AWS AMIs: Your Shortcut to AI/ML Efficient Deployment

Introduction

what is the common point between those two questions

• How much time does it take to prepare an AWS instance for AI/ML?
• Is anybody here know about the AWS Marketplace?

If you ever know the answer for that, keep reading there is a lot to get from this Article.

So in the second edition of Cloud_for_AI we will be talking about The AWS Marketplace especially its AMI market subset also why to use prebuild AMI templates but more importantly we will learn how to pic the right AMI for your workload?

AWS Marketplace

The AWS Marketplace is a digital catalog and procurement platform that allows customers to discover, purchase, and deploy third-party software and services directly on Amazon Web Services (AWS). It serves as a centralized hub for a wide range of solutions, including software applications, tools, and services, all optimized for AWS infrastructure and categorized as following:

• Infrastructure Software
• Professional services
• DevOps
• Business Applications
• Data Products
• Machine Learning
• Industries
• Cloud Operations
• IoT

AWS AMI

Amazon Machine Image (AMI) is a pre-configured template that contains the necessary information to launch virtual servers, known as Amazon EC2 instances, in the AWS Cloud. It serves as the foundational building block for creating compute environments tailored to specific applications or workloads.

The AWS Marketplace offers a vast array of AMIs from various vendors, making it a one-stop shop for cloud-based solutions.

So we recognize three AMI categories:

• Operating System AMIs: These are the most basic AMIs, providing a foundational operating system like Linux (Ubuntu, CentOS, RHEL) or Windows Server. These AMIs are ideal for building custom applications and infrastructure.
• Software AMIs: These AMIs come pre-installed with specific software applications or development environments, such as databases, web servers, or programming frameworks. They streamline development and deployment processes.
• Specialized AMIs: These AMIs are tailored for specific use cases, such as machine learning, Generative Artificial Intelligence, data analytics, or security. They often include pre-configured software stacks and libraries, saving time and effort.

AMI for Generative AI & Machine Learning

Those are Specialized AMIs that are tailored for machine learning and Generative Artificial Intelligence workload. Machine Learning and Generative AI workflows are resource-intensive, requiring advanced software stacks, GPUs, and efficient storage configurations. AMIs simplify the process by offering pre-configured environments optimized for:

• Training deep learning models.
• Running inference tasks with low latency.
• Fine-tuning large language models (LLMs).
• Handling massive datasets for data analytics.

Different applications have different needs and to meet those needs, vendors across the industry have crafted specialized AMIs optimized with various frameworks, architectures, and applications.

NVIDIA Deep Learning AMI

• GPU-Optimized: Tailored for GPU-powered EC2 instances like P5, P5e, and G5.
• Pre-Installed Frameworks: Includes TensorFlow, PyTorch, and MXNet, alongside NVIDIA CUDA, cuDNN, and NCCL libraries for maximizing GPU performance.
• Use Cases: Training large-scale deep learning models. Running inference for generative AI applications like image synthesis or text generation.

NVIDIA’s AMIs leverage its Hopper architecture GPUs (e.g., H100, H200), providing unmatched acceleration for transformer models foundational to LLMs like GPT and BERT.

Hugging Face AMI

• Specialized for LLMs: Optimized for training and fine-tuning transformers, such as BERT, GPT, and T5.
• Easy Integration: Pre-installed with Hugging Face Transformers library and datasets, enabling seamless integration with generative AI workflows.
• Use Cases: Fine-tuning generative AI models. Deploying APIs for real-time text or image generation.

Hugging Face AMIs emphasize ease of use, allowing developers to focus on their models without worrying about infrastructure complexities.

AWS Deep Learning AMI

• Broad Compatibility: Supports both CPU and GPU-based EC2 instances.
• Comprehensive Frameworks: Includes TensorFlow, PyTorch, Apache MXNet, Keras, and more.
• Optimized for SageMaker: Integrates seamlessly with Amazon SageMaker, AWS’s machine learning service for large-scale model training and deployment.
• Use Cases: Research and prototyping for generative AI models. Training foundational models across diverse architectures.

AWS Deep Learning AMIs provide the flexibility to build custom workflows while leveraging native AWS optimizations.

Intel Optimized AI AMI

• CPU-Centric Optimization: Optimized for Intel Xeon processors, making it ideal for cost-sensitive workloads that don’t require GPUs.
• AI Tools: Pre-installed with Intel’s oneAPI toolkit, OpenVINO, and optimizations for TensorFlow and PyTorch.
• Use Cases: Smaller-scale AI/ML models. Inference for edge computing or low-resource environments.

Intel AMIs are designed for cost-efficiency, providing high performance for ML workloads on CPU-only instances.

Red Hat AI/ML AMI

• Enterprise Focus: Combines Red Hat Enterprise Linux (RHEL) with AI/ML tools for secure, scalable deployments.
• Hybrid Workflows: Supports integration with on-premises and hybrid cloud environments.
• Use Cases: Enterprise-grade generative AI projects. AI model deployment in regulated industries.

Red Hat AMIs cater to enterprises that prioritize security and compliance while scaling generative AI workloads.

Databricks AMI

• Big Data Meets AI: Integrates Apache Spark with ML frameworks, enabling end-to-end data processing and AI workflows.
• Lakehouse Integration: Designed for the Databricks Lakehouse platform, combining data warehousing with AI/ML model training.
• Use Cases: Building generative AI models on massive datasets. Integrating ML into big data pipelines.

Databricks AMIs bridge the gap between data engineering and AI, making them ideal for data-driven generative AI applications.

Canonical AI/ML AMI

• Ubuntu Foundation: Built on Ubuntu, a popular OS for developers.
• Open-Source Friendly: Optimized for open-source AI tools and frameworks.
• Use Cases: Research-oriented generative AI. Prototyping models using cutting-edge, open-source tools.

Canonical AMIs focus on developer flexibility, supporting rapid experimentation and iteration.

The Best for your workload

When selecting an AMI for generative AI or ML, consider the following factors:

Hardware Requirements:

• For GPU-intensive tasks like training LLMs, choose NVIDIA Deep Learning AMIs.
• For cost-efficient CPU-only tasks, Intel AMIs are a better fit.

Framework Compatibility:

• Use Hugging Face AMIs for transformer-based models.
• Opt for AWS Deep Learning AMIs for general-purpose AI/ML frameworks.

Integration Needs:

• For seamless integration with big data pipelines, consider Databricks AMIs.
• For enterprise-grade security, Red Hat AMIs are the ideal choice.

Conclusion

Amazon Machine Images (AMIs) are an essential tool for AI and ML workloads, offering tailored environments that simplify deployment, improve performance, and reduce setup time. With diverse optimizations from vendors like NVIDIA, Hugging Face, Intel, and more. AWS provides a marketplace rich with options to suit any generative AI or machine learning need.

As AI continues to evolve, leveraging the right AMI can be a game-changer, accelerating innovation and unlocking new possibilities across industries.