints

Advantages of Native ML/DL Servers:

• Broad hardware support (CPUs, GPUs, TPUs)
• Can be used for both training and inference
• Seamless integration with the native ML/DL frameworks
• Large ecosystem and community support

Disadvantages of Native ML/DL Servers:

• Not optimized specifically for inference workloads
• May have higher latency and lower throughput compared to specialized servers
• Can be more resource-intensive and costly for inference workloads
• May require more manual effort for optimizations and deployment management

What are Specialized Inference Servers?

Specialized inference servers are purpose-built to provide the best optimization possible. On the hardware side, we have specific inference servers from all the major GPU vendors:

• Nvidia
• Intel
• AMD

Their inference engines or frameworks use kernel drivers, libraries, and compilers to provide the best optimization possible. They use different compilers and optimization techniques to reduce latency and memory consumption. Some of the popular inference servers from known vendors include:

• Nvidia Triton: Optimized for Nvidia GPUs, provides high-performance inference for deep learning models, supports models from various frameworks, and enables easy deployment and management of inference workloads.
• Intel OpenVino: Enables optimized inference across Intel hardware (CPUs, GPUs, VPUs), supports models from various frameworks, and provides tools for optimizing and deploying deep learning models on Intel architecture.
• AMD ROCm: Optimized for AMD GPUs and other AMD accelerators, provides optimized deep learning inference performance on AMD hardware, supports models from various frameworks, and integrates with the ROCm software stack.

LLMs have many parameters, often in the billions, translating to high computational and memory requirements during inference. Inference needs to be performed quickly, with low latency, to support real-time applications like:

• Chatbots
• Text generation
• Question answering

Deploying LLMs at scale requires high throughput and efficient resource utilization to serve numerous concurrent requests. General-purpose ML/DL inference servers may need to be optimized for LLMs' specific computational and memory access patterns, leading to suboptimal performance and efficiency.

Following are some of the inference servers that are for serving LLMs specifically:

• vLLM
• OpenLLM
• TGI - Text Generation Inference

Advantages of Specialized Inference Servers

• Explicitly optimized for efficient inference workloads
• Lower latency and higher throughput for inference
• Often more cost-effective for inference deployments
• Leverage hardware-specific optimizations:
  • Tensor Cores
  • INT8 support
• Simplified deployment and management of inference workflows
• Often provide advanced features for inference workflows:
  • Batching
  • Ensembling
  • Model analytics
• Can leverage optimized libraries and kernels for specific hardware:
  • CUDA
  • ROCm

Disadvantages of Specialized Inference Servers

• May have limited hardware support:
  • Only Nvidia GPUs
  • Intel CPUs/GPUs
• More miniature ecosystem and community compared to native ML/DL frameworks
• May require additional tooling or integration efforts for model conversion/optimization
• Limited support for training workloads (inference-only)
• May have limited support for custom models or architectures outside the main frameworks
• Potential vendor lock-in or dependency on specific hardware/software stacks

MLOps Frameworks with Model Serving

Serving a model constitutes just one facet of the overall product lifecycle. Numerous operational tasks are involved in effective tenant-based routing, including:

• Deployment planning
• Managing different model versions
• Efficient traffic routing
• Dynamic scaling based on workload
• Implementing security measures
• Setting up gateways

Aside from operational considerations, model serving involves preparatory and follow-up tasks, such as:

• Ensuring model availability in registries
• Integrating with evaluation frameworks
• Publishing model metadata
• Designing workflows for preprocessing and post-processing data inputs and outputs

Efficient Model Serving with MLOps

Your operational team may already utilize orchestrators like Kubernetes to serve microservices and prefer to handle model serving similarly. They might also have established cost optimization practices, such as:

• Utilizing spot instances
• Scaling clusters during non-peak hours

MLOps frameworks-based solutions address all the phases of model development and deployment and provide sufficient flexibility for the teams to plug in whatever they already have. They give options to streamline work during model development, such as:

• Offering services to provision notebooks for data scientists
• Integrating with storage solutions to enhance efficiency for data engineers

Furthermore, they facilitate integration with ML/DL frameworks, enabling model training on clusters.

Popular MLOps Solutions for Model Serving

Some of the more popular MLOps solutions for model serving include:

• MLFlow
• MLRun
• Kubeflow
• Seldon
• KServe
• BentoML
• RayServe

These MLOps solutions offer comprehensive end-to-end support for developing, deploying, and operating the model in production. If your team is sizable and aims to establish a robust process for effective management right from the start, opting for one of these solutions is highly recommended.

They offer customization and integration options that extend beyond the confines of supported tools, allowing you the flexibility to incorporate specific tools tailored to your needs.

Inference Pipelines: The New Normal

As the use of AI models becomes more common, production inference pipelines become more complex. For instance, specific tasks like text processing require multiple models to generate the final output.

Based on the result, I suggest starting with a classification model and passing input data to another model to process it appropriately. This requires designing activities that sometimes include processing the output from one stage to prepare it for the next stage.

Designing Efficient ML Inference Pipelines

Most of the MLOps frameworks covered above support designing the inference pipelines natively. They use the capabilities of orchestrators like Kubernetes to schedule and execute the different stages effectively. These frameworks also allow you to use different inference servers that are purpose-built for a specific work set. There are also advanced ML inference design patterns based on these concepts, like ensembles and cascaded inferences.

Ensembles

An ensemble inference pipeline is a type of inference workflow that combines the predictions or outputs of multiple machine learning models to produce a final result. During the prediction phase, they serve as a machine learning strategy that integrates various models, referred to as base estimators.

