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HCLTech’s AWS powered AutoWise Companion: A seamless experience for informed automotive buyer decisions with data-driven design

AWS Machine Learning Blog

Requested information is intelligently fetched from multiple sources such as company product metadata, sales transactions, OEM reports, and more to generate meaningful responses. Vector embedding and data cataloging To support natural language query similarity matching, the respective data is vectorized and stored as vector embeddings.

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Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

AWS Machine Learning Blog

Therefore, when the Principal team started tackling this project, they knew that ensuring the highest standard of data security such as regulatory compliance, data privacy, and data quality would be a non-negotiable, key requirement. He has 20 years of enterprise software development experience.

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MLOps deployment best practices for real-time inference model serving endpoints with Amazon SageMaker

AWS Machine Learning Blog

With this option, you are testing the new model and minimizing the risks of a low-performing model, and you can compare both models’ performance with the same data. SageMaker deployment guardrails Guardrails are an essential part of software development.

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Level Up Your AI Game with More ODSC West Announced Sessions

ODSC - Open Data Science

In particular, you’ll focus on tabular (or structured) synthetic data and the privacy-preserving benefits of working with synthetic data. You’ll even get hands-on with the open-source tool (DataLLM) and create tabular synthetic data yourselves. Gen AI in Software Development. What should you be looking for?

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How to Build an Experiment Tracking Tool [Learnings From Engineers Behind Neptune]

The MLOps Blog

Building a tool for managing experiments can help your data scientists; 1 Keep track of experiments across different projects, 2 Save experiment-related metadata, 3 Reproduce and compare results over time, 4 Share results with teammates, 5 Or push experiment outputs to downstream systems.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning Blog

The AWS managed offering ( SageMaker Ground Truth Plus ) designs and customizes an end-to-end workflow and provides a skilled AWS managed team that is trained on specific tasks and meets your data quality, security, and compliance requirements. The following example describes usage and cost per model per tenant in Athena.

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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning Blog

Data Management – Efficient data management is crucial for AI/ML platforms. Regulations in the healthcare industry call for especially rigorous data governance. It should include features like data versioning, data lineage, data governance, and data quality assurance to ensure accurate and reliable results.