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Tag: Model Deployment

Improve LLM performance with human and AI feedback on Amazon SageMaker for Amazon Engineering | Amazon Web Services

The Amazon EU Design and Construction (Amazon D&C) team is the engineering team designing and constructing Amazon warehouses. The team navigates a large volume...

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Moderate audio and text chats using AWS AI services and LLMs | Amazon Web Services

Online gaming and social communities offer voice and text chat functionality for their users to communicate. Although voice and text chat often support friendly...

What Drives 53% of Singaporeans Away from Digital-Only Banking? – Fintech Singapore

A plethora of technological advancements and geopolitical events repeatedly upend the banking industry. 2023 was no exception, marked by highly publicised bank failures, rising...

Detect anomalies in manufacturing data using Amazon SageMaker Canvas | Amazon Web Services

With the use of cloud computing, big data and machine learning (ML) tools like Amazon Athena or Amazon SageMaker have become available and useable...

Train and host a computer vision model for tampering detection on Amazon SageMaker: Part 2 | Amazon Web Services

In the first part of this three-part series, we presented a solution that demonstrates how you can automate detecting document tampering and fraud at...

Benchmark and optimize endpoint deployment in Amazon SageMaker JumpStart  | Amazon Web Services

When deploying a large language model (LLM), machine learning (ML) practitioners typically care about two measurements for model serving performance: latency, defined by the...

Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium | Amazon Web Services

Today, we’re excited to announce the availability of Llama 2 inference and fine-tuning support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker...

Modernizing data science lifecycle management with AWS and Wipro | Amazon Web Services

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination...

Llama Guard is now available in Amazon SageMaker JumpStart | Amazon Web Services

Today we are excited to announce that the Llama Guard model is now available for customers using Amazon SageMaker JumpStart. Llama Guard provides input...

Scientists Assert: AI Now Capable of Independent Replication

Scientists have revealed that AI can now autonomously create smaller AI systems, heralding a new era in machine intelligence.  This development, revealed on Friday by...

Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator | Amazon Web Services

This post was written in collaboration with Ankur Goyal and Karthikeyan Chokappa from PwC Australia’s Cloud & Digital business. Artificial intelligence (AI) and machine...

How AWS Prototyping enabled ICL-Group to build computer vision models on Amazon SageMaker | Amazon Web Services

This is a customer post jointly authored by ICL and AWS employees. ICL is a multi-national manufacturing and mining corporation based in Israel that...

Build an end-to-end MLOps pipeline using Amazon SageMaker Pipelines, GitHub, and GitHub Actions | Amazon Web Services

Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering...

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