With the advent of high-speed 5G mobile networks, enterprises are more easily positioned than ever with the opportunity to harness the convergence of telecommunications networks and the cloud. As one of the most prominent use cases to date, machine learning (ML) at the edge has allowed enterprises to deploy ML models closer to their end-customers to reduce latency and increase responsiveness of their applications. As an example, smart venue solutions can use near-real-time computer vision for crowd analytics over 5G networks, all while minimizing investment in on-premises hardware networking equipment. Retailers can deliver more frictionless experiences on the go with natural language processing (NLP), real-time recommendation systems, and fraud detection. Even ground and aerial robotics can use ML to unlock safer, more autonomous operations.

In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models in Amazon SageMaker JumpStart. Stable Diffusion is a deep learning model that allows you to generate realistic, high-quality images and stunning art in just a few seconds. Although creating impressive images can find use in industries ranging from art to NFTs and beyond, today we also expect AI to be personalizable. Today, we announce that you can personalize the image generation model to your use case by fine-tuning it on your custom dataset in Amazon SageMaker JumpStart. This can be useful when creating art, logos, custom designs, NFTs, and so on, or fun stuff such as generating custom AI images of your pets or avatars of yourself. In this post, we provide an overview of how to fine-tune the Stable Diffusion model in two ways: programmatically through JumpStart APIs available in the SageMaker Python SDK, and JumpStart's user interface (UI) in Amazon SageMaker Studio. We also discuss how to make design choices including dataset quality, size of training dataset, choice of hyperparameter values, and applicability to multiple datasets.

AWS announced that it is now easier to share machine learning artifacts like models and notebooks with other users using SageMaker JumpStart. Amazon SageMaker JumpStart is a machine learning hub that helps users accelerate their journey into the world of machine learning. It provides access to built-in algorithms and pre-trained models from popular model hubs, as well as pre-trained foundation models for tasks such as article summarization and image generation. SageMaker JumpStart offers end-to-end solutions to solve common use cases in machine learning. One of the key features is the ability to share machine learning artifacts, such as models and notebooks, with other users within the same AWS account.

Amazon SageMaker JumpStart is the Machine Learning (ML) hub of SageMaker providing pre-trained, publicly available models for a wide range of problem types to help you get started with machine learning. JumpStart also offers example notebooks that use Amazon SageMaker features like spot instance training and experiments over a large variety of model types and use cases. These example notebooks contain code that shows how to apply ML solutions by using SageMaker and JumpStart. They can be adapted to match to your own needs and can thus speed up application development. Recently, we added 10 new notebooks to JumpStart in Amazon SageMaker Studio.

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. These features remove the heavy lifting from each step of the ML process, making it easier to develop high-quality models and reducing time to deployment. This post is the fifth in a series on using JumpStart for specific ML tasks. In the first post, we showed how you can run image classification use cases on JumpStart.

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. These features remove the heavy lifting from each step of the ML process, making it easier to develop high-quality models and reducing time to deployment. This post is the fourth in a series on using JumpStart for specific ML tasks. In the first post, we showed how to run image classification use cases on JumpStart.

Figure 4. Final image, background removed SageMaker JumpStart streamlines the deployment of the prebuilt model on SageMaker, which supports the semantic segmentation algorithm. You can test this using the sample Jupyter notebook available at Extract Image using Semantic Segmentation, which demonstrates how to extract an individual form from the surrounding background. SageMaker JumpStart is a quick way to learn about SageMaker features and capabilities through curated one-step solutions, example notebooks, and deployable pre-trained models.

According to Gartner, hyperautomation is the number one trend in 2022 and will continue advancing in future. One of the main barriers to hyperautomation is in areas where we're still struggling to reduce human involvement. Intelligent systems have a hard time matching human visual recognition abilities, despite great advancements in deep learning in computer vision. This is mainly due to the lack of annotated data (or when data is sparse) and in areas such as quality control, where trained human eyes still dominate. Another reason is the feasibility of human access in all areas of the product supply chain, such as quality control inspection on the production line.