AWS Machine Learning Blog

SEPTEMBER 1, 2023

Nowadays, the majority of our customers is excited about large language models (LLMs) and thinking how generative AI could transform their business. In this post, we discuss how to operationalize generative AI applications using MLOps principles leading to foundation model operations (FMOps).

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AWS Machine Learning Blog

OCTOBER 2, 2024

Large enterprises are building strategies to harness the power of generative AI across their organizations. Managing bias, intellectual property, prompt safety, and data integrity are critical considerations when deploying generative AI solutions at scale.

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AWS Machine Learning Blog

MARCH 18, 2025

This transcription then serves as the input for a powerful LLM, which draws upon its vast knowledge base to provide personalized, context-aware responses tailored to your specific situation. LLM integration The preprocessed text is fed into a powerful LLM tailored for the healthcare and life sciences (HCLS) domain.

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AWS Machine Learning Blog

AUGUST 22, 2024

Building a deployment pipeline for generative artificial intelligence (AI) applications at scale is a formidable challenge because of the complexities and unique requirements of these systems. Generative AI models are constantly evolving, with new versions and updates released frequently. We use Python to do this.

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AWS Machine Learning Blog

FEBRUARY 11, 2025

Clean up To clean up the model and endpoint, use the following code: predictor.delete_model() predictor.delete_endpoint() Conclusion In this post, we explored how SageMaker JumpStart empowers data scientists and ML engineers to discover, access, and run a wide range of pre-trained FMs for inference, including the Falcon 3 family of models.

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ODSC - Open Data Science

MARCH 10, 2025

Perfect for developers and data scientists looking to push the boundaries of AI-powered assistants. With real-world examples from regulated industries, this session equips data scientists, ML engineers, and risk professionals with the skills to build more transparent and accountable AIsystems.

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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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Marktechpost

MAY 30, 2024

Introduction to AI and Machine Learning on Google Cloud This course introduces Google Cloud’s AI and ML offerings for predictive and generative projects, covering technologies, products, and tools across the data-to-AI lifecycle. It also introduces Google’s 7 AI principles.

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Chatbots Life

MAY 16, 2023

Top 5 Generative AI Integration Companies to Drive Customer Support in 2023 If you’ve been following the buzz around ChatGPT, OpenAI, and generative AI, it’s likely that you’re interested in finding the best Generative AI integration provider for your business.

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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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TheSequence

MAY 21, 2023

. 📝 Editorial: The Undisputed Champion of Open Source Generative AI Stability AI is synonymous with open-source generative AI. The release of Stable Diffusion was a sort of Sputnik moment in the evolution of open-source generative AI models.

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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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Marktechpost

JULY 25, 2024

Introduction to Large Language Models Difficulty Level: Beginner This course covers large language models (LLMs), their use cases, and how to enhance their performance with prompt tuning. This short course also includes guidance on using Google tools to develop your own Generative AI apps.

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AWS Machine Learning Blog

FEBRUARY 28, 2024

Today, generative AI can enable people without SQL knowledge. 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. on Amazon Bedrock as our LLM. Here, the generated SQL is sent for syntax errors.

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AWS Machine Learning Blog

FEBRUARY 26, 2024

Our proposed architecture provides a scalable and customizable solution for online LLM monitoring, enabling teams to tailor your monitoring solution to your specific use cases and requirements. We suggest that each module take incoming inference requests to the LLM, passing prompt and completion (response) pairs to metric compute modules.

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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. The conversation included notes about how much time data scientists spend preparing data, and the responsibility that comes with using AI.

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AWS Machine Learning Blog

OCTOBER 4, 2023

Healthcare and life sciences (HCLS) customers are adopting generative AI as a tool to get more from their data. In this post, we walk you through deploying a Falcon large language model (LLM) using Amazon SageMaker JumpStart and using the model to summarize long documents with LangChain and Python.

