AWS Machine Learning

AUGUST 2, 2024

GraphStorm is a low-code enterprise graph machine learning (GML) framework to build, train, and deploy graph ML solutions on complex enterprise-scale graphs in days instead of months. allows you to define multiple training targets on different nodes and edges within a single training loop. Specifically, GraphStorm 0.3

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AWS Machine Learning

OCTOBER 29, 2024

To enable the video insights solution, the architecture uses a combination of AWS services, including the following: Amazon API Gateway is a fully managed service that makes it straightforward for developers to create, publish, maintain, monitor, and secure APIs at scale.

Engineering 110

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AWS Machine Learning

MARCH 19, 2025

For their AI training and inference workloads, Adobe uses NVIDIA GPU-accelerated Amazon Elastic Compute Cloud (Amazon EC2) P5en (NVIDIA H200 GPUs), P5 (NVIDIA H100 GPUs), P4de (NVIDIA A100 GPUs), and G5 (NVIDIA A10G GPUs) instances. To train generative AI models at enterprise scale, ServiceNow uses NVIDIA DGX Cloud on AWS.

Healthcare 143

Healthcare Enterprise APIs Transportation 143

AWS Machine Learning

MAY 23, 2024

By utilizing sparse expert subnetworks that process different subsets of tokens, MoE models can effectively increase the number of parameters while requiring less computation per token during training and inference. This enables more cost-effective training of larger models within fixed compute budgets compared to dense architectures.

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AWS Machine Learning

JANUARY 28, 2025

Similarly, maintaining detailed information about the datasets used for training and evaluation helps identify potential biases and limitations in the models knowledge base. It functions as a standalone HTTP server that provides various REST API endpoints for monitoring, recording, and visualizing experiment runs.

Management 107

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AWS Machine Learning

APRIL 23, 2025

For more information, see Redacting PII entities with asynchronous jobs (API). The query is then forwarded using a REST API call to an Amazon API Gateway endpoint along with the access tokens in the header. The user query is sent using an API call along with the authentication token through Amazon API Gateway.

APIs 89

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AWS Machine Learning

MARCH 26, 2025

Amazon SageMaker JumpStart is a machine learning (ML) hub that provides pre-trained models, solution templates, and algorithms to help developers quickly get started with machine learning. For a list of available models, refer to Built-in Algorithms with pre-trained Model Table. No model artifacts need to be managed by the customer.

APIs 100

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AWS Machine Learning

OCTOBER 19, 2023

In this post, we discuss how to use the Custom Moderation feature in Amazon Rekognition to enhance the accuracy of your pre-trained content moderation API. You can train a custom adapter with as few as 20 annotated images in less than 1 hour. Create a project A project is a container to store your adapters.

APIs 132

APIs Best practices Advertising Accountability 132

AWS Machine Learning

JANUARY 24, 2025

Contrast that with Scope 4/5 applications, where not only do you build and secure the generative AI application yourself, but you are also responsible for fine-tuning and training the underlying large language model (LLM). These steps might involve both the use of an LLM and external data sources and APIs.

APIs 111

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AWS Machine Learning

APRIL 10, 2025

Demystifying RAG and model customization RAG is a technique to enhance the capability of pre-trained models by allowing the model access to external domain-specific data sources. Unlike fine-tuning, in RAG, the model doesnt undergo any training and the model weights arent updated to learn the domain knowledge.

APIs 119

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AWS Machine Learning

APRIL 10, 2025

You can get started without any prior machine learning (ML) experience, and Amazon Personalize allows you to use APIs to build sophisticated personalization capabilities. Train an Amazon Personalize Top picks for you recommender. You start by creating your dataset group, loading your data, and then training a recommender.

Personalization 97

Personalization APIs Advertising Accountability 97

AWS Machine Learning

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. config_yaml = f""" SchemaVersion: '1.0'

Metrics 98

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AWS Machine Learning

APRIL 24, 2025

Recent advances in generative AI have led to the rapid evolution of natural language to SQL (NL2SQL) technology, which uses pre-trained large language models (LLMs) and natural language to generate database queries in the moment. The end-user sends their natural language queries to the NL2SQL solution using a REST API.

