AWS Machine Learning

MAY 2, 2024

In September of 2023, we announced the launch of Amazon Titan Text Embeddings V1, a multilingual text embeddings model that converts text inputs like single words, phrases, or large documents into high-dimensional numerical vector representations. In this benchmark, 33 different text embedding models were evaluated on the MTEB tasks.

Benchmark 128

Benchmark Metrics Enterprise APIs 128

AWS Machine Learning

NOVEMBER 18, 2024

One of the key considerations while designing the chat assistant was to avoid responses from the default large language model (LLM) trained on generic data and only use the insurance policy documents. The benchmarking results demonstrated unparalleled speed and affordability in its category.

Benchmark 105

Benchmark APIs Engineering Sales 105

AWS Machine Learning

NOVEMBER 15, 2024

Vision Instruct models demonstrated impressive performance on the challenging DocVQA benchmark for visual question answering. This dataset consists of a diverse collection of document images paired with a series of natural language questions that require both visual and textual understanding to answer correctly. The Meta Llama 3.2

Benchmark 108

Benchmark Metrics Management Analytics 108

Calltools

AUGUST 19, 2020

This data allows them to bolster those areas to meet or even surpass industry standard call center KPI benchmarks, which is essential for your brand’s reputation. Wait time should be one of your most important call center KPI benchmarks. Proper documentation is key to preserving your brand’s reputation.

Benchmark 52

Benchmark Call Center Abandon rate Interactive Voice Response 52

AWS Machine Learning

JUNE 25, 2024

Jamba-Instruct is built by AI21 Labs, and most notably supports a 256,000-token context window, making it especially useful for processing large documents and complex Retrieval Augmented Generation (RAG) applications. Prompt guidance for Jamba-Instruct can be found in the AI21 model documentation.

Benchmark 120

Benchmark APIs Enterprise Technology 120

AWS Machine Learning

NOVEMBER 18, 2024

Amazon Titan Text Embeddings models generate meaningful semantic representations of documents, paragraphs, and sentences. We ran the Massive Text Embedding Benchmark (MTEB) retrieval data set with binary embeddings. RAG helps FMs deliver more relevant, accurate, and customized responses. Binary embeddings maintained 98.5%

Benchmark 104

Benchmark APIs Engineering Analytics 104

AWS Machine Learning

SEPTEMBER 12, 2024

You can use the BGE embedding model to retrieve relevant documents and then use the BGE reranker to obtain final results. On Hugging Face, the Massive Text Embedding Benchmark (MTEB) is provided as a leaderboard for diverse text embedding tasks. It currently provides 129 benchmarking datasets across 8 different tasks on 113 languages.

APIs 129

APIs Benchmark Enterprise Construction 129

AWS Machine Learning

AUGUST 2, 2024

For more details about how to run graph multi-task learning with GraphStorm, refer to Multi-task Learning in GraphStorm in our documentation. we released a LM+GNN benchmark using the large graph dataset, Microsoft Academic Graph (MAG), on two standard graph ML tasks: node classification and link prediction. Dataset Num. of nodes Num.

APIs 119

APIs Benchmark Construction Enterprise 119

AWS Machine Learning

JUNE 6, 2024

You can use this tutorial as a starting point for a variety of chatbot-based solutions for customer service, internal support, and question answering systems based on internal and private documents. This makes the models especially powerful at tasks such as clustering for long documents like legal text or product documentation.

Benchmark 117

Benchmark Enterprise Construction APIs 117

Lumoa

MARCH 24, 2022

Setting survey response rate benchmarks can help you assess the performance and overall growth of your customer experience management (CEM) system. While benchmarking is a common process in many companies, the exact steps and data collected need to be adjusted to each organization’s requirements.

Surveys 78

Surveys Benchmark Metrics Feedback 78

Amity

NOVEMBER 21, 2016

Customer benchmarking — the practice of identifying where a customer can improve or is already doing well by comparing to other customers – helps Customer Success Managers to deliver unique value to their customers. I’ve found that SaaS vendors use seven distinct strategies to empower CSMs with customer benchmarking.

Benchmark 59

Benchmark SaaS Best practices Metrics 59

AWS Machine Learning

OCTOBER 10, 2024

One such initiative focused on developing a custom solution for grounded question answering (Q&A) based on reference documents. In this post, we share how SKT customizes Anthropic Claude models for telco-specific Q&A regarding technical telecommunication documents of SKT using Amazon Bedrock. He holds an M.S.

