AWS Machine Learning

MARCH 6, 2025

Current RAG pipelines frequently employ similarity-based metrics such as ROUGE , BLEU , and BERTScore to assess the quality of the generated responses, which is essential for refining and enhancing the models capabilities. More sophisticated metrics are needed to evaluate factual alignment and accuracy.

Metrics 118

Metrics Enterprise APIs Engineering 118

AWS Machine Learning

OCTOBER 29, 2024

All of this data is centralized and can be used to improve metrics in scenarios such as sales or call centers. For integration between services, we use API Gateway as an event trigger for our Lambda function, and DynamoDB as a highly scalable database to store our customer details.

Engineering 111

Engineering APIs Sales Enterprise 111

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

APIs 117

APIs Benchmark Construction Enterprise 117

AWS Machine Learning

FEBRUARY 12, 2025

This approach allows organizations to assess their AI models effectiveness using pre-defined metrics, making sure that the technology aligns with their specific needs and objectives. The introduction of an LLM-as-a-judge framework represents a significant step forward in simplifying and streamlining the model evaluation process.

Metrics 104

Metrics Engineering Benchmark APIs 104

AWS Machine Learning

SEPTEMBER 28, 2023

In this post, we describe the enhancements to the forecasting capabilities of SageMaker Canvas and guide you on using its user interface (UI) and AutoML APIs for time-series forecasting. While the SageMaker Canvas UI offers a code-free visual interface, the APIs empower developers to interact with these features programmatically.

APIs 119

APIs Construction Finance Enterprise 119

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 107

APIs Enterprise Best practices Government 107

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 116

Biotechnology Scripts Healthcare APIs 116

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

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 125

APIs Engineering Scripts Management 125

AWS Machine Learning

OCTOBER 9, 2024

Amazon Lookout for Metrics is a fully managed service that uses machine learning (ML) to detect anomalies in virtually any time-series business or operational metrics—such as revenue performance, purchase transactions, and customer acquisition and retention rates—with no ML experience required. To learn more, see the documentation.

Metrics 94

Metrics APIs Engineering Accountability 94

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

Engineering Management APIs Healthcare 115

AWS Machine Learning

MARCH 22, 2023

With Amazon Rekognition Custom Labels , you can have Amazon Rekognition train a custom model for object detection or image classification specific to your business needs. Rekognition Custom Labels builds off of the existing capabilities of Amazon Rekognition, which is already trained on tens of millions of images across many categories.

APIs 98

APIs Metrics Engineering Feedback 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 128

Finance Metrics Banking Engineering 128

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 109

APIs Management Marketing Engineering 109

AWS Machine Learning

FEBRUARY 24, 2025

Performance metrics and benchmarks According to Mistral, the instruction-tuned version of the model achieves over 81% accuracy on Massive Multitask Language Understanding (MMLU) with 150 tokens per second latency, making it currently the most efficient model in its category. It doesnt support Converse APIs or other Amazon Bedrock tooling.

APIs 94

APIs Enterprise Benchmark Feedback 94

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 109

Benchmark APIs Enterprise Construction 109

AWS Machine Learning

JANUARY 28, 2025

To effectively optimize AI applications for responsiveness, we need to understand the key metrics that define latency and how they impact user experience. These metrics differ between streaming and nonstreaming modes and understanding them is crucial for building responsive AI applications.

Benchmark 77

Benchmark APIs Engineering Metrics 77

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. Here we use the number of requests per second as a custom metric.

Metrics 125

Metrics APIs Scripts Construction 125

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 88

Customer Service Scripts Coaching Finance 88

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 70

APIs Engineering Metrics Big data 70

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

NOVEMBER 15, 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.

Chatbots 113

Chatbots Engineering Enterprise Government 113

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

MARCH 6, 2023

Large language models (LLMs) are neural network-based language models with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical. The size of an LLM and its training data is a double-edged sword: it brings modeling quality, but entails infrastructure challenges.

Best practices 106

Best practices APIs Transportation Scripts 106

AWS Machine Learning

DECEMBER 2, 2024

NVIDIA Nemotron-4 is now available on Amazon SageMaker JumpStart , significantly expanding the range of high-quality, pre-trained models available to our customers. 70B-Instruct : a 70-billion-parameter pre-trained, instruction-tuned model optimized for multilingual dialogue. Mixtral 8x7B Instruct v0.1:

Enterprise 111

Enterprise Benchmark Technology APIs 111

AWS Machine Learning

OCTOBER 5, 2022

In this post, we discuss the key elements needed to evaluate the performance aspect of a content moderation service in terms of various accuracy metrics, and a provide an example using Amazon Rekognition Content Moderation API’s. Understanding such distribution can help you define your actual metric goals. What to evaluate.

