Auto-classification, Auto-complete and Generative AI

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Generative AI

Announcing New Tools for Building with Generative AI on AWS

Flipboard

APRIL 13, 2023

Just recently, generative AI applications like ChatGPT have captured widespread attention and imagination. We are truly at an exciting inflection point in the widespread adoption of ML, and we believe most customer experiences and applications will be reinvented with generative AI.

Generative AI

Generative AI ML AI AI

Building Generative AI prompt chaining workflows with human in the loop

AWS Machine Learning Blog

MAY 17, 2024

Generative AI is a type of artificial intelligence (AI) that can be used to create new content, including conversations, stories, images, videos, and music. Like all AI, generative AI works by using machine learning models—very large models that are pretrained on vast amounts of data called foundation models (FMs).

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Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

AWS Machine Learning Blog

DECEMBER 5, 2023

The insurance provider receives payout claims from the beneficiary’s attorney for different insurance types, such as home, auto, and life insurance. When this is complete, the document can be routed to the appropriate department or downstream process. Custom classification is a two-step process.

Metadata

Metadata Auto-classification Auto-complete Content Enrichment

Webinars

Relevance, Reach, Return: How to Turn Marketing Trends From Hype to High-Impact

Automation, Evolved: Your New Playbook For Smarter Knowledge Work

MORE WEBINARS

Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

JUNE 3, 2024

In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing. With a data flow, you can prepare data using generative AI, over 300 built-in transforms, or custom Spark commands. For Problem type , select Classification.

Generative AI

Generative AI Categorization Auto-complete Auto-classification

Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning Blog

MARCH 28, 2024

These generative AI applications are not only used to automate existing business processes, but also have the ability to transform the experience for customers using these applications.

LLM

LLM Auto-complete Auto-classification Generative AI

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

NOVEMBER 14, 2024

Optionally, if Account A and Account B are part of the same AWS Organizations, and the resource sharing is enabled within AWS Organizations, then the resource sharing invitation are auto accepted without any manual intervention. Following are the steps completed by using APIs to create and share a model package group across accounts.

ML Machine Learning Auto-complete Auto-classification

Dialogue-guided visual language processing with Amazon SageMaker JumpStart

AWS Machine Learning Blog

NOVEMBER 1, 2023

Visual language processing (VLP) is at the forefront of generative AI, driving advancements in multimodal learning that encompasses language intelligence, vision understanding, and processing. Solution overview The proposed VLP solution integrates a suite of state-of-the-art generative AI modules to yield accurate multimodal outputs.

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Auto-classification LLM Auto-complete Generative AI

Scaling Thomson Reuters’ language model research with Amazon SageMaker HyperPod

AWS Machine Learning Blog

SEPTEMBER 12, 2024

Thomson Reuters , a global content and technology-driven company, has been using artificial intelligence and machine learning (AI/ML) in its professional information products for decades. Legal research is a critical area for Thomson Reuters customers—it needs to be as complete as possible. 55 440 0.1 164 64 512 0.1

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Auto-classification Auto-complete LLM ML

Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

AUGUST 17, 2023

Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Save the CloudFormation template sm-redshift-demo-vpc-cfn-v1.yaml Launch SageMaker Studio Complete the following steps to launch your SageMaker Studio domain: On the SageMaker console, choose Domains in the navigation pane.

ML Auto-complete Auto-classification Machine Learning

List of Groundbreaking and Open-Source Conversational AI Models in the Language Domain

Marktechpost

JULY 15, 2023

Based on the transformer architecture, Vicuna is an auto-regressive language model and offers natural and engaging conversation capabilities. The chatbot is designed for conversation and instruction and excels in summarizing, generating tables, classification, and dialog. trillion tokens.

Conversational AI

Conversational AI AI Modeling Large Language Models Auto-classification

Falcon 2 11B is now available on Amazon SageMaker JumpStart

AWS Machine Learning Blog

MAY 31, 2024

It’s a next generation model in the Falcon family—a more efficient and accessible large language model (LLM) that is trained on a 5.5 It’s built on causal decoder-only architecture, making it powerful for auto-regressive tasks. After deployment is complete, you will see that an endpoint is created.

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Re-imagining Glamour Photography with Generative AI

Mlearning.ai

JULY 3, 2023

Life however decided to take me down a different path (partly thanks to Fujifilm discontinuing various films ), although I have never quite completely forgotten about glamour photography. Stable Diffusion — Generative AI for Paupers, Misers and Cheapskates Many state-of-the-art generative AI models are not open source or free to use.

Generative AI

Generative AI Auto-complete Auto-classification AI

Evaluate the reliability of Retrieval Augmented Generation applications using Amazon Bedrock

AWS Machine Learning Blog

JUNE 20, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

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Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

AWS Machine Learning Blog

JANUARY 17, 2024

Llama 2 is an auto-regressive generative text language model that uses an optimized transformer architecture. As a publicly available model, Llama 2 is designed for many NLP tasks such as text classification, sentiment analysis, language translation, language modeling, text generation, and dialogue systems.