Telecommunications 86

Telecommunications Construction Benchmark Engineering 86

AWS Machine Learning

JUNE 11, 2024

This post describes how to get started with the software development agent, gives an overview of how the agent works, and discusses its performance on public benchmarks. We are grateful to the team releasing and maintaining this benchmark. We are proud to be able to share our state-of-the-art results on this benchmark.

Benchmark 128

Benchmark Metrics Feedback Personalization 128

DMG Consulting

MARCH 15, 2017

Consumers Channel of Choice – A Multi-generational Benchmark Study of Consumer Channel Preferences (whitepaper). DMG Consulting LLC has conducted a worldwide consumer benchmark study to identify consumer channel preferences for interacting with businesses. By Donna Fluss. Monthly DMG Newsletter. DMG Surveys.

Benchmark 48

Benchmark Consulting Consulting Surveys 48

AWS Machine Learning

NOVEMBER 22, 2023

When a customer has a production-ready intelligent document processing (IDP) workload, we often receive requests for a Well-Architected review. To follow along with this post, you should be familiar with the previous posts in this series ( Part 1 and Part 2 ) and the guidelines in Guidance for Intelligent Document Processing on AWS.

APIs 120

APIs Metrics Benchmark Enterprise 120

AWS Machine Learning

JUNE 20, 2024

In addition, RAG architecture can lead to potential issues like retrieval collapse , where the retrieval component learns to retrieve the same documents regardless of the input. Lack of standardized benchmarks – There are no widely accepted and standardized benchmarks yet for holistically evaluating different capabilities of RAG systems.

Metrics 132

Metrics Engineering Accountability Benchmark 132

Totango

FEBRUARY 2, 2021

Customer success plans are proposals that document your clients’ goals and how you will help achieve them. A set of key performance indicators and benchmarks to track and measure client progress towards goals. You could then define four minutes and three minutes as benchmarks along your customer’s path to their goal.

Benchmark 127

Benchmark Chatbots Accountability Enterprise 127

AWS Machine Learning

JUNE 20, 2023

AWS Inferentia and AWS Trainium for sustainable ML To provide you with realistic numbers of the energy savings potential of AWS Inferentia and AWS Trainium in a real-world application, we have conducted several power draw benchmark experiments. Therefore, we used common customer-inspired ML use cases for benchmarking and testing.

Benchmark 88

Benchmark Engineering Banking Chatbots 88

AWS Machine Learning

AUGUST 3, 2022

Amazon Comprehend is a natural-language processing (NLP) service you can use to automatically extract entities, key phrases, language, sentiments, and other insights from documents. All you need to do is load your dataset of documents and annotations, and use the Amazon Comprehend console, AWS CLI, or APIs to create the model.

Benchmark 79

Benchmark APIs Metrics Scripts 79

AWS Machine Learning

AUGUST 13, 2024

The healthcare industry generates and collects a significant amount of unstructured textual data, including clinical documentation such as patient information, medical history, and test results, as well as non-clinical documentation like administrative records. read()) answer = response_body.get("content")[0].get("text")

Healthcare 128

Healthcare APIs Consulting Consulting 128

AWS Machine Learning

JUNE 15, 2023

First, we put the source documents, reference documents, and parallel data training set in an S3 bucket. The source_data folder contains the source documents before the translation; the generated documents after the batch translation are put in the output folder.

APIs 94

APIs Benchmark Best practices Engineering 94

AWS Machine Learning

NOVEMBER 22, 2023

Model choices – SageMaker JumpStart offers a selection of state-of-the-art ML models that consistently rank among the top in industry-recognized HELM benchmarks. For instance, a financial firm might prefer its Q&A bot to source answers from its latest internal documents, ensuring accuracy and compliance with its business rules.

Engineering 121

Engineering Chatbots Benchmark APIs 121

Callminer

MAY 26, 2020

Average Handle Time (AHT) gives an accurate, real-time measurement of the usual amount of time it takes to handle an interaction from start to finish, from the initiation of the call to the time your organization’s call center agents are spending on the phone with individual callers and handling any follow-up tasks, such as documentation.

Call Center 340

Call Center Metrics Average Handle Time First call resolution 340

AWS Machine Learning

MAY 17, 2024

What is Mixtral 8x22B Mixtral 8x22B is Mistral AI’s latest open-weights model and sets a new standard for performance and efficiency of available foundation models , as measured by Mistral AI across standard industry benchmarks. making the model available for exploring, testing, and deploying.

