Category: Amazon SageMaker

In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models using Amazon SageMaker JumpStart. Today, we are excited to introduce a new feature that enables users to inpaint images with Stable Diffusion models. Inpainting refers to the process of replacing a portion of an image with another image […]

You don’t have to be an expert in machine learning (ML) to appreciate the value of large language models (LLMs). Better search results, image recognition for the visually impaired, creating novel designs from text, and intelligent chatbots are just some examples of how these models are facilitating various applications and tasks. ML practitioners keep improving […]

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 […]

Data is at the heart of machine learning (ML). Including relevant data to comprehensively represent your business problem ensures that you effectively capture trends and relationships so that you can derive the insights needed to drive business decisions. With Amazon SageMaker Canvas, you can now import data from over 40 data sources to be used […]

The rise of text and semantic search engines has made ecommerce and retail businesses search easier for its consumers. Search engines powered by unified text and image can provide extra flexibility in search solutions. You can use both text and images as queries. For example, you have a folder of hundreds of family pictures in […]

Time series are sequences of data points that occur in successive order over some period of time. We often analyze these data points to make better business decisions or gain competitive advantages. An example is Shimamura Music, who used Amazon Forecast to improve shortage rates and increase business efficiency. Another great example is Arneg, who […]

In his book The Book of Why, Judea Pearl advocates for teaching cause and effect principles to machines in order to enhance their intelligence. The accomplishments of deep learning are essentially just a type of curve fitting, whereas causality could be used to uncover interactions between the systems of the world under various constraints without […]

The size and complexity of large language models (LLMs) have exploded in the last few years. LLMs have demonstrated remarkable capabilities in learning the semantics of natural language and producing human-like responses. Many recent LLMs are fine-tuned with a powerful technique called instruction tuning, which helps the model perform new tasks or generate responses to […]

Bounding box annotation is a time-consuming and tedious task that requires annotators to create annotations that tightly fit an object’s boundaries. Bounding box annotation tasks, for example, require annotators to ensure that all edges of an annotated object are enclosed in the annotation. In practice, creating annotations that are precise and well-aligned to object edges […]

This is a guest post by Neslihan Erdogan, Global Industrial IT Manager at HAYAT HOLDING. With the ongoing digitization of the manufacturing processes and Industry 4.0, there is enormous potential to use machine learning (ML) for quality prediction. Process manufacturing is a production method that uses formulas or recipes to produce goods by combining ingredients […]