AWS Machine Learning

JANUARY 29, 2024

This post explores these relationships via a comprehensive benchmarking of LLMs available in Amazon SageMaker JumpStart, including Llama 2, Falcon, and Mistral variants. We provide theoretical principles on how accelerator specifications impact LLM benchmarking. Additionally, models are fully sharded on the supported instance.

Benchmark 129

Benchmark APIs Enterprise Accountability 129

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 95

APIs Enterprise Benchmark Feedback 95

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 108

APIs Enterprise Best practices Government 108

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. This integration provides a powerful multilingual model that excels in reasoning benchmarks. Mixtral 8x7B Instruct v0.1:

Enterprise 112

Enterprise Benchmark Technology APIs 112

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 119

Metrics Enterprise APIs Engineering 119

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 77

APIs Management Benchmark Scripts 77

AWS Machine Learning

AUGUST 18, 2022

Amazon SageMaker is a fully-managed service for ML, and SageMaker model training is an optimized compute environment for high-performance training at scale. SageMaker model training offers a remote training experience with a seamless control plane to easily train and reproduce ML models at high performance and low cost.

Scripts 106

Scripts APIs Benchmark Engineering 106

AWS Machine Learning

OCTOBER 31, 2022

Training these gigantic models is challenging and requires complex distribution strategies. Data scientists and machine learning engineers are constantly looking for the best way to optimize their training compute, yet are struggling with the communication overhead that can increase along with the overall cluster size. on 256 GPUs.

Scripts 94

Scripts Benchmark APIs Engineering 94

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 120

Scripts APIs Metrics Management 120

AWS Machine Learning

JUNE 29, 2022

However, training these gigantic networks from scratch requires a tremendous amount of data and compute. For smaller NLP datasets, a simple yet effective strategy is to use a pre-trained transformer, usually trained in an unsupervised fashion on very large datasets, and fine-tune it on the dataset of interest. training script.

Benchmark 101

Benchmark Metrics APIs Scripts 101

AWS Machine Learning

JULY 29, 2024

You can also either use the SageMaker Canvas UI, which provides a visual interface for building and deploying models without needing to write any code or have any ML expertise, or use its automated machine learning (AutoML) APIs for programmatic interactions.

APIs 119

APIs Scripts Benchmark Metrics 119

AWS Machine Learning

OCTOBER 24, 2022

The plentiful and jointly trained parameters of DL models have a large representational capacity that brought improvements in numerous customer use cases, including image and speech analysis, natural language processing (NLP), time series processing, and more. The challenge with DL training.

Scripts 98

Scripts Enterprise Benchmark APIs 98

AWS Machine Learning

FEBRUARY 16, 2023

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. At the server level, such training workloads demand faster compute and increased memory allocation. As models grow to hundreds of billions of parameters, they require a distributed training mechanism that spans multiple nodes (instances).

Scripts 101

Scripts Benchmark APIs Engineering 101

AWS Machine Learning

OCTOBER 27, 2022

Certain machine learning (ML) workloads, such as training computer vision models or reinforcement learning, often involve combining the GPU- or accelerator-intensive task of neural network model training with the CPU-intensive task of data preprocessing, like image augmentation. Performance benchmark results.

Scripts 97

Scripts Benchmark Metrics Transportation 97

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

OCTOBER 18, 2023

The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring. It is already trained on tens of millions of images across many categories. API Gateway calls the Lambda function to obtain the pet attributes.

APIs 128

APIs Metrics Consulting Consulting 128

AWS Machine Learning

MAY 2, 2024

A common way to select an embedding model (or any model) is to look at public benchmarks; an accepted benchmark for measuring embedding quality is the MTEB leaderboard. The Massive Text Embedding Benchmark (MTEB) evaluates text embedding models across a wide range of tasks and datasets. on reranking tasks, for example.

Benchmark 129

Benchmark Metrics Enterprise APIs 129

AWS Machine Learning

MARCH 3, 2025

They enable applications requiring very low latency or local data processing using familiar APIs and tool sets. Through comparative benchmarking tests, we illustrate how deploying FMs in Local Zones closer to end users can significantly reduce latencya critical factor for real-time applications such as conversational AI assistants.

APIs 84

APIs Benchmark Metrics Healthcare 84

AWS Machine Learning

NOVEMBER 30, 2023

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at scale. SageMaker makes it easy to deploy models into production directly through API calls to the service. SageMaker provides a variety of options to deploy models.

Benchmark 129

Benchmark APIs Scripts Engineering 129

AWS Machine Learning

SEPTEMBER 6, 2023

It’s powered by large language models (LLMs) that are pre-trained on vast amounts of data and commonly referred to as foundation models (FMs). These SageMaker endpoints are consumed in the Amplify React application through Amazon API Gateway and AWS Lambda functions. You access the React application from your computer.