Benchmark 127

Benchmark APIs Personalization Enterprise 127

AWS Machine Learning

NOVEMBER 21, 2023

Before intelligent applications could be built from embeddings, enterprises and organizations had to embed their existing documents, which can be expensive and technically complicated. It currently provides 129 benchmarking datasets across 8 different tasks on 113 languages.

Benchmark 114

Benchmark Enterprise Management Personalization 114

AWS Machine Learning

MAY 9, 2024

Benchmarking and metrics – Defining standardized metrics and benchmarking to measure and compare the performance of AI models, and the business value derived. Documentation – Emphasize the documentation of processes, workflows, and best practices within the platform to facilitate knowledge sharing among practitioners and teams.

Government 131

Government Best practices Benchmark Metrics 131

AWS Machine Learning

APRIL 25, 2024

Government agencies summarize lengthy policy documents and reports to help policymakers strategize and prioritize goals. By creating condensed versions of long, complex documents, summarization technology enables users to focus on the most salient content. This leads to better comprehension and retention of critical information.

Metrics 132

Metrics Benchmark Government Calibration 132

AWS Machine Learning

NOVEMBER 19, 2024

Its agent for software development can solve complex tasks that go beyond code suggestions, such as building entire application features, refactoring code, or generating documentation. Attendees will learn practical applications of generative AI for streamlining and automating document-centric workflows. Hear from Availity on how 1.5

APIs 89

APIs Enterprise Best practices Government 89

AWS Machine Learning

NOVEMBER 18, 2024

Sonnet, also newly released, setting new industry benchmarks for graduate-level reasoning and improvements in grasping complex instructions. Thanks to the multimodal capabilities of Amazon Bedrock models, you can apply this solution to different types of media, such as documents, summarizations, translations, and more.

APIs 108

APIs Entertainment Benchmark Advertising 108

AWS Machine Learning

MARCH 20, 2023

Refer to the appendix for instance details and benchmark data. To access the code and documentation, refer to the GitHub repo. Given a document as an input, the model will answer simple questions based on the learning and contexts from the input document. The following diagram illustrates the high-level flow.

Calibration 91

Calibration Scripts Benchmark APIs 91

AWS Machine Learning

OCTOBER 2, 2024

Laying the groundwork: Collecting ground truth data The foundation of any successful agent is high-quality ground truth data—the accurate, real-world observations used as reference for benchmarks and evaluating the performance of a model, algorithm, or system. Implement citation mechanisms to reference source documents in responses.

Best practices 99

Best practices APIs Metrics Benchmark 99

AWS Machine Learning

SEPTEMBER 11, 2024

Accelerator benchmarking When considering compute services, users benchmark measures such as price-performance, absolute performance, availability, latency, and throughput. A second benchmarking measure is absolute-performance , meaning how quickly can a given job be completed independent of price.

Benchmark 102

Benchmark Banking Analytics Big data 102

AWS Machine Learning

APRIL 22, 2024

These include the ability to analyze massive amounts of data, identify patterns, summarize documents, perform translations, correct errors, or answer questions. This involves documenting data lineage, data versioning, automating data processing, and monitoring data management costs.

Best practices 133

Best practices Benchmark Transportation Engineering 133

AWS Machine Learning

NOVEMBER 15, 2024

This centralized system consolidates a wide range of data sources, including detailed reports, FAQs, and technical documents. The system integrates structured data, such as tables containing product properties and specifications, with unstructured text documents that provide in-depth product descriptions and usage guidelines.

APIs 119

APIs Engineering Chatbots Construction 119

AWS Machine Learning

SEPTEMBER 25, 2024

With a focus on responsible innovation and system-level safety, these new models demonstrate state-of-the-art performance on a wide range of industry benchmarks and introduce features that help you build a new generation of AI experiences. Model Name Model ID Default instance type Supported instance types Llama-3.2-1B 32xlarge Llama-3.2-1B-Instruct

Engineering 129

Engineering Benchmark Management Accountability 129

Totango

NOVEMBER 10, 2020

Outcome success plans focus on capturing mutual objectives, documenting the steps toward achieving them, and sharing information between both clients and your own internal teams—driving interconnectivity and displaying progress through one easily accessed live portal. Document and capture new initiatives as they arise.

Benchmark 121

Benchmark Enterprise Accountability Management 121