Amazon rolls out generative AI tool to help sellers write listings

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years.

Cem’s work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School. Specialist Solutions Architect, Containers, at AWS where he helps customers who are building modern application platforms on AWS container services. They range from things that help us be more cost-effective and streamlined in how we run operations and various businesses, to the absolute heart of every customer experience in which we offer.

According to Goldman Sachs, generative AI could drive a 7% (or almost $7 trillion) increase in global GDP and lift productivity growth by 1.5 percentage points over a 10-year period. Using Transformer architecture, generative AI models can be pre-trained on massive amounts of unlabeled data of all kinds—text, images, audio, etc. There is no manual data preparation, and because of the massive amount of pre-training (basically learning), the models can be used out-of-the-box for a wide variety of generalized tasks.

Amazon rolls out generative AI tool to help sellers write product listings

Traditionally, utilizing AI meant creation of a specialized model for each specific use-case, which required a huge amount of compute and human resources each time. FMs allow reuse by providing ability to fine-tune them to be utilized for multiple use-cases without having to build models from the ground up repeatedly. The most popularly used foundational models today utilize transformers (text generation)/diffusers (i.e., image generation) to achieve this adaptability.

First, they need a straightforward way to find and access high-performing FMs that give outstanding results and are best-suited for their purposes. Second, customers want integration into applications to be seamless, without having to manage huge clusters of infrastructure or incur large costs. Finally, customers want it to be easy to take the base FM, and build differentiated apps using their own data (a little data or a lot). Since the data customers want to use for customization is incredibly valuable IP, they need it to stay completely protected, secure, and private during that process, and they want control over how their data is shared and used. Microsoft, which invested $10 billion in OpenAI, offers access to GPT-3.5, one of the language models that powers ChatGPT, through an application program interface that lets developers make access calls to the model directly from their code. With generative AI on AWS, you can reinvent your applications, create entirely new customer experiences, and drive unprecedented levels of productivity.

Amazon taps generative AI to enhance product reviews

Yet others have acted faster, and invested more, to capture business from the generative AI boom. When OpenAI launched ChatGPT in November, Microsoft gained widespread attention for hosting the viral chatbot, and investing a reported $13 billion in OpenAI. It was quick to add the generative AI models to its own products, incorporating them into Bing in February.

Claude, Anthropic’s model on Bedrock, can perform a range of conversational and text-processing tasks. Meanwhile, Stability AI’s suite of text-to-image Bedrock-hosted models, including Stable Diffusion, can generate images, art, logos and graphic designs. The first one acts as a generator (e.g. of an image), which is then provided as an input of the second network. The latter acts as a discriminator, being able to distinguish between a real image and an artificial one. The output of such a discriminator network can be seen as an error value, representing how much the image produced by the generator network looks artificial.

This is why CodeWhisperer is free for all individual users with no qualifications or time limits for generating code! Anyone can sign up for CodeWhisperer with just an email account and become more productive within minutes. For business users, we’re offering a CodeWhisperer Professional Tier that includes administration features like single sign-on (SSO) with AWS Identity and Access Management (IAM) integration, Yakov Livshits as well as higher limits on security scanning. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

In this blog let us try to understand what Generative AI is and its applications and limitations. You can use the RayService custom resource definition (CRD) to deploy a RayCluster with a RayServe application that pulls the dogbooth model from Hugging Face that you pushed earlier via accelerate training script as an output of the fine-tune experiment. If you look under the data-on-eks/ai-ml/jark-stack/terraform/helm-values folder, you will see the values three HELM values file. In this example, pass a minimal values.yaml to the helm chart that enables the gpu-feature-discovery and node-feature-discovery features of the chart as well as a toleration that allows the node-feature-discovery pods to run on the GPU nodes we created via the blueprint. We’ll dive deeper in to advanced configuration of the NVIDIA Device Plugin/NVIDIA GPU Operator in another post. “We don’t believe that one model is going to rule the world, and we want our customers to have the state-of-the-art models from multiple providers because they are going to pick the right tool for the right job,” Sivasubramanian said.

