AWS Machine Learning Blog

FEBRUARY 28, 2024

This generative AI task is called text-to-SQL, which generates SQL queries from natural language processing (NLP) and converts text into semantically correct SQL. With the emergence of large language models (LLMs), NLP-based SQL generation has undergone a significant transformation. on Amazon Bedrock as our LLM.

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AWS Machine Learning Blog

MARCH 14, 2024

We formulated a text-to-SQL approach where by a user’s natural language query is converted to a SQL statement using an LLM. This data is again provided to an LLM, which is asked to answer the user’s query given the data. The relevant information is then provided to the LLM for final response generation.

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AWS Machine Learning Blog

MAY 13, 2024

In part 1 of this blog series, we discussed how a large language model (LLM) available on Amazon SageMaker JumpStart can be fine-tuned for the task of radiology report impression generation. This post then seeks to assess whether prompt engineering is more performant for clinical NLP tasks compared to the RAG pattern and fine-tuning.

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Flipboard

JUNE 20, 2023

Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. This is important because training ML models and then using the trained models to make predictions (inference) can be highly energy-intensive tasks.

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AWS Machine Learning Blog

MARCH 13, 2024

The LLM analysis provides a violation result (Y or N) and explains the rationale behind the model’s decision regarding policy violation. The audio moderation workflow activates the LLM’s policy evaluation only when the toxicity analysis exceeds a set threshold. LLMs, in contrast, offer a high degree of flexibility.

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Natural Language Processing LLM Prompt Engineer Prompt Engineering 130

Towards AI

FEBRUARY 20, 2025

You will also find useful tools from the community, collaboration opportunities for diverse skill sets, and, in my industry-special Whats AI section, I will dive into the most sought-after role: LLM developers. But who exactly is an LLM developer, and how are they different from software developers and ML engineers?

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Snorkel AI

OCTOBER 27, 2023

Snorkel AI held its Enterprise LLM Virtual Summit on October 26, 2023, drawing an engaged crowd of more than 1,000 attendees across three hours and eight sessions that featured 11 speakers. How to fine-tune and customize LLMs Hoang Tran, ML Engineer at Snorkel AI, outlined how he saw LLMs creating value in enterprise environments.

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AWS Machine Learning Blog

APRIL 29, 2024

Historically, natural language processing (NLP) would be a primary research and development expense. In 2024, however, organizations are using large language models (LLMs), which require relatively little focus on NLP, shifting research and development from modeling to the infrastructure needed to support LLM workflows.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Solution overview In the following sections, we share how you can develop an example ML project with Code Editor on Amazon SageMaker Studio. We will deploy a Mistral-7B large language model (LLM) model into an Amazon SageMaker real-time endpoint using a built-in container from HuggingFace.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

Furthermore, we deep dive on the most common generative AI use case of text-to-text applications and LLM operations (LLMOps), a subset of FMOps. MLOps engineers are responsible for providing a secure environment for data scientists and ML engineers to productionize the ML use cases.

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ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Agentic AI DesignPatterns LLMs & RAG forAgents Agent Architectures &Chaining Evaluating AI Agent Performance Building with LangChain and LlamaIndex Real-World Applications of Autonomous Agents Who Should Attend: Data Scientists, Developers, AI Architects, and ML Engineers seeking to build cutting-edge autonomous systems.

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AWS Machine Learning Blog

JUNE 8, 2023

We have included a sample project to quickly deploy an Amazon Lex bot that consumes a pre-trained open-source LLM. This mechanism allows an LLM to recall previous interactions to keep the conversation’s context and pace. We also use LangChain, a popular framework that simplifies LLM-powered applications.

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AWS Machine Learning Blog

AUGUST 16, 2023

Thomson Reuters Labs, the company’s dedicated innovation team, has been integral to its pioneering work in AI and natural language processing (NLP). This technology was one of the first of its kind, using NLP for more efficient and natural legal research. A key milestone was the launch of Westlaw Is Natural (WIN) in 1992.

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Chatbots Life

MAY 16, 2023

Services : AI Solution Development, ML Engineering, Data Science Consulting, NLP, AI Model Development, AI Strategic Consulting, Computer Vision. Generative AI integration service : proposes to train Generative AI on clients data and add new features to products.

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AWS Machine Learning Blog

OCTOBER 19, 2023

Amazon Comprehend is a natural language processing (NLP) service that uses ML to uncover insights and relationships in unstructured data, with no managing infrastructure or ML experience required. Amazon SageMaker provides purpose-built tools for ML teams to automate and standardize processes across the ML lifecycle.