Enterprise 103

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AWS Machine Learning

OCTOBER 30, 2024

Enabling Global Resiliency for an Amazon Lex bot is straightforward using the AWS Management Console , AWS Command Line Interface (AWS CLI), or APIs. Global Resiliency APIs Global Resiliency provides API support to create and manage replicas. To better understand the solution, refer to the following architecture diagram.

Chatbots 108

Chatbots Interactive Voice Response APIs Telecommunications 108

AWS Machine Learning

APRIL 29, 2024

This often means the method of using a third-party LLM API won’t do for security, control, and scale reasons. First, the AWS Trainium accelerator provides a high-performance, cost-effective, and readily available solution for training and fine-tuning large models. The following figure illustrates this workflow.

APIs 124

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AWS Machine Learning

MAY 31, 2024

In this blog post and open source project , we show you how you can pre-train a genomics language model, HyenaDNA , using your genomic data in the AWS Cloud. Amazon SageMaker Amazon SageMaker is a fully managed ML service offered by AWS, designed to reduce the time and cost associated with training and tuning ML models at scale.

Biotechnology 112

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AWS Machine Learning

APRIL 29, 2024

In recent years, large language models (LLMs) have gained attention for their effectiveness, leading various industries to adapt general LLMs to their data for improved results, making efficient training and hardware availability crucial. In this post, we show you how efficient we make our continual pre-training by using Trainium chips.

APIs 123

APIs Healthcare Scripts Enterprise 123

AWS Machine Learning

FEBRUARY 12, 2025

Many use cases involve using pre-trained large language models (LLMs) through approaches like Retrieval Augmented Generation (RAG). Fine-tuning is a supervised training process where labeled prompt and response pairs are used to further train a pre-trained model to improve its performance for a particular use case.

APIs 75

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AWS Machine Learning

NOVEMBER 19, 2024

Discover how the fully managed infrastructure of SageMaker enables high-performance, low cost ML throughout the ML lifecycle, from building and training to deploying and managing models at scale. AWS Trainium and AWS Inferentia deliver high-performance AI training and inference while reducing your costs by up to 50%.

APIs 100

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AWS Machine Learning

NOVEMBER 21, 2024

As large language models (LLMs) increasingly integrate more multimedia capabilities, human feedback becomes even more critical in training them to generate rich, multi-modal content that aligns with human quality standards. Programmatic setup Alternatively, you can create your labeling job programmatically using the CreateLabelingJob API.

APIs 98

APIs Feedback Telecommunications Management 98

AWS Machine Learning

MARCH 28, 2024

Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. For example, Meta’s Llama models are trained on datasets such as CommonCrawl , C4 , Wikipedia, and ArXiv. The resulting LLM outperforms LLMs trained on non-domain-specific datasets when tested on finance-specific tasks.

Finance 125

Finance Metrics Banking Engineering 125

AWS Machine Learning

NOVEMBER 13, 2024

The Amazon Bedrock single API access, regardless of the models you choose, gives you the flexibility to use different FMs and upgrade to the latest model versions with minimal code changes. Amazon Titan FMs provide customers with a breadth of high-performing image, multimodal, and text model choices, through a fully managed API.

Engineering 115

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AWS Machine Learning

DECEMBER 3, 2024

Agent architecture The following diagram illustrates the serverless agent architecture with standard authorization and real-time interaction, and an LLM agent layer using Amazon Bedrock Agents for multi-knowledge base and backend orchestration using API or Python executors. Domain-scoped agents enable code reuse across multiple agents.

Sales 107

Sales APIs Engineering Analytics 107

AWS Machine Learning

FEBRUARY 24, 2025

It provides a collection of pre-trained models that you can deploy quickly, accelerating the development and deployment of ML applications. One of the key components of SageMaker JumpStart is model hubs, which offer a vast catalog of pre-trained models, such as Mistral, for a variety of tasks.

APIs 86

APIs Enterprise Benchmark Feedback 86

AWS Machine Learning

MARCH 3, 2025

The vision encoder was specifically trained to natively handle variable image sizes, enabling Pixtral to accurately interpret high-resolution diagrams, charts, and documents while maintaining fast inference speeds for smaller images such as icons, clipart, and equations. To begin using Pixtral 12B, choose Deploy.

