AWS Machine Learning Blog

FEBRUARY 28, 2024

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. Today, generative AI can enable people without SQL knowledge. Therefore, collecting comprehensive and high-quality metadata also remains a challenge. We use Anthropic Claude v2.1 on Amazon Bedrock as our LLM.

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Metadata LLM Generative AI NLP 125

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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LLM Large Language Models ML Algorithm 93

AWS Machine Learning Blog

FEBRUARY 19, 2025

At AWS re:Invent 2024, we launched a new innovation in Amazon SageMaker HyperPod on Amazon Elastic Kubernetes Service (Amazon EKS) that enables you to run generative AI development tasks on shared accelerated compute resources efficiently and reduce costs by up to 40%. queue-name: hyperpod-ns-researchers-localqueue kueue.x-k8s.io/priority-class:

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Data Scientist Data Science ML Engineer Generative AI 70

Marktechpost

JULY 25, 2024

This short course also includes guidance on using Google tools to develop your own Generative AI apps. Prompt Engineering with LLaMA-2 Difficulty Level: Beginner This course covers the prompt engineering techniques that enhance the capabilities of large language models (LLMs) like LLaMA-2.

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Large Language Models Prompt Engineering Prompt Engineer Chatbots 111

AWS Machine Learning Blog

DECEMBER 19, 2023

Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. However, putting an ML model into production at scale is challenging and requires a set of best practices.

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Machine Learning ML Engineer DevOps ML 121

AWS Machine Learning Blog

NOVEMBER 14, 2024

However, model governance functions in an organization are centralized and to perform those functions, teams need access to metadata about model lifecycle activities across those accounts for validation, approval, auditing, and monitoring to manage risk and compliance. An experiment collects multiple runs with the same objective.

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ML Machine Learning Auto-complete Auto-classification 87

AWS Machine Learning Blog

JUNE 8, 2023

However, businesses can meet this challenge while providing personalized and efficient customer service with the advancements in generative artificial intelligence (generative AI) powered by large language models (LLMs). Generative AI chatbots have gained notoriety for their ability to imitate human intellect.

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Generative AI LLM Machine Learning Large Language Models 83

AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

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ML Data Scientist ML Engineer Data Science 119

ODSC - Open Data Science

MARCH 27, 2023

In this session, you will explore the flow of Imperva’s botnet detection, including data extraction, feature selection, clustering, validation, and fine-tuning, as well as the organization’s method for measuring the results of unsupervised learning problems using a query engine. Should you have manual sign-offs?

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Machine Learning Data Science Deep Learning Data Ingestion 98

AWS Machine Learning Blog

JUNE 13, 2023

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. Forethought is a leading generative AI suite for customer service.

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Generative AI Auto-complete AI Modeling Machine Learning 73

AWS Machine Learning Blog

JULY 24, 2024

Fine-tuning an LLM can be a complex workflow for data scientists and machine learning (ML) engineers to operationalize. By logging your datasets with MLflow, you can store metadata, such as dataset descriptions, version numbers, and data statistics, alongside your MLflow runs.

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LLM ML Generative AI Machine Learning 121

AWS Machine Learning Blog

MAY 30, 2024

Additionally, VitechIQ includes metadata from the vector database (for example, document URLs) in the model’s output, providing users with source attribution and enhancing trust in the generated answers. Prompt engineering Prompt engineering is crucial for the knowledge retrieval system. “We

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Chatbots Prompt Engineer Prompt Engineering Large Language Models 120

AWS Machine Learning Blog

MARCH 18, 2025

You will notice the content of this file as JSON with a text transcript available under the key transcripts, along with other metadata. Rushabh Lokhande is a Senior Data & ML Engineer with AWS Professional Services Analytics Practice. You can download a sample file and review the contents.

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LLM NLP Data Integration AI 101

AWS Machine Learning Blog

JANUARY 10, 2024

Specialist Data Engineering at Merck, and Prabakaran Mathaiyan, Sr. ML Engineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. This post is co-written with Jayadeep Pabbisetty, Sr.

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ML Machine Learning Data Scientist ETL 116

AWS Machine Learning Blog

APRIL 7, 2025

Repository Information**: Not shown in the provided excerpt, but likely contains metadata about the repository. Search for the embedding and text generation endpoints. Specialist Solutions Architect focused on generative AI strategy, applied AI solutions, and conducting research to help customers hyper-scale on AWS.

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Machine Learning Large Language Models Python Automation 62

ODSC - Open Data Science

FEBRUARY 25, 2025

At ODSC East 2025 , were excited to present 12 curated tracks designed to equip data professionals, machine learning engineers, and AI practitioners with the tools they need to thrive in this dynamic landscape. Whats Next in AI TrackExplore the Cutting-Edge Stay ahead of the curve with insights into the future of AI.

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Data Scientist Machine Learning Large Language Models ML Engineer 52

AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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Data Scientist ML Data Science Machine Learning 105

AWS Machine Learning Blog

JUNE 25, 2024

His area of focus is generative AI and AWS AI Accelerators. Niithiyn works closely with the Generative AI GTM team to enable AWS customers on multiple fronts and accelerate their adoption of generative AI. Rohit Talluri is a Generative AI GTM Specialist (Tech BD) at Amazon Web Services (AWS).

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ML Metadata Software Development Generative AI 84

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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Large Language Models Algorithm LLM Responsible AI 103

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Similarly, a study by Meta AI and Carnegie Melon university found that, in the worst cases, 43 percent of compute time was wasted because of overheads due to hardware failures. This can adversely impact a customer’s ability to keep up with the pace of innovation in generative AI and can also increase the time-to-market for their models.

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Auto-complete ML Generative AI Deep Learning 103

The MLOps Blog

OCTOBER 3, 2023

I see so many of these job seekers, especially on the MLOps side or the ML engineer side. There’s no component that stores metadata about this feature store? Mikiko Bazeley: In the case of the literal feature store, all it does is store features and metadata. Aurimas: Was it content generation? It’s two things.

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ML Data Scientist Machine Learning Data Science 52

AWS Machine Learning Blog

NOVEMBER 29, 2023

An evaluation is a task used to measure the quality and responsibility of output of an LLM or generative AI service. Based on this tenet, we can classify generative AI users who need LLM evaluation capabilities into 3 groups as shown in the following figure: model providers, fine-tuners, and consumers.

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LLM Data Scientist Algorithm Generative AI 99

Flipboard

NOVEMBER 27, 2024

The application needs to search through the catalog and show the metadata information related to all of the data assets that are relevant to the search context. This allows FMs to retain their inductive abilities while grounding their language understanding and generation in well-structured domain knowledge and logical reasoning.

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Metadata Auto-complete Data Discovery ML Engineer 149

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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Metadata Generative AI LLM Data Ingestion 98

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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Metadata Generative AI Categorization Data Scientist 109