AWS Machine Learning Blog

JUNE 19, 2024

In this post, we show you how to unlock new levels of efficiency and creativity by bringing the power of generative AI directly into your Slack workspace using Amazon Bedrock. To learn more about how to use generative AI with AWS services, see Generative AI on AWS.

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Towards AI

JANUARY 6, 2025

Professional Development Certificate in Applied AI by McGill UNIVERSITY The Professional Development Certificate in Applied AI from McGill is an appropriate advanced and practical program designed to equip professionals with actionable industry-relevant knowledge and skills required to be senior AI developers and the ranks.

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AWS Machine Learning Blog

FEBRUARY 12, 2025

Generative AI has emerged as a transformative force, captivating industries with its potential to create, innovate, and solve complex problems. Machine learning (ML) engineers must make trade-offs and prioritize the most important factors for their specific use case and business requirements.

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AWS Machine Learning Blog

JANUARY 28, 2025

By investing in robust evaluation practices, companies can maximize the benefits of LLMs while maintaining responsible AI implementation and minimizing potential drawbacks. To support robust generative AI application development, its essential to keep track of models, prompt templates, and datasets used throughout the process.

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AWS Machine Learning Blog

AUGUST 1, 2024

Amazon Bedrock also provides a broad set of capabilities needed to build generative AI applications with security, privacy, and responsible AI practices. However, deploying customized FMs to support generative AI applications in a secure and scalable manner isn’t a trivial task.

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AWS Machine Learning Blog

OCTOBER 15, 2024

Fast Bria AI offers a family of high-quality visual content models. Fast in SageMaker JumpStart and AWS Marketplace, enterprises can now use advanced generative AI capabilities to enhance their visual content creation processes. About the Authors Bar Fingerman is the Head of AI/ML Engineering at Bria.

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AWS Machine Learning Blog

DECEMBER 4, 2024

When using generative AI, achieving high performance with low latency models that are cost-efficient is often a challenge, because these goals can clash with each other. With Amazon Bedrock Model Distillation, you can now customize models for your use case using synthetic data generated by highly capable models.

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AWS Machine Learning Blog

AUGUST 4, 2023

With Einstein Studio, a gateway to AI tools on the data platform, admins and data scientists can effortlessly create models with a few clicks or using code. Einstein Studio’s bring your own model (BYOM) experience provides the capability to connect custom or generative AI models from external platforms such as SageMaker to Data Cloud.

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AWS Machine Learning Blog

DECEMBER 9, 2024

With access to a wide range of generative AI foundation models (FM) and the ability to build and train their own machine learning (ML) models in Amazon SageMaker , users want a seamless and secure way to experiment with and select the models that deliver the most value for their business. An MLflow 2.16.2

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ODSC - Open Data Science

JANUARY 10, 2024

The AI Paradigm Shift: Under the Hood of a Large Language Models Valentina Alto | Azure Specialist — Data and Artificial Intelligence | Microsoft Develop an understanding of Generative AI and Large Language Models, including the architecture behind them, their functioning, and how to leverage their unique conversational capabilities.

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AWS Machine Learning Blog

FEBRUARY 12, 2025

About the authors Daniel Zagyva is a Senior ML Engineer at AWS Professional Services. His experience extends across different areas, including natural language processing, generative AI and machine learning operations. Moran Beladev is a Senior ML Manager at Booking.com.

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AWS Machine Learning Blog

MAY 9, 2024

The rapid advancements in artificial intelligence and machine learning (AI/ML) have made these technologies a transformative force across industries. According to a McKinsey study , across the financial services industry (FSI), generative AI is projected to deliver over $400 billion (5%) of industry revenue in productivity benefits.

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AWS Machine Learning Blog

JANUARY 26, 2024

Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications. This post provides three guided steps to architect risk management strategies while developing generative AI applications using LLMs.

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Flipboard

NOVEMBER 28, 2023

In 2023, the pace of adoption of AI technologies has accelerated further with the development of powerful foundation models (FMs) and a resulting advancement in generative AI capabilities. To realize the full potential of generative AI, however, it’s important to carefully reflect on any potential risks.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

The risks associated with generative AI have been well-publicized. Research shows that not only do risks for bias and toxicity transfer from pre-trained foundation models (FM) to task-specific generative AI services, but that tuning an FM for specific tasks, on incremental datasets, introduces new and possibly greater risks.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

The Ranking team at Booking.com learned that migrating to the cloud and SageMaker has proved beneficial, and that adapting machine learning operations (MLOps) practices allows their ML engineers and scientists to focus on their craft and increase development velocity. Daniel Zagyva is a Data Scientist at AWS Professional Services.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

Use case and model governance plays a crucial role in implementing responsible AI and helps with the reliability, fairness, compliance, and risk management of ML models across use cases in the organization. Anastasia Tzeveleka is a Senior Generative AI/ML Specialist Solutions Architect at AWS.

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AWS Machine Learning Blog

SEPTEMBER 12, 2024

As generative AI moves from proofs of concept (POCs) to production, we’re seeing a massive shift in how businesses and consumers interact with data, information—and each other. While these layers provide different points of entry, the fundamental truth is that every generative AI journey starts at the foundational bottom layer.

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AWS Machine Learning Blog

DECEMBER 2, 2024

The generative AI landscape has been rapidly evolving, with large language models (LLMs) at the forefront of this transformation. As LLMs continue to expand, AI engineers face increasing challenges in deploying and scaling these models efficiently for inference. 70B model on an ml.p4d.24xlarge cu124 Model Meta Llama3.1

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AWS Machine Learning Blog

NOVEMBER 29, 2023

However, harnessing this potential while ensuring the responsible and effective use of these models hinges on the critical process of LLM evaluation. An evaluation is a task used to measure the quality and responsibility of output of an LLM or generative AI service.

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AWS Machine Learning Blog

NOVEMBER 18, 2024

Generative AI is transforming the way healthcare organizations interact with their data. MSD collaborated with AWS Generative Innovation Center (GenAIIC) to implement a powerful text-to-SQL generative AI solution that streamlines data extraction from complex healthcare databases.

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AWS Machine Learning Blog

DECEMBER 17, 2024

This presents an opportunity to augment and automate the existing content creation process using generative AI. In this post, we discuss how we used Amazon SageMaker and Amazon Bedrock to build a content generator that rewrites marketing content following specific brand and style guidelines. Connect with Hin Yee on LinkedIn.

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AWS Machine Learning Blog

OCTOBER 11, 2024

Customers across all industries are experimenting with generative AI to accelerate and improve business outcomes. They contribute to the effectiveness and feasibility of generative AI applications across various domains.

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