Metrics 101

Metrics APIs Advertising Enterprise 101

AWS Machine Learning

OCTOBER 6, 2022

Model training forms the core of any machine learning (ML) project, and having a trained ML model is essential to adding intelligence to a modern application. Generally speaking, training a model from scratch is time-consuming and compute intensive. Model training in Studio. This post showcases the results of the study.

Metrics 98

Metrics Benchmark APIs Accountability 98

AWS Machine Learning

JULY 10, 2024

Frontier large language models (LLMs) like Anthropic Claude on Amazon Bedrock are trained on vast amounts of data, allowing Anthropic Claude to understand and generate human-like text. Solution overview Fine-tuning is a technique in natural language processing (NLP) where a pre-trained language model is customized for a specific task.

APIs 131

APIs Airlines Metrics Engineering 131

AWS Machine Learning

JANUARY 7, 2025

By incorporating these human translations into the LLMs training or inference process, the LLM can learn from and reuse these high-quality translations, potentially improving its overall performance. Amazon Titan Text Embeddings V2 includes multilingual support for over 100 languages in pre-training.

Engineering 113

Engineering Metrics industry standards Finance 113

AWS Machine Learning

JANUARY 10, 2024

When a model is trained and ready to be used, it needs to be approved after being registered in the Amazon SageMaker Model Registry. Overview of solution This post focuses on a workflow solution that the ML model development lifecycle can use between the training pipeline and inferencing pipeline.

APIs 129

APIs Construction Engineering Analytics 129

AWS Machine Learning

JUNE 5, 2024

In this post, we show how to use Amazon Comprehend Custom to train and host an ML model to classify if the input email is an phishing attempt or not. Comprehend Custom builds customized NLP models on your behalf, using training data that you provide. For minimum training requirements, see General quotas for document classification.

APIs 121

APIs Metrics Accountability Enterprise 121

AWS Machine Learning

OCTOBER 2, 2024

It’s a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like Anthropic, Cohere, Meta, Mistral 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.

Government 129

Government APIs Enterprise Best practices 129

AWS Machine Learning

MAY 30, 2023

In this post, we focus on SageMaker training jobs. With SageMaker training jobs, you can bring your own algorithm or choose from more than 25 built-in algorithms. The cost of a training job is based on the resources you use (instances and storage) for the duration (in seconds) that those instances are running.

Best practices 97

Best practices APIs Metrics Enterprise 97

AWS Machine Learning

NOVEMBER 6, 2023

Within hours, you can annotate your sample documents using the AWS Management Console and train an adapter. Adapters are components that plug in to the Amazon Textract pre-trained deep learning model, customizing its output based on your annotated documents. Adapters can be created via the console or programmatically via the API.

APIs 128

APIs Best practices Banking Accountability 128

AWS Machine Learning

JUNE 7, 2023

Trained on 1 trillion tokens, TII Falcon LLM boasts top-notch performance while remaining incredibly cost-effective. It’s available as open-source in two different sizes – Falcon-40B and Falcon-7B and was built from scratch using data preprocessing and model training jobs built on Amazon SageMaker. In 2022, Hoffman et al.

Technology 101

Technology APIs Engineering Education 101

AWS Machine Learning

AUGUST 7, 2024

Trained on a large volume of datasets, these models incorporate memory components in their architectural design, allowing them to understand and comprehend textual context. RAG is the process of optimizing the output of an LLM so it references an authoritative knowledge base outside of its training data sources before generating a response.

APIs 134

APIs Chatbots Metrics Education 134

AWS Machine Learning

OCTOBER 24, 2024

The retrieve_and_generate API does both the retrieval and a call to an FM (Amazon Titan or Anthropic’s Claude family of models on Amazon Bedrock ), for a fully managed solution. Mean Reciprocal Rank (MRR) – This metric considers the ranking of the retrieved documents. External – Customers directly chat with a generative AI chatbot.

Chatbots 93

Chatbots Metrics Scripts APIs 93