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ODSC - Open Data Science

FEBRUARY 13, 2025

The AI agent classified and summarized GenAI-related content from Reddit, using a structured pipeline with utility functions for API interactions, web scraping, and LLM-based reasoning. The workshop underscored the value of knowledge graphs in improving AI explainability and retrieval precision.

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AWS Machine Learning Blog

MARCH 13, 2024

By orchestrating toxicity classification with large language models (LLMs) using generative AI, we offer a solution that balances simplicity, latency, cost, and flexibility to satisfy various requirements. The audio moderation workflow activates the LLM’s policy evaluation only when the toxicity analysis exceeds a set threshold.

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AWS Machine Learning Blog

MAY 30, 2024

Instead, Vitech opted for Retrieval Augmented Generation (RAG), in which the LLM can use vector embeddings to perform a semantic search and provide a more relevant answer to users when interacting with the chatbot. Prompt engineering Prompt engineering is crucial for the knowledge retrieval system.

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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

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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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. The conversation included notes about how much time data scientists spend preparing data, and the responsibility that comes with using AI.

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LLM Data Scientist Machine Learning Large Language Models 59

AWS Machine Learning Blog

OCTOBER 22, 2024

You can now create an end-to-end workflow to train, fine tune, evaluate, register, and deploy generative AI models with the visual designer for Amazon SageMaker Pipelines. Create a complete AI/ML pipeline for fine-tuning an LLM using drag-and-drop functionality. But fine-tuning an LLM just once isn’t enough.

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TheSequence

JANUARY 19, 2024

Generative AI and Large Language Models (LLMs) are new to most companies. If you are an engineering leader building Gen AI applications, it can be hard to know what skills and types of people are needed. At the same time, the capabilities of AI models have grown.

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Towards AI

JUNE 18, 2024

About Building LLMs for Production Generative AI and LLMs are transforming industries with their ability to understand and generate human-like text and images. However, building reliable and scalable LLM applications requires a lot of extra work and a deep understanding of various techniques and frameworks.

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TheSequence

JULY 21, 2024

HuggingFace open-sourced a series of small, high-performance LLMs. Even by the crazy standards of the generative AI market, last week was a remarkable week in terms of model releases. Proven-Verifier Games in LLMs OpenAI published a paper unveiling a prover-verifier game to improve the legibility of LLM outputs.

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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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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. The conversation included notes about how much time data scientists spend preparing data, and the responsibility that comes with using AI.

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LLM Data Scientist Machine Learning Large Language Models 52

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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AWS Machine Learning Blog

NOVEMBER 30, 2023

This significantly reduces the time you spend setting up your environment and decreases the complexity of managing package dependencies in your ML project. Amazon CodeWhisperer integration – Code Editor also comes with generative AI capabilities powered by Amazon CodeWhisperer. You can find the sample code in this GitHub repo.

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AWS Machine Learning Blog

AUGUST 16, 2023

The introduction of generative AI provides another opportunity for Thomson Reuters to work with customers and once again advance how they do their work, helping professionals draw insights and automate workflows, enabling them to focus their time where it matters most.

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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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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. This track dives into the design, development, and deployment of intelligent agents that leverage LLMs and machine learning.

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Data Scientist Machine Learning Large Language Models ML Engineer 52

AWS Machine Learning Blog

APRIL 29, 2024

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. This often means the method of using a third-party LLM API won’t do for security, control, and scale reasons.

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Towards AI

SEPTEMBER 22, 2023

However, with the advent of LLM, everything has changed. LLMs seem to rule them all, and interestingly, no one knows how LLMs work. Now, people are questioning whether they should still develop solutions other than LLM but know little about how to make LLM-based solutions accountable. Everyone was happy.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Configuration files (YAML and JSON) allow ML practitioners to specify undifferentiated code for orchestrating training pipelines using declarative syntax. The following are the key benefits of this solution: Automation – The entire ML workflow, from data preprocessing to model registry, is orchestrated with no manual intervention.

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