Benchmark 101

Benchmark APIs Enterprise Construction 101

AWS Machine Learning

SEPTEMBER 26, 2024

This text-to-video API generates high-quality, realistic videos quickly from text and images. Trained on the Amazon SageMaker HyperPod , Dream Machine excels in creating consistent characters, smooth motion, and dynamic camera movements. Luma AI’s recently launched Dream Machine represents a significant advancement in this field.

Scripts 119

Scripts APIs Metrics Management 119

AWS Machine Learning

APRIL 19, 2024

In this post, we focus on how we used Karpenter on Amazon Elastic Kubernetes Service (Amazon EKS) to scale AI training and inference, which are core elements of the Iambic discovery platform. We wanted to build a scalable system to support AI training and inference. per training task 20 minutes average $0.38

Metrics 124

Metrics APIs Scripts Construction 124

AWS Machine Learning

OCTOBER 16, 2024

This allowed Intact to transcribe customer calls accurately, train custom language models, simplify the call auditing process, and extract valuable customer insights more efficiently. This efficiency has allowed for more effective use of auditors’ time in devising coaching strategies, improving scripts, and agent training.

Customer Service 86

Customer Service Scripts Coaching Finance 86

AWS Machine Learning

JANUARY 31, 2025

Increasingly, FMs are completing tasks that were previously solved by supervised learning, which is a subset of machine learning (ML) that involves training algorithms using a labeled dataset. Although we used a pre-trained FM, the problem was formulated as a text classification task.

Engineering 91

Engineering Enterprise APIs Consulting 91

AWS Machine Learning

MARCH 4, 2025

invoke(inputs["query"])) ) return retrieval_chain Option 2: Access the underlying Boto3 API The Boto3 API is able to directly retrieve with a dynamic retrieval_config. For Amazon Bedrock: Use IAM roles and policies to control access to Bedrock resources and APIs.

APIs 106

APIs Best practices Management Personalization 106

AWS Machine Learning

MARCH 6, 2025

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, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

Metrics 114

Metrics Enterprise APIs Engineering 114

AWS Machine Learning

JANUARY 24, 2025

Fine-tuning pre-trained language models allows organizations to customize and optimize the models for their specific use cases, providing better performance and more accurate outputs tailored to their unique data and requirements. Model customization in Amazon Bedrock involves the following actions: Create training and validation datasets.

Best practices 113

Best practices Accountability industry standards Metrics 113

AWS Machine Learning

JANUARY 31, 2024

We provide guidance on building, training, and deploying deep learning networks on Amazon SageMaker. With training and practice, ELA can also learn to identify image scaling, quality, cropping, and resave transformations. We selected a larger instance type to reduce the training time of the model. Set up Amazon SageMaker Studio.

APIs 117

APIs Banking Construction Accountability 117

AWS Machine Learning

APRIL 10, 2025

For instance, Pixtral Large is highly effective at spotting irregularities or insightful trends within training loss curves or performance metrics, enhancing the accuracy of data-driven decision-making. By choosing View API , you can also access the model using code examples in the AWS Command Line Interface (AWS CLI) and AWS SDKs.

APIs 100

APIs Management Marketing Engineering 100

AWS Machine Learning

FEBRUARY 18, 2025

During these live events, F1 IT engineers must triage critical issues across its services, such as network degradation to one of its APIs. This impacts downstream services that consume data from the API, including products such as F1 TV, which offer live and on-demand coverage of every race as well as real-time telemetry.

APIs 69

APIs Engineering Metrics Big data 69

CCNG

JUNE 7, 2023

Language Support : Chat GPT can be trained in multiple languages, enabling contact centers to provide support to customers globally without the need for multilingual agents. Training is key and a very important first step as Fred shares. This helps businesses expand their customer base and cater to a diverse range of customers.

Contact Center 195

Contact Center CCNG APIs Scripts 195

AWS Machine Learning

APRIL 8, 2025

Additionally, the integration of SageMaker features in iFoods infrastructure automates critical processes, such as generating training datasets, training models, deploying models to production, and continuously monitoring their performance. In this post, we show how iFood uses SageMaker to revolutionize its ML operations.

Engineering 102

Engineering Virtual Agent Management Technology 102