Adopting ensemble models can address the technical complexities associated with constructing a singular estimator. This approach involves sending the same input data to an ensemble of models and then combining or aggregating the predictions from all the models to derive the final, most accurate prediction.

Ensemble Inference for Credit Risk Assessment

To understand how an ensemble inference pipeline can be used in the real world, consider the following example scenario for credit risk assessment.

Banks could use an ensemble pipeline consisting of a machine learning model for predicting credit scores, a rule-based system for identifying potentially fraudulent applications, and a deep learning model for analyzing unstructured data like applicant social media activity. The outputs from these models would be aggregated to arrive at a final credit risk assessment for loan approval decisions.

Cascaded Inferences

The cascaded inference pattern involves a sequential arrangement where the output of one model serves as the input for another, forming a cascading pattern. This technique proves valuable in mitigating model biases or addressing incomplete data scenarios by leveraging an additional predictor to compensate for these shortcomings.

Enhanced Model Accuracy

Imagine a scenario where a primary model exhibits a particular bias or struggles with incomplete data, potentially leading to inaccurate predictions. In such cases, employing the cascaded inference pattern allows for integrating a secondary model. This secondary model utilizes the output of the primary model as its input, thereby refining the predictions further or compensating for the limitations of the primary model.

By cascading models in this manner, the overall predictive accuracy can be improved, leading to more reliable outcomes, especially in situations where individual models may falter due to inherent biases or data limitations.

Cascaded Inference for Fraud Detection

In the real world, a cascaded inference pipeline for a credit card fraud detection system could involve a machine learning model that analyzes transaction patterns to identify potentially fraudulent activities.

Suspicious transactions are then passed to a deeper neural network model that examines associated data like:

• Customer profiles
• Purchase details

Finally, a rule-based system applies specific policies and regulations to determine the ultimate fraud.

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Model Optimization Strategies for Inference

Deploying large AI models can be challenging due to their substantial computational, storage, and memory demands and the need to ensure low-latency response times. A key optimization strategy is model compression, which involves techniques that reduce the model’s size.

Smaller models can be loaded faster, leading to lower latency, and they require fewer resources for computation, storage, and memory. Compressing the model makes deployment more straightforward and efficient, enabling quicker inference while minimizing resource requirements.

Reducing Model Size While Maintaining Performance

Compressing a model’s size is beneficial for optimizing deployment, but the challenge lies in achieving effective size reduction while preserving good model performance. Consequently, there is often a trade-off between maintaining the model’s accuracy, adhering to computational resource constraints, and meeting latency requirements.

Here, we talk about three techniques aimed at reducing model size.

1. Quantization: Lowering Precision to Speed Up Inference

Quantization minimizes the memory necessary for loading and training a model by decreasing the precision of its weights. It involves converting model parameters from 32-bit to 16-bit, 8-bit, or 4-bit precision.

By quantizing the model weights from 32-bit full-precision to 16-bit or 8-bit precision, you can substantially reduce the memory requirement of a model with one billion parameters to only 2 GB, cutting it by 50%, or even down to just 1 GB, a 75% reduction, for loading.

Balancing Accuracy and Efficiency with Quantization

As this optimization is achieved by moving to lower, more precise data types, precision will be lost. So, it is always best to benchmark the quantization results based on your use case.

In most cases, a drop in prediction accuracy by 1-2% is a good trade-off to achieve low inference time and a reduction in infrastructure cost. Quantization is classified into two categories based on when it is applied:

• Post-training
• Pre-training

Post-Training Quantization

Post-training quantization (PTQ) is a quantization technique that involves applying the quantization process to a trained model after it has finished training. This method entails converting the model’s weights and activations from high precision, like FP32, to lower precision, such as INT8.

While PTQ is relatively simple and easy to implement, it does not consider the effects of quantization during the training phase. Most inference servers support applying PTQ to the trained models, and vendor-specific frameworks provide supported libraries.

Quantization Aware Training

Quantization-aware training (QAT) is an approach that considers the effects of quantization throughout the training process. During QAT, the model is trained using operations that simulate quantization, enabling it to adapt and perform effectively in a quantized representation.

This method enhances accuracy by ensuring the model learns to accommodate quantization nuances, resulting in superior performance compared to post-training quantization.

2. Pruning: Cutting Out the Unnecessary for Faster Inference

Pruning endeavors to remove model weights that do not substantially contribute to the model’s overall performance. Discarding these weights reduces the model’s size for inference, consequently diminishing the necessary compute resources—pruning targets model weights that are either zero or extremely close to zero.

Post-training techniques, known as one-shot pruning methods, can eliminate weights without the need for retraining. However, one-shot pruning often presents a computational challenge, particularly for large models containing billions of parameters.

SparseGPT: Efficient Pruning for Large Language Models

A post-training technique, SparseGPT, addresses the difficulties of one-shot pruning for large language models. Explicitly designed for language-based generative foundational models, SparseGPT introduces an algorithm capable of conducting sparse regression on a significant scale.

Theoretically, pruning diminishes the size of the language model (LLM), reducing computational requirements and model latency.

3. Distillation: The Smart Way to Create Smaller AI Models

Distillation is a method aimed at diminishing the size of a model, consequently cutting down on computations and enhancing model inference performance. It employs statistical techniques to train a compact student model based on a more extensive teacher model.

The outcome is a student model that preserves a significant portion of the teacher’s model accuracy while employing fewer parameters. Once trained, the student model is utilized for inference tasks. Due to its reduced size, the student model demands less hardware, reducing costs per inference request.

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