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

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.

APIs 120

APIs Benchmark Enterprise Technology 120

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

JUNE 15, 2023

We demonstrate how to use the AWS Management Console and Amazon Translate public API to deliver automatic machine batch translation, and analyze the translations between two language pairs: English and Chinese, and English and Spanish. First, we put the source documents, reference documents, and parallel data training set in an S3 bucket.

APIs 94

APIs Benchmark Best practices Engineering 94

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 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

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

SEPTEMBER 6, 2023

These SageMaker endpoints are consumed in the Amplify React application through Amazon API Gateway and AWS Lambda functions. To protect the application and APIs from inadvertent access, Amazon Cognito is integrated into Amplify React, API Gateway, and Lambda functions. You access the React application from your computer.

Enterprise 122

Enterprise APIs Real estate Construction 122

AWS Machine Learning

OCTOBER 2, 2024

In addition, they use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledge bases using Retrieval Augmented Generation (RAG) to provide an answer to the user’s request. In Part 1, we focus on creating accurate and reliable agents.

Best practices 99

Best practices APIs Metrics Accountability 99

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

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

OCTOBER 21, 2024

In this blog post, we will introduce how to use an Amazon EC2 Inf2 instance to cost-effectively deploy multiple industry-leading LLMs on AWS Inferentia2 , a purpose-built AWS AI chip, helping customers to quickly test and open up an API interface to facilitate performance benchmarking and downstream application calls at the same time.

APIs 86

APIs Benchmark Education Marketing 86

AWS Machine Learning

NOVEMBER 1, 2024

Tools and APIs – For example, when you need to teach Anthropic’s Claude 3 Haiku how to use your APIs well. Based on our hyperparameter tuning experiments across different use cases, the API allows a range of 4–256, with a default of 32.

Best practices 96

Best practices APIs Finance Engineering 96

AWS Machine Learning

NOVEMBER 22, 2023

An intelligent document processing (IDP) project usually combines optical character recognition (OCR) and natural language processing (NLP) to read and understand a document and extract specific terms or words. As of this writing, it includes the following values: TABLES , FORMS , QUERIES , SIGNATURES , and LAYOUT.

Finance 108

Finance Best practices APIs Accountability 108

AWS Machine Learning

NOVEMBER 30, 2023

SageMaker makes it easy to deploy models into production directly through API calls to the service. It’s a low-level API available for Java, C++, Go, JavaScript, Node.js, PHP, Ruby, and Python. It’s a low-level API available for Java, C++, Go, JavaScript, Node.js, PHP, Ruby, and Python.

Benchmark 129

Benchmark APIs Scripts Engineering 129

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

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 APIs Benchmark 121

AWS Machine Learning

JULY 10, 2024

AI Service Cards are a form of responsible AI documentation that provide customers with a single place to find information on the intended use cases and limitations, responsible AI design choices, and deployment and performance optimization best practices for our AI services and models.

Government 126

Government Education Best practices APIs 126

AWS Machine Learning

JULY 18, 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.

APIs 118

APIs Technology Analytics Benchmark 118

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.

APIs 127

APIs Benchmark Personalization Enterprise 127

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

SEPTEMBER 11, 2024

In terms of resulting speedups, the approximate order is programming hardware, then programming against PBA APIs, then programming in an unmanaged language such as C++, then a managed language such as Python. The CUDA API and SDK were first released by NVIDIA in 2007. GPU PBAs, 4% other PBAs, 4% FPGA, and 0.5%

Benchmark 102

Benchmark Banking Analytics Big data 102

AWS Machine Learning

NOVEMBER 30, 2023

When ML models deployed on instances receive API calls from a large number of clients, a random distribution of requests can work very well when there is not a lot of variability in your requests and responses. To learn more about SageMaker routing features, refer to documentation.

Engineering 116

Engineering APIs Benchmark Enterprise 116

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 31, 2022

To get started, follow Modify a PyTorch Training Script to adapt SMPs’ APIs in your training script. You can follow the comments in the script and API document to learn more about where SMP APIs are used. Benchmarking performance. We benchmarked sharded data parallelism in the SMP library on both 16 and 32 p4d.24xlarge

Scripts 76

Scripts Benchmark APIs Engineering 76

AWS Machine Learning

DECEMBER 16, 2022

Finally, we’ll benchmark performance of 13B, 50B, and 100B parameter auto-regressive models and wrap up with future work. For training a different model type, you can follow the API document to learn about how to apply SMP APIs. You can refer to this document for supported configurations. Benchmarking performance.

