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AWS Machine Learning Blog

Optimizing enterprise AI assistants: How Crypto.com uses LLM reasoning and feedback for enhanced efficiency

by Jessie Jiao, Yanwei Cui, Michelle Hong, and Gary Lo on 28 JUL 2025 in Amazon Bedrock, Amazon Machine Learning, Artificial Intelligence, Customer Solutions Permalink Comments Share

Modern AI assistants—artificial intelligence systems designed to interact with users through natural language, answer questions, and even perform tasks—face increasingly complex challenges in production environments. Beyond handling basic FAQs, they must now execute meaningful actions, adhere to company policies, implement content filtering, escalate to human operators when needed, and manage follow-up tasks. These requirements demand sophisticated systems capable of handling diverse scenarios while maintaining consistency and compliance. In this post, we explore how we used user and system feedback to continuously improve and optimize our instruction prompts. This feedback-driven approach has enabled us to create more effective prompts that adapt to various subsystems while maintaining high performance across different use cases.

How Apoidea Group enhances visual information extraction from banking documents with multimodal models using LLaMA-Factory on Amazon SageMaker HyperPod

by Tony Wong, Ken Tsui, Edward Tsoi Po Wa, Mickey Yip, Yanwei Cui, and Zhihao Lin on 15 MAY 2025 in Amazon SageMaker, Amazon SageMaker HyperPod, Artificial Intelligence, Customer Solutions, Generative AI Permalink Comments Share

The banking industry has long struggled with the inefficiencies associated with repetitive processes such as information extraction, document review, and auditing. These tasks, which require significant human resources, slow down critical operations such as Know Your Customer (KYC) procedures, loan applications, and credit analysis. As a result, banks face operational challenges, including limited scalability, slow processing speeds, and high costs associated with staff training and turnover. In this post, we present our work and step-by-step code on fine-tuning the Qwen2-VL-7B-Instruct model using LLaMA-Factory on SageMaker HyperPod. Our results demonstrate significant improvements in table structure recognition accuracy and efficiency compared to the original base model and traditional methods, with particular success in handling complex financial tables and multi-page documents.

Scalable training platform with Amazon SageMaker HyperPod for innovation: a video generation case study

In this post, we share an ML infrastructure architecture that uses SageMaker HyperPod to support research team innovation in video generation. We will discuss the advantages and pain points addressed by SageMaker HyperPod, provide a step-by-step setup guide, and demonstrate how to run a video generation algorithm on the cluster.

Build an automated insight extraction framework for customer feedback analysis with Amazon Bedrock and Amazon QuickSight

In this post, we explore how to integrate LLMs into enterprise applications to harness their generative capabilities. We delve into the technical aspects of workflow implementation and provide code samples that you can quickly deploy or modify to suit your specific requirements. Whether you’re a developer seeking to incorporate LLMs into your existing systems or a business owner looking to take advantage of the power of NLP, this post can serve as a quick jumpstart.

Personalize your generative AI applications with Amazon SageMaker Feature Store

In this post, we elucidate the simple yet powerful idea of combining user profiles and item attributes to generate personalized content recommendations using LLMs. As demonstrated throughout the post, these models hold immense potential in generating high-quality, context-aware input text, which leads to enhanced recommendations. To illustrate this, we guide you through the process of integrating a feature store (representing user profiles) with an LLM to generate these personalized recommendations.

Build an image-to-text generative AI application using multimodality models on Amazon SageMaker

In this post, we provide an overview of popular multimodality models. We also demonstrate how to deploy these pre-trained models on Amazon SageMaker. Furthermore, we discuss the diverse applications of these models, focusing particularly on several real-world scenarios, such as zero-shot tag and attribution generation for ecommerce and automatic prompt generation from images.

Build a generative AI-based content moderation solution on Amazon SageMaker JumpStart

In this post, we introduce a novel method to perform content moderation on image data with multi-modal pre-training and a large language model (LLM). With multi-modal pre-training, we can directly query the image content based on a set of questions of interest and the model will be able to answer these questions. This enables users to chat with the image to confirm if it contains any inappropriate content that violates the organization’s policies. We use the powerful generating capability of LLMs to generate the final decision including safe/unsafe labels and category type. In addition, by designing a prompt, we can make an LLM generate the defined output format, such as JSON format. The designed prompt template allows the LLM to determine if the image violates the moderation policy, identify the category of violation, explain why, and provide the output in a structured JSON format.

Implement a multi-object tracking solution on a custom dataset with Amazon SageMaker

The demand for multi-object tracking (MOT) in video analysis has increased significantly in many industries, such as live sports, manufacturing, and traffic monitoring. For example, in live sports, MOT can track soccer players in real time to analyze physical performance such as real-time speed and moving distance. Since its introduction in 2021, ByteTrack remains to […]

Implementing Amazon Forecast in the retail industry: A journey from POC to production

Amazon Forecast is a fully managed service that uses statistical and machine learning (ML) algorithms to deliver highly accurate time-series forecasts. Recently, based on Amazon Forecast, we helped one of our retail customers achieve accurate demand forecasting, within 8 weeks. The solution improved the manual forecast by an average of 10% in regards to the […]

Onboard PaddleOCR with Amazon SageMaker Projects for MLOps to perform optical character recognition on identity documents

Optical character recognition (OCR) is the task of converting printed or handwritten text into machine-encoded text. OCR has been widely used in various scenarios, such as document electronization and identity authentication. Because OCR can greatly reduce the manual effort to register key information and serve as an entry step for understanding large volumes of documents, […]

Graph-based recommendation system with Neptune ML: An illustration on social network link prediction challenges

Recommendation systems are one of the most widely adopted machine learning (ML) technologies in real-world applications, ranging from social networks to ecommerce platforms. Users of many online systems rely on recommendation systems to make new friendships, discover new music according to suggested music lists, or even make ecommerce purchase decisions based on the recommended products. […]

Run your TensorFlow job on Amazon SageMaker with a PyCharm IDE

As more machine learning (ML) workloads go into production, many organizations must bring ML workloads to market quickly and increase productivity in the ML model development lifecycle. However, the ML model development lifecycle is significantly different from an application development lifecycle. This is due in part to the amount of experimentation required before finalizing a […]

Onboarding Amazon SageMaker Studio with AWS SSO and Okta Universal Directory

This blog was reviewed and updated June, 2022 to address latest changes to steps and User Interface on Studio and Okta. In 2019, AWS announced Amazon SageMaker Studio, a unified integrated development environment (IDE) for machine learning (ML) development. You can write code, track experiments, visualize data, and perform debugging and monitoring within a single, […]

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