Enterprise 129

Enterprise APIs Real estate Construction 129

AWS Machine Learning

SEPTEMBER 18, 2024

Together, these AI-driven tools and technologies aren’t just reshaping how brands perform marketing tasks; they’re setting new benchmarks for what’s possible in customer engagement. It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines.

APIs 122

APIs Engineering Analytics Marketing 122

AWS Machine Learning

SEPTEMBER 11, 2024

As shown in the preceding figure, the ML paradigm is learning (training) followed by inference. 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.

Benchmark 114

Benchmark Banking Analytics Big data 114

AWS Machine Learning

JULY 10, 2024

To date, we have developed over 70 internal and external offerings, tools, and mechanisms that support responsible AI, published or funded over 500 research papers, studies, and scientific blogs on responsible AI, and delivered tens of thousands of hours of responsible AI training to our Amazon employees.

Government 137

Government Education Best practices Benchmark 137

AWS Machine Learning

FEBRUARY 6, 2023

And an ML researcher may ask questions like: “How can I generate my own fair comparison of multiple model architectures against a specified dataset while controlling training hyperparameters and computer specifications, such as GPUs, CPUs, and RAM?” swin-large-patch4-window7-224 195.4M efficientnet-v2-imagenet21k-ft1k-l 118.1M

APIs 98

APIs Scripts Metrics Benchmark 98

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 103

APIs Benchmark Best practices Engineering 103

AWS Machine Learning

JUNE 6, 2024

RAG is the process of optimizing the output of a large language model (LLM) so it references an authoritative knowledge base outside of its training data sources before generating a response. What is RAG? Long input-context length – Jina Embeddings v2 models support 8,192 input tokens.

Benchmark 120

Benchmark Enterprise Construction APIs 120

Cincom

FEBRUARY 14, 2025

Use APIs and middleware to bridge gaps between CPQ and existing enterprise systems, ensuring smooth data flow. Automate Price Calculations and Adjustments Utilize real-time pricing engines within CPQ to dynamically calculate prices based on market trends, cost fluctuations, and competitor benchmarks.

Best practices 40

Best practices Sales CRM Finance 40

AWS Machine Learning

FEBRUARY 8, 2023

Building ML models involves preparing the data for training, extracting features, and then training and fine-tuning the model using the features. The procedure is further simplified with the use of Inference Recommender , a right-sizing and benchmarking tool built inside SageMaker. large two-core machine.

Benchmark 104

Benchmark Metrics APIs Engineering 104

AWS Machine Learning

JUNE 21, 2024

A foundation model (FM) is an LLM that has undergone unsupervised pre-training on a corpus of text. A foundation model (FM) is an LLM that has undergone unsupervised pre-training on a corpus of text. This further step updates the FM by training with data labeled by security experts (such as Q&A pairs and investigation conclusions).

Engineering 127

Engineering Construction Benchmark Advertising 127

AWS Machine Learning

AUGUST 2, 2024

An approach to product stewardship with generative AI Large language models (LLMs) are trained with vast amounts of information crawled from the internet, capturing considerable knowledge from multiple domains. However, their knowledge is static and tied to the data used during the pre-training phase.

APIs 128

APIs Analytics Chatbots Engineering 128

AWS Machine Learning

MARCH 19, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models from leading AI companies and Amazon via a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. The GSM8K train set comprises 7,473 records.

APIs 135

APIs Benchmark SaaS Engineering 135

AWS Machine Learning

DECEMBER 16, 2022

Foundation models are large deep learning models trained on a vast quantity of data at scale. The most prominent category is large-language models (LLM), including auto-regressive models such as GPT variants trained to complete natural text. FlashAttention is introduced in Dao et al. 24xlarge instances.

Benchmark 97

Benchmark Engineering APIs Scripts 97

AWS Machine Learning

AUGUST 3, 2022

For example, you can immediately start detecting entities such as people, places, commercial items, dates, and quantities via the Amazon Comprehend console , AWS Command Line Interface , or Amazon Comprehend APIs. Amazon Comprehend simplifies your model training work significantly. Dataset preparation.

Benchmark 96

Benchmark APIs Metrics Scripts 96

AWS Machine Learning

JUNE 13, 2023

Because the hyper-personalization of models requires unique models to be trained and deployed, the number of models scales linearly with the number of clients, which can become costly. In addition, deployments are now as simple as calling Boto3 SageMaker APIs and attaching the proper auto scaling policies.

APIs 96

APIs Benchmark Engineering Management 96

AWS Machine Learning

APRIL 20, 2023

We train an XGBoost model for a classification task on a credit card fraud dataset. An advanced job is a custom load test job that allows you to perform extensive benchmarks based on your ML application SLA requirements, such as latency, concurrency, and traffic pattern. Deploy the trained model to a SageMaker real-time endpoint.

APIs 101

APIs Metrics Benchmark Engineering 101

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. When summarizing healthcare texts, pre-trained LLMs do not always achieve optimal performance.

Healthcare 136

Healthcare Engineering APIs Benchmark 136

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.

Enterprise 130

Enterprise APIs Benchmark Scripts 130