Exploring alternatives and seamlessly migrating data from Amazon Lookout for Vision
Amazon Lookout for Vision, the AWS service designed to create customized artificial intelligence and machine learning (AI/ML) computer vision models for automated quality inspection, will be discontinuing on October 31, 2025. New customers will not be able to access the service effective October 10, 2024, but existing customers will be able to use the service as normal until October 31, 2025. AWS will continue to support the service with security updates, bug fixes, and availability enhancements, but we do not plan to introduce new features for this service.
This post discusses some alternatives to Lookout for Vision and how you can export your data from Lookout for Vision to migrate to an alternate solution.
Alternatives to Lookout for Vision
If you’re interested in an alternative to Lookout for Vision, AWS has options for both buyers and builders.
For an out-of-the-box solution, the AWS Partner Network offers solutions from multiple partners. You can browse solutions on the Computer Vision for Quality Insights page in the AWS Solutions Library. These partner solutions include options for software, software as a service (SaaS) applications, managed solutions or custom implementations based on your needs. This approach provides a solution that addresses your use case without requiring you to have expertise in imaging, computer vision, AI, or application development. This typically provides the fastest time to value by taking advantage of the specialized expertise of the AWS Partners. The Solutions Library also has additional guidance to help you build solutions faster.
If you prefer to build your own solution, AWS offers AI tools and services to help you develop an AI-based computer vision inspection solution. Amazon SageMaker provides a set of tools to build, train, and deploy ML models for your use case with fully managed infrastructure, tools, and workflows. In addition to SageMaker enabling you to build your own models, Amazon SageMaker JumpStart offers built-in computer vision algorithms and pre-trained defect detection models that can be fine-tuned to your specific use case. This approach provides you the tools to accelerate your AI development while providing complete flexibility to build a solution that meets your exact requirements and integrates with your existing hardware and software infrastructure. This typically provides the lowest operating costs for a solution.
AWS also offers Amazon Bedrock, a fully managed service that offers a choice of high-performing generative AI foundation models (FMs), including models that can help build a defect detection model running in the cloud. This approach enables you to build a custom solution while using the power of generative AI to handle the custom computer vision model creation and some of the code generation to speed development, eliminating the need for full AI computer vision expertise. Amazon Bedrock provides the ability to analyze images for defects, compare performance of different models, and generate code for custom applications. This alternative is useful for use cases that don’t require low latency processing, providing faster time to value and lower development costs.
Migrating data from Lookout for Vision
To move existing data from Lookout for Vision to use in an alternative implementation, the Lookout for Vision SDK provides the capability to export a dataset from the service to an Amazon Simple Storage Service (Amazon S3) bucket. This procedure exports the training dataset, including manifest and dataset images, for a project to a destination Amazon S3 location that you specify. With the exported dataset and manifest file, you can use the same data that you used to create a Lookout for Vision model to create a model using SageMaker or Amazon Bedrock, or provide it to a partner to incorporate into their customizations for your use case.
Summary
Although Lookout for Vision is planned to shut down on October 31, 2025, AWS offers a powerful set of AI/ML services and solutions in the form of SageMaker tools to build custom models and generative AI with Amazon Bedrock to do customized inspection and generate code, in addition to a range of offerings from partners in the AWS Partner Network. Export tools enable you to effortlessly move your data from Lookout for Vision to an alternate solution if you so choose. You should explore these options to determine what works best for your specific needs.
For more details, refer to the following resources:
- Amazon Lookout for Vision Developer Guide – Detailed documentation on how to train a model or run a model in the cloud or at the edge in order to continue using the service, as well as how to export data for use in an alternate solution
- Amazon SageMaker Developer Guide – Detailed documentation on how to build a model or work with built-in algorithms available in SageMaker JumpStart.
- Amazon Bedrock User Guide – Detailed documentation on how to access FMs and customize a model for your use case, along with a defect detection example
- AWS Solutions Library – Partner offerings from specialists providing solutions using computer vision for quality insights in manufacturing
About the Author
Tim Westman is the Product Manager and Go-to-Market Lead for Edge Machine Learning, AWS. Tim leads the Product Management and Business Development for the Edge Machine Learning business at Amazon Web Services. In this role, he works with customers to help build computer vision solutions at the edge to solve complex operational challenges. Tim has more than 30 years of experience in sales, business development and product management roles for leading hardware and software companies, with the last 8 years specializing in AI and computer vision for IoT applications.
Shared by: AWS Machine Learning
October 11, 2024
Leave a Reply