Transforming Applications with Customized Foundation Models on AWS
Businesses today are constantly evolving to stay ahead in the competitive landscape. One key strategy they use is leveraging foundation models (FMs) to enhance their applications. While FMs come with impressive capabilities out of the box, true competitive advantage often comes from customizing these models through pre-training or fine-tuning. However, this process can be complex and expensive, requiring advanced AI expertise, high-performance compute resources, and fast storage access.
In this post, we explore how organizations can overcome these challenges and efficiently customize FMs using AWS managed services like Amazon SageMaker training jobs and Amazon SageMaker HyperPod. These tools empower organizations to optimize their compute resources, simplify model training, and make informed decisions on customizing FMs to meet their specific business needs.
Addressing Business Challenges with Amazon SageMaker
Businesses today face a myriad of challenges when implementing and managing machine learning (ML) initiatives. These challenges range from handling vast amounts of data and models to accelerating ML solution development and managing complex infrastructures. Additionally, organizations must focus on cost optimization, data security, democratizing access to ML tools, and maintaining compliance.
Many businesses have traditionally built their ML architectures on bare metal machines using open-source solutions. While this approach offers control over infrastructure, it also requires significant effort to manage and maintain infrastructure components over time. Integrating these components, ensuring security and compliance, and optimizing for performance can be daunting tasks for many organizations.
As a result, businesses often struggle to leverage the full potential of ML while staying efficient and innovative in a competitive environment.
Empowering Customization with Amazon SageMaker
Amazon SageMaker offers a comprehensive managed service that simplifies and accelerates the entire ML lifecycle. With SageMaker, organizations can leverage a wide range of tools for building and training models at scale while offloading infrastructure management to SageMaker.
SageMaker enables organizations to scale training clusters, choose their preferred compute resources, and optimize workloads for performance using distributed training libraries. The service also offers self-healing capabilities for cluster resiliency, supports popular ML frameworks like TensorFlow and PyTorch, and allows for customization using libraries and containers.
For organizations with varying business and technical use cases, Amazon SageMaker provides two key options for distributed pre-training and fine-tuning: SageMaker training jobs and SageMaker HyperPod.
SageMaker Training Jobs
SageMaker training jobs offer a managed experience for large-scale distributed FM training, eliminating the need to manage infrastructure and ensuring smooth training operations. This pay-as-you-go option includes automatic cluster setup, orchestration, fault recovery, and seamless billing based on training time. SageMaker training jobs also support resource optimization, enabling businesses to choose the right instance types for their training needs.
With SageMaker training jobs, organizations can integrate tools like SageMaker Profiler, Amazon CloudWatch, TensorBoard, and more to enhance model development and training processes. Leading companies like AI21 Labs, Technology Innovation Institute, and Upstage have benefited from SageMaker training jobs, reducing their total cost of ownership and accelerating their FM training processes.
SageMaker HyperPod
SageMaker HyperPod offers persistent clusters with deep infrastructure control, enabling builders to fine-tune models and manage infrastructure effectively. With support for SSH access, custom orchestration, and tools like Slurm and Amazon EKS, HyperPod allows for advanced model training and performance optimization. By leveraging SageMaker distributed training libraries and integrated ML tools, organizations can enhance model performance and streamline workflow processes.
Trusted by leading companies like Articul8, IBM, and Hugging Face, SageMaker HyperPod provides a self-healing, high-performance environment for advanced ML workflows and optimizations.
Choosing the Right Option
When deciding between SageMaker HyperPod and training jobs, organizations must align their choice with their specific training needs, workflow preferences, and desired level of control over training infrastructure. HyperPod is ideal for deep technical control and customization, while training jobs offer a streamlined, managed solution for organizations focused on model development.
Conclusion
Explore more about Amazon SageMaker and distributed training on AWS by visiting the Amazon SageMaker resource page, watching the Generative AI on Amazon SageMaker Deep Dive Series, and exploring the AWS GitHub repositories for distributed training and SageMaker examples.
About the Authors
Trevor Harvey is a Principal Specialist in Generative AI at Amazon Web Services, leading go-to-market strategies for AI services.
Kanwaljit Khurmi is a Principal Generative AI/ML Solutions Architect at Amazon Web Services, specializing in containerized and ML applications.
Miron Perel is a Principal Machine Learning Business Development Manager with Amazon Web Services, advising companies on next-gen AI models.
Guillaume Mangeot is Senior WW GenAI Specialist Solutions Architect at Amazon Web Services, specializing in HPC and ML.