Benchmark 81

Benchmark Engineering APIs Scripts 81

Spearline

JULY 21, 2022

And testingRTC offers multiple ways to export these metrics, from direct collection from webhooks, to downloading results in CSV format using the REST API. testingRTC is predominantly a self-service platform, where you write and test any script you want independently of us with our extensive knowledge base documentation as a guide.

Scripts 98

Scripts APIs Metrics Analytics 98

AWS Machine Learning

JULY 25, 2024

Although existing large language model (LLM) benchmarks like MT-bench evaluate model capabilities, they lack the ability to validate the application layers. Refer to the agent-evaluation target documentation for details. The principal must have the permissions to call the target agent.

APIs 130

APIs Engineering Best practices Virtual Agent 130

AWS Machine Learning

AUGUST 22, 2024

Snowflake Arctic is a family of enterprise-grade large language models (LLMs) built by Snowflake to cater to the needs of enterprise users, exhibiting exceptional capabilities (as shown in the following benchmarks ) in SQL querying, coding, and accurately following instructions. To learn more, refer to API documentation.

Enterprise 124

Enterprise APIs Benchmark Scripts 124

AWS Machine Learning

MAY 13, 2024

This is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API. It’s serverless, so you don’t have to manage any infrastructure.

Healthcare 129

Healthcare Engineering APIs Benchmark 129

AWS Machine Learning

DECEMBER 1, 2022

They show the usage of various SageMaker and JumpStart APIs. This notebook demonstrates how to deploy AlexaTM 20B through the JumpStart API and run inference. NTM is most commonly used to discover a user-specified number of topics shared by documents within a text corpus. This is called zero-shot in-context learning.

APIs 116

APIs Benchmark Advertising Banking 116

AWS Machine Learning

AUGUST 26, 2024

This involves benchmarking new models against our current selections across various metrics, running A/B tests, and gradually incorporating high-performing models into our production pipeline. API design Account summary generation requests are handled asynchronously to eliminate client wait times for responses.

Sales 126

Sales Accountability Feedback Metrics 126

AWS Machine Learning

JANUARY 10, 2023

We first benchmark the performance of our model on a single instance to identify the TPS it can handle per our acceptable latency requirements. For example, if you client is making the InvokeEndpoint API call over the internet, from the client’s perspective, the end-to-end latency would be internet + ModelLatency + OverheadLatency.

Best practices 88

Best practices Scripts APIs Metrics 88

AWS Machine Learning

MARCH 14, 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 via a single API. Kojima et al. 2022) introduced an idea of zero-shot CoT by using FMs’ untapped zero-shot capabilities.

Best practices 129

Best practices Engineering Chatbots Enterprise 129

AWS Machine Learning

APRIL 27, 2023

Similar to the process of PyTorch integration with C++ code, Neuron CustomOps requires a C++ implementation of an operator via a NeuronCore-ported subset of the Torch C++ API. Finally, the custom library is built by calling the load API. The steps are described in the AWS Neuron Documentation. The format is usually torch.ops.

APIs 86

APIs Engineering Scripts Benchmark 86

Hodusoft

SEPTEMBER 18, 2024

Abandonment Rates A recent survey reported average abandonment rates between five percent and eight percent, with the benchmark for healthcare being at nearly seven percent. First Contact Resolution Rate The healthcare industry benchmark for first contact resolution ( FCR ) rate in healthcare is 71 percent.

Call Center 52

Call Center Contact center software Healthcare Contact Center 52

AWS Machine Learning

MARCH 13, 2024

Now, let’s look at latency and throughput performance benchmarking for model serving with the default JumpStart deployment configuration. For more information on how to consider this information and adjust deployment configurations for your specific use case, see Benchmark and optimize endpoint deployment in Amazon SageMaker JumpStart.

Benchmark 131

Benchmark Scripts APIs Feedback 131