Generative Data Intelligence

Tag: Amazon SageMaker

Use Amazon DocumentDB to build no-code machine learning solutions in Amazon SageMaker Canvas | Amazon Web Services

We are excited to announce the launch of Amazon DocumentDB (with MongoDB compatibility) integration with Amazon SageMaker Canvas, allowing Amazon DocumentDB customers to build...

Boost productivity on Amazon SageMaker Studio: Introducing JupyterLab Spaces and generative AI tools | Amazon Web Services

Amazon SageMaker Studio offers a broad set of fully managed integrated development environments (IDEs) for machine learning (ML) development, including JupyterLab, Code Editor based...

How AWS Prototyping enabled ICL-Group to build computer vision models on Amazon SageMaker | Amazon Web Services

This is a customer post jointly authored by ICL and AWS employees. ICL is a multi-national manufacturing and mining corporation based in Israel that...

Automate PDF pre-labeling for Amazon Comprehend | Amazon Web Services

Amazon Comprehend is a natural-language processing (NLP) service that provides pre-trained and custom APIs to derive insights from textual data. Amazon Comprehend customers can...

Improve your Stable Diffusion prompts with Retrieval Augmented Generation | Amazon Web Services

Text-to-image generation is a rapidly growing field of artificial intelligence with applications in a variety of areas, such as media and entertainment, gaming, ecommerce...

Streamlining ETL data processing at Talent.com with Amazon SageMaker | Amazon Web Services

This post is co-authored by Anatoly Khomenko, Machine Learning Engineer, and Abdenour Bezzouh, Chief Technology Officer at Talent.com. Established in 2011, Talent.com aggregates paid...

Fine-tune Llama 2 using QLoRA and Deploy it on Amazon SageMaker with AWS Inferentia2 | Amazon Web Services

In this post, we showcase fine-tuning a Llama 2 model using a Parameter-Efficient Fine-Tuning (PEFT) method and deploy the fine-tuned model on AWS Inferentia2....

Build an end-to-end MLOps pipeline using Amazon SageMaker Pipelines, GitHub, and GitHub Actions | Amazon Web Services

Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering...

Create a web UI to interact with LLMs using Amazon SageMaker JumpStart | Amazon Web Services

The launch of ChatGPT and rise in popularity of generative AI have captured the imagination of customers who are curious about how they can...

Frugality meets Accuracy: Cost-efficient training of GPT NeoX and Pythia models with AWS Trainium | Amazon Web Services

Large language models (or LLMs) have become a topic of daily conversations. Their quick adoption is evident by the amount of time required to...

Mitigate hallucinations through Retrieval Augmented Generation using Pinecone vector database & Llama-2 from Amazon SageMaker JumpStart | Amazon Web Services

Despite the seemingly unstoppable adoption of LLMs across industries, they are one component of a broader technology ecosystem that is powering the new AI...

Techniques for automatic summarization of documents using language models | Amazon Web Services

Summarization is the technique of condensing sizable information into a compact and meaningful form, and stands as a cornerstone of efficient communication in our...

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