Optimizing AI Inference with AWS Inferentia and Amazon SageMaker

Overview

Artificial Intelligence (AI) is revolutionizing industries, from customer service chatbots to real-time fraud detection tools. As companies seek to use AI models cost-effectively without over-expenditure, AWS Inferentia and Amazon SageMaker provide tools to scale AI at affordable costs.

AWS Inferentia is a custom-built AI inference chip designed to run machine learning models efficiently and at a lower cost than traditional GPUs. When combined with Amazon SageMaker, AWS’s fully managed machine learning platform enables businesses to deploy, scale, and manage AI inference workloads seamlessly.

In this blog, we will explore how AWS Inferentia works within Amazon SageMaker, why it is a smart choice for large-scale AI inference, and how you can start with it.

Why AWS Inferentia for AI Inference?

Before diving into how AWS Inferentia is integrated into Amazon SageMaker, let us first see why it is a significant change in AI inference. After a machine learning model is trained, it must be produced in the real world, whether answering user questions, processing images, or suggesting recommendations. This function, called inference, consists of taking new data and running it through an already-trained model to produce predictions. Inference can be costly, though, particularly when performed on GPUs. While GPUs work great at training deep learning models, they are not necessarily the most efficient or cost-effective to run them in production. AWS Inferentia might be specifically designed for inference and provides:

• Lower costs compared to GPUs.
• Higher efficiency by optimizing how models process new data.
• Scalability to manage large workloads without excessive infrastructure.

Let us see how we can leverage this power inside Amazon SageMaker.

Amazon SageMaker

AWS’s complete machine learning solution, Amazon SageMaker, lets developers and organizations train, deploy, and iterate on ML models without any infrastructural worries. Most of the time-consuming jobs are done for them by Amazon SageMaker, relieving them from manual server configurations, environment preparations, and deployment management.

Amazon SageMaker provides real-time endpoints that can manage incoming prediction requests for AI inference at scale. Moreover, combining Amazon SageMaker with AWS Inferentia provides a high-performance, cost-effective means of running AI models in production.

Integrating AWS Inferentia with Amazon SageMaker

To use AWS Inferentia for AI inference in Amazon SageMaker, we follow a few key steps:

1. Choose an AWS Inferentia-Powered Instance in Amazon SageMaker

Amazon SageMaker offers specialized ‘Inf1’ instances, which are backed by AWS Inferentia chips. These instances are optimized for AI inference workloads and provide a more cost-effective solution than traditional GPU-based instances.

When setting up an Amazon SageMaker endpoint, you can choose an Inf1 instance (like ml.inf1.xlarge or ml.inf1.6xlarge) to maximize AWS Inferentia’s efficiency.

2. Optimize the Model for AWS Inferentia Using AWS Neuron SDK

AWS Inferentia doesn’t work like a regular CPU or GPU. To run models efficiently on AWS Inferentia, they must be optimized using the AWS Neuron SDK.

The AWS Neuron SDK provides tools to convert deep learning models from frameworks like TensorFlow, PyTorch, and MXNet into a format that can be executed on AWS Inferentia chips.

For example, if you have a trained PyTorch model, you would:

• Convert it using Neuron SDK’s compiler (torch-neuron).
• Deploy it on an Inferentia-powered SageMaker instance.

3. Deploy the Model as an Amazon SageMaker Endpoint

After optimizing the model, you can host it as an Amazon SageMaker endpoint, where applications can send requests and get predictions in real-time.

Amazon SageMaker endpoint on Inferentia can easily process millions of inference requests, and it is best suited for use cases such as chatbots, image classification, fraud detection, and recommendation systems.

Scaling AI Inference with AWS Inferentia and Amazon SageMaker

One of the greatest benefits of employing AWS Inferentia with Amazon SageMaker is scalability. SageMaker enables you to:

• Auto-scale inference endpoints according to incoming traffic.
• Distribute inference across multiple Inferentia-powered instances for large-scale workloads.
• Lower latency with fast Inferentia chips, allowing quick predictions.

For instance, an online retailer with AI-driven recommendations can process millions of customer interactions daily by distributing its model across multiple Inferentia-backed SageMaker instances, scaling up or down on demand.

Real-World Use Cases

AWS Inferentia with Amazon SageMaker is currently utilized across industries to enable AI inference at scale. Some of the notable use cases are:

1. Natural Language Processing (NLP) Models

Heavy NLP models such as BERT and GPT are compute-intensive, but with AWS Inferentia, organizations can afford to run chatbots and virtual assistants.

2. Computer Vision Applications

From medical imaging to security camera monitoring, businesses can use image recognition models running thousands of images per second at a fraction of the cost with high-end GPUs.

3. Fraud Detection and Risk Analysis

Banks deploy AI-based fraud detection to screen transactions in real-time. With Inferentia, they can upscale their models without going broke.

Conclusion

If you want to scale AI inference workloads without overspending, AWS Inferentia is the way to go. Try deploying a model on Inf1 instances in Amazon SageMaker and see the difference.

Drop a query if you have any questions regarding AWS Inferentia or Amazon SageMaker and we will get back to you quickly.

About CloudThat

CloudThat is a leading provider of Cloud Training and Consulting services with a global presence in India, the USA, Asia, Europe, and Africa. Specializing in AWS, Microsoft Azure, GCP, VMware, Databricks, and more, the company serves mid-market and enterprise clients, offering comprehensive expertise in Cloud Migration, Data Platforms, DevOps, IoT, AI/ML, and more.

CloudThat is the first Indian Company to win the prestigious Microsoft Partner 2024 Award and is recognized as a top-tier partner with AWS and Microsoft, including the prestigious ‘Think Big’ partner award from AWS and the Microsoft Superstars FY 2023 award in Asia & India. Having trained 650k+ professionals in 500+ cloud certifications and completed 300+ consulting projects globally, CloudThat is an official AWS Advanced Consulting Partner, Microsoft Gold Partner, AWS Training Partner, AWS Migration Partner, AWS Data and Analytics Partner, AWS DevOps Competency Partner, AWS GenAI Competency Partner, Amazon QuickSight Service Delivery Partner, Amazon EKS Service Delivery Partner,  AWS Microsoft Workload Partners, Amazon EC2 Service Delivery Partner, Amazon ECS Service Delivery Partner, AWS Glue Service Delivery Partner, Amazon Redshift Service Delivery Partner, AWS Control Tower Service Delivery Partner, AWS WAF Service Delivery Partner, Amazon CloudFront, Amazon OpenSearch, AWS DMS and many more.