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Auto-complete Python Machine Learning Deep Learning

Revolutionize Customer Satisfaction with tailored reward models for your business on Amazon SageMaker

AWS Machine Learning Blog

MAY 2, 2024

Unlike traditional model tasks such as classification, which can be neatly benchmarked on test datasets, assessing the quality of a sprawling conversational agent is highly subjective. Launch SageMaker Studio Complete the following steps to launch SageMaker Studio: On the SageMaker console, choose Studio in the navigation pane.

LLM

LLM Auto-complete Auto-classification Artificial Intelligence

Improved ML model deployment using Amazon SageMaker Inference Recommender

AWS Machine Learning Blog

APRIL 20, 2023

We train an XGBoost model for a classification task on a credit card fraud dataset. Model Framework XGBoost Model Size 10 MB End-to-End Latency 100 milliseconds Invocations per Second 500 (30,000 per minute) ML Task Binary Classification Input Payload 10 KB We use a synthetically created credit card fraud dataset. sm_client = boto3.client("sagemaker",

ML Auto-classification Python Auto-complete

Build well-architected IDP solutions with a custom lens – Part 5: Cost optimization

AWS Machine Learning Blog

NOVEMBER 22, 2023

If you’re not actively using the endpoint for an extended period, you should set up an auto scaling policy to reduce your costs. SageMaker provides different options for model inferences , and you can delete endpoints that aren’t being used or set up an auto scaling policy to reduce your costs on model endpoints.

IDP

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Creating An Information Edge With Conversational Access To Data

Topbots

JUNE 29, 2023

On a more advanced stance, everyone who has done SQL query optimisation will know that many roads lead to the same result, and semantically equivalent queries might have completely different syntax. 3] provides a more complete survey of Text2SQL data augmentation techniques. different variants of semantic parsing.

Algorithm

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Accelerate your generative AI distributed training workloads with the NVIDIA NeMo Framework on Amazon EKS

AWS Machine Learning Blog

JULY 16, 2024

NVIDIA NeMo Framework NVIDIA NeMo is an end-to-end cloud-centered framework for training and deploying generative AI models with billions and trillions of parameters at scale. NVIDIA NeMo simplifies generative AI model development, making it more cost-effective and efficient for enterprises. 24xlarge instances.

Generative AI

Generative AI Auto-complete Auto-classification Deep Learning

Build and evaluate machine learning models with advanced configurations using the SageMaker Canvas model leaderboard

AWS Machine Learning Blog

NOVEMBER 30, 2023

In HPO mode, SageMaker Canvas supports the following types of machine learning algorithms: Linear learner: A supervised learning algorithm that can solve either classification or regression problems. Auto: Autopilot automatically chooses either ensemble mode or HPO mode based on your dataset size. Otherwise, it chooses ensemble mode.

Machine Learning

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Fine-tune Llama 2 for text generation on Amazon SageMaker JumpStart

AWS Machine Learning Blog

SEPTEMBER 6, 2023

Now you can also fine-tune 7 billion, 13 billion, and 70 billion parameters Llama 2 text generation models on SageMaker JumpStart using the Amazon SageMaker Studio UI with a few clicks or using the SageMaker Python SDK. In this post, we walk through how to fine-tune Llama 2 pre-trained text generation models via SageMaker JumpStart.

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Fine-tune a BGE embedding model using synthetic data from Amazon Bedrock

AWS Machine Learning Blog

OCTOBER 23, 2024

Solution overview BGE stands for Beijing Academy of Artificial Intelligence (BAAI) General Embeddings. AmazonBedrockFullAccess AmazonS3FullAccess AmazonEC2ContainerRegistryFullAccess Open SageMaker Studio To open SageMaker studio, complete the following steps: On the SageMaker console, choose Studio in the navigation pane.

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Auto-complete Auto-classification Generative AI Artificial Intelligence

Artificial Intelligence Zone

Announcing New Tools for Building with Generative AI on AWS

Building Generative AI prompt chaining workflows with human in the loop

Webinars

Trending Sources

Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

Webinars

Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

Advanced RAG patterns on Amazon SageMaker

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

Dialogue-guided visual language processing with Amazon SageMaker JumpStart

Scaling Thomson Reuters’ language model research with Amazon SageMaker HyperPod

Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

List of Groundbreaking and Open-Source Conversational AI Models in the Language Domain

Falcon 2 11B is now available on Amazon SageMaker JumpStart

Re-imagining Glamour Photography with Generative AI

Evaluate the reliability of Retrieval Augmented Generation applications using Amazon Bedrock

Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

Revolutionize Customer Satisfaction with tailored reward models for your business on Amazon SageMaker

Improved ML model deployment using Amazon SageMaker Inference Recommender

Build well-architected IDP solutions with a custom lens – Part 5: Cost optimization

Creating An Information Edge With Conversational Access To Data

Accelerate your generative AI distributed training workloads with the NVIDIA NeMo Framework on Amazon EKS

Build and evaluate machine learning models with advanced configurations using the SageMaker Canvas model leaderboard

Fine-tune Llama 2 for text generation on Amazon SageMaker JumpStart

Fine-tune a BGE embedding model using synthetic data from Amazon Bedrock

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