• JupyterHub provides a shared platform for running notebooks that are popular in business, education, and research.
• In this blog let us try to understand what Generative AI is and its applications and limitations.
• Building powerful applications like CodeWhisperer is transformative for developers and all our customers.
• In 2021, the company admitted it had blocked 200 million fake reviews the year prior, for example.
• Such algorithms are part of a research area known as generative AI and have shown incredibly powerful features.

Text generation has numerous applications in the realm of natural language processing, chatbots, and content creation. Building, training, and deploying large language models (LLMs) and vision models are expensive and time consuming and require deep ML expertise. Complex models containing hundreds of billions of parameters make generative AI an immense challenge for many startup developers. AWS is collaborating with Hugging Face, an open-source provider of natural language processing (NLP) models known as transformers, to make it easier to access AWS services and deploy models for generative AI applications. Generative AI (GenAI) is a type of Artificial Intelligence that can create a wide variety of data, such as images, videos, audio, text, and 3D models. It does this by learning patterns from existing data, then using this knowledge to generate new and unique outputs.

Step-by-step guide on how to tune and deploy a generative model on Amazon EKS

If the model overfits or underfits, then please refer to an in-depth analysis of dreambooth performed by Hugging Face to help you adjust the hyper-parameters to improve model performance. Upon successful installation of bitsandbytes, next setup the requirements for running the dreambooth training script. This includes installing some additional dependencies, setting up a default configuration for accelerate , logging into Hugging Face, and downloading a sample dataset from Hugging Face. Later in the post, we describe how to create an inference service for dogbooth using the RayService custom resource definition on the cluster. In a world ruled by algorithms, SEJ brings timely, relevant information for SEOs, marketers, and entrepreneurs to optimize and grow their businesses — and careers. Our models learn to infer product information through the diverse sources of information, latent knowledge, and logical reasoning that they learn.

For our purpose, it provides a high-level API that makes it easy to experiment with different hyper-parameters and training configurations without the need to rewrite the training loop each time and efficiently use available hardware resources. As we mentioned earlier, most downstream use-cases require fine-tuning an LLM for specific tasks as per your business requirements. This typically just requires a small dataset with relatively few examples and in most cases can be performed with a single GPU. In this post, we use the example of Dreambooth to demonstrate how we can adapt a large text-to-image model such as Stable Diffusion to generate contextualized images of a subject (e.g., a dog) in different scenes. The Dreambooth paper describes an approach to bind a unique identifier with the subject (e.g., a photo of [v]dog), in order to synthesize photos of the said subject in photorealistic images based on the input prompt (e.g., a photo of [v]dog on the moon).

The easiest way to build with FMs

Artists can complement and enhance their albums with AI-generated music to create whole new genres. Media organizations can use generative AI to improve their audience experiences by offering personalized content and ads to grow revenues. Gaming companies can use generative AI to create new games and allow players to build avatars. The large models that power generative AI applications—those foundation models—are built using a neural network architecture called “Transformer.” It arrived in AI circles around 2017, and it cuts down development process significantly. Developers aren’t truly going to be more productive if code suggested by their generative AI tool contains hidden security vulnerabilities or fails to handle open source responsibly.

FMs can perform so many more tasks because they contain such a large number of parameters that make them capable of learning complex concepts. And through their pre-training exposure to internet-scale data in all its various forms and myriad of patterns, FMs learn to apply their knowledge within a wide range of contexts. The customized FMs can create Yakov Livshits a unique customer experience, embodying the company’s voice, style, and services across a wide variety of consumer industries, like banking, travel, and healthcare. Generative AI leverages AI and machine learning algorithms to enable machines to generate artificial content such as text, images, audio and video content based on its training data.

The RayServe python application is packaged in a container image that can be pulled down for the RayCluster during deployment. Ray documentation  provides a sample code to create an application for inference using Ray Serve and FastAPI . We tweak the provided python code to pass our custom dogbooth model that was pushed to Hugging Face as model_id by passing an environment variable MODEL_ID to the RayService configuration as shown in the following steps. To introspect the Dockerfile used to build a container image for the RayCluster, the head and worker nodes see src/service/Dockerfile. Accelerate is an open-source library specifically designed to simplify and optimize the process of training and fine-tuning deep learning models.