Artificial Intelligence

Serverless Inference For AI Models

Team 2day

• 4 min read
Serverless Inference For AI Models
Photo by Rod Long / Unsplash

Serverless Inference for AI Models refers to the deployment and execution of AI models in a serverless computing environment, where a cloud provider manages the underlying infrastructure and the user only pays for the actual usage of the model. This allows for the efficient and cost-effective deployment of AI models without the need to manage and maintain a dedicated server, making it a popular choice for small and large-scale AI applications.

Why It's Better Than the Traditional Approach?

There are several reasons why serverless inference for AI models is considered better than traditional approaches:

  1. Cost-effective: Serverless computing eliminates the need to provision, manage, and maintain servers, reducing the cost of deploying and running AI models.
  2. Scalability: Serverless infrastructure provides automatic scalability, allowing AI models to handle unpredictable traffic spikes without needing manual intervention.
  3. Flexibility: With serverless computing, AI models can be deployed and updated quickly and easily, without the need for complex configuration or maintenance.
  4. High availability: Serverless computing offers built-in redundancy and fault tolerance, ensuring high availability for AI models even in the case of failures.
  5. Focus on innovation: By removing the burden of managing infrastructure, organizations can focus on innovation and improvement of their AI models, rather than managing servers and infrastructure.

CPU Inference For AI Models

My Goto Choice for CPU inference is AWS Lambda.

To ease this process use Serverless. It has been a lifesaver.  Here is a brief overview of how easy the process is.

  1. Set up the Serverless Framework: Install the Serverless Framework CLI, create a new project and configure AWS credentials.
  2. Prepare the AI model: The AI model should be in a format compatible with AWS Lambda and should be saved in an S3 bucket.
  3. Write the Lambda function: The Lambda function should be written in a language supported by AWS Lambda and should be designed to load the AI model and perform inference.
  4. Define the Serverless Framework configuration: This involves defining the AWS Lambda function and the S3 bucket for storing the AI model in a serverless.yml file.
  5. Deploy the AI model: Use the Serverless Framework CLI to deploy the AI model to AWS Lambda by executing the "serverless deploy" command.
  6. Test the deployed AI model: Use the Serverless Framework CLI to invoke the deployed AWS Lambda function and test the AI model's inference capabilities.

Once deployed, the AI model can be used for real-time inference by invoking the AWS Lambda function, making it accessible and scalable to a wide range of applications and users. The Serverless Framework provides an efficient and cost-effective way to deploy and manage AI models on AWS Lambda.

GPU Inference For AI Models

It's a relatively newer concept. GPU inference for AI models using tools like Pipeline.ai refers to the process of executing AI models on a GPU for improved performance and speed. Tools like Pipeline.ai provide a unified platform for managing and deploying AI models, including support for GPU inference.

With Pipeline.ai, you can easily deploy and run AI models on GPU-accelerated infrastructure, allowing you to take advantage of the performance benefits of GPUs for tasks such as image and video processing, real-time prediction, and more. The platform provides a seamless experience for deploying, managing, and scaling AI models, allowing you to focus on developing and improving your models, rather than managing infrastructure.

In addition to Pipeline.ai, there are several other similar tools available for GPU inference for AI models, including FloydHub, Gradient, and KubeFlow. These tools offer a range of features and functionalities for managing and deploying AI models, including support for GPU acceleration and seamless integration with cloud platforms like AWS and Google Cloud.

Overall, GPU inference for AI models using tools like Pipeline.ai provides a cost-effective and scalable solution for executing AI models on GPU-accelerated infrastructure, delivering improved performance and faster results.

Hosted Inference API using Huggingface

This is probably the easiest to get started. Huggingface inference API is super simple to setup and deploy AI models in production. It is a cloud-based service that allows you to deploy and run AI models created using Hugging Face's Transformers library as a web API. This allows you to make predictions using your AI models from any application or device with an internet connection, without the need to manage infrastructure or worry about scalability.

To use Hugging Face's Hosted Inference API, you'll need to first train and export your AI model using the Transformers library. Then, you can use Hugging Face's API to deploy your model and make predictions by sending API requests with your input data. The API will return the prediction results, which you can use in your application.

The Hosted Inference API provides a simple and efficient way to deploy and run AI models, allowing you to focus on developing and improving your models, rather than managing infrastructure. Additionally, the API is fully managed, providing automatic scaling, high availability, and low latency, making it a reliable and cost-effective solution for running AI models in production.

Future Of AI Serverless Inference

The future of AI serverless inference looks promising and is expected to continue to evolve and mature in the coming years. Here are some of the trends and developments that are shaping the future of AI serverless inference:

  1. Increased adoption: As more organizations seek to deploy AI models at scale, the demand for serverless inference solutions is expected to increase, leading to more widespread adoption of serverless computing for AI.
  2. Improved performance: With advancements in hardware and cloud technology, the performance of serverless inference for AI is expected to improve, allowing for faster and more efficient predictions.
  3. Advancements in edge computing: As the Internet of Things (IoT) and edge devices continue to grow, there is expected to be a growing demand for serverless inference solutions that can be deployed and run on edge devices, providing real-time predictions with low latency.
  4. Integration with other technologies: The integration of serverless inference with other technologies, such as 5G networks, blockchain, and automation, is expected to lead to new and innovative use cases for AI.
  5. Growing focus on privacy and security: As AI models are deployed and used in increasingly sensitive applications, there is a growing focus on privacy and security, leading to the development of new and innovative serverless inference solutions that ensure the secure and confidential use of AI models.

Overall, the future of AI serverless inference looks bright, with continued advancements and innovations expected in the coming years, providing organizations with new and more efficient ways to deploy and run AI models at scale.