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The MLOps Blog

JANUARY 30, 2025

TL;DR Finding an optimal set of hyperparameters is essential for efficient and effective training of Large Language Models (LLMs). The key LLM hyperparameters influence the model size, learning rate, learning behavior, and token generation process. Hyperparameters set and tuned during pre-training influence the total size of an LLM.

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Snorkel AI

OCTOBER 12, 2023

We hope that you will enjoy watching the videos and learning more about the impact of LLMs on the world. Closing Keynote: LLMOps: Making LLM Applications Production-Grade Large language models are fluent text generators, but they struggle at generating factual, correct content.

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Heartbeat

OCTOBER 5, 2023

The emergence of Large Language Models (LLMs) like OpenAI's GPT , Meta's Llama , and Google's BERT has ushered in a new era in this field. These LLMs can generate human-like text, understand context, and perform various Natural Language Processing (NLP) tasks.

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Snorkel AI

OCTOBER 12, 2023

We hope that you will enjoy watching the videos and learning more about the impact of LLMs on the world. Closing Keynote: LLMOps: Making LLM Applications Production-Grade Large language models are fluent text generators, but they struggle at generating factual, correct content.

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AWS Machine Learning Blog

AUGUST 1, 2023

Jurassic-2 Grande Instruct is a large language model (LLM) by AI21 Labs, optimized for natural language instructions and applicable to various language tasks. By fine-tuning the model with your domain-specific data, you can optimize its performance for your particular use case, such as text summarization or any other NLP task.

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Snorkel AI

OCTOBER 12, 2023

We hope that you will enjoy watching the videos and learning more about the impact of LLMs on the world. Closing Keynote: LLMOps: Making LLM Applications Production-Grade Large language models are fluent text generators, but they struggle at generating factual, correct content.

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Data Scientist NLP ML Computer Vision 52

Snorkel AI

JUNE 9, 2023

Snorkel Foundry will allow customers to programmatically curate unstructured data to pre-train an LLM for a specific domain. Leveraging Data-centric AI for Document Intelligence and PDF Extraction Snorkel AI ML Engineer Ashwini Ramamoorthy highlighted the challenges of extracting entities from semi-structured documents.

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Large Language Models Data Scientist Machine Learning Computer Vision 52

Snorkel AI

JUNE 9, 2023

Snorkel Foundry will allow customers to programmatically curate unstructured data to pre-train an LLM for a specific domain. Leveraging Data-centric AI for Document Intelligence and PDF Extraction Snorkel AI ML Engineer Ashwini Ramamoorthy highlighted the challenges of extracting entities from semi-structured documents.

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Large Language Models Data Scientist Machine Learning Computer Vision 52

AWS Machine Learning Blog

JANUARY 26, 2024

Understanding and addressing LLM vulnerabilities, threats, and risks during the design and architecture phases helps teams focus on maximizing the economic and productivity benefits generative AI can bring. This post provides three guided steps to architect risk management strategies while developing generative AI applications using LLMs.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Available in SageMaker AI and SageMaker Unified Studio (preview) Data scientists and ML engineers can access these applications from Amazon SageMaker AI (formerly known as Amazon SageMaker) and from SageMaker Unified Studio. Deepchecks Deepchecks specializes in LLM evaluation.

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AWS Machine Learning Blog

DECEMBER 17, 2024

Amazon SageMaker helps data scientists and machine learning (ML) engineers build FMs from scratch, evaluate and customize FMs with advanced techniques, and deploy FMs with fine-grain controls for generative AI use cases that have stringent requirements on accuracy, latency, and cost. Of the six challenges, the LLM met only one.

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ODSC - Open Data Science

MARCH 11, 2025

The second script shows how to query those embeddings with an LLM for RAG-based Q&A. This quick workflow lets you maintain a powerful, scalable knowledge base for any LLM-powered application. He brings deep expertise in building and training models for applications like NLP, data visualization, and real-time analytics.

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AWS Machine Learning Blog

OCTOBER 11, 2024

This post dives deep into Amazon Bedrock Knowledge Bases , which helps with the storage and retrieval of data in vector databases for RAG-based workflows, with the objective to improve large language model (LLM) responses for inference involving an organization’s datasets. The LLM response is passed back to the agent.

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AWS Machine Learning Blog

NOVEMBER 18, 2024

To give the LLM access to these codes without overwhelming the main prompt, we created lookup tools that the LLM can use to look up for sex, race, and state codes. Because data analysts need to filter on complex combinations of factors, this list can get too long to be reliably rewritten by the LLM in the SQL query.

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