AWS Machine Learning

MARCH 27, 2025

These include metrics such as ROUGE or cosine similarity for text similarity, and specific benchmarks for assessing toxicity (Detoxify), prompt stereotyping (cross-entropy loss), or factual knowledge (HELM, LAMA). Refer to Getting started with the API to set up your environment to make Amazon Bedrock requests through the AWS API.

Education 111

Education Engineering APIs Enterprise 111

AWS Machine Learning

AUGUST 18, 2022

The team’s early benchmarking results show 7.3 The baseline model used in these benchmarking is a multi-layer perceptron neural network with seven dense fully connected layers and over 200 parameters. The following table summarizes the benchmarking result on ml.p3.16xlarge SageMaker training instances. Number of Instances.

Scripts 104

Scripts APIs Benchmark Engineering 104

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

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. Flip the script With testingRTC, you only need to write scripts once, you can then run them multiple times and scale them up or down as you see fit. Happy days!

Scripts 98

Scripts APIs Metrics Analytics 98

AWS Machine Learning

FEBRUARY 6, 2023

The former question addresses model selection across model architectures, while the latter question concerns benchmarking trained models against a test dataset. This post provides details on how to implement large-scale Amazon SageMaker benchmarking and model selection tasks. swin-large-patch4-window7-224 195.4M efficientnet-b5 29.0M

APIs 95

APIs Scripts Metrics Benchmark 95

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. Note that the model container also includes any custom inference code or scripts that you have passed for inference. Any issues related to end-to-end latency can then be isolated separately.

Best practices 97

Best practices Scripts APIs Metrics 97

AWS Machine Learning

MAY 30, 2025

For this step, we use Amazon Bedrock, which provides a serverless API-driven endpoint for inference. Sonnet as a model with leading benchmarks in deep reasoning and mathematics to generate our new floor plan. After retrieving the most relevant examples for the users prompt, in step four we use Amazon Bedrock and Anthropics Claude 3.5

Engineering 69

Engineering Construction Metrics Entertainment 69

AWS Machine Learning

APRIL 12, 2023

The code to invoke the pipeline script is available in the Studio notebooks, and we can change the hyperparameters and input/output when invoking the pipeline. This is quite different from our earlier method where we had all the parameters hard coded within the scripts and all the processes were inextricably linked.

Scripts 108

Scripts APIs Metrics Best practices 108

AWS Machine Learning

OCTOBER 31, 2022

To get started, follow Modify a PyTorch Training Script to adapt SMPs’ APIs in your training script. In this section, we only call out a few main steps with code snippets from the ready-to-use training script train_gpt_simple.py. The notebook uses the script data_prep_512.py Benchmarking performance.

Scripts 88

Scripts Benchmark APIs Engineering 88

AWS Machine Learning

JULY 26, 2023

We compile the UNet for one batch (by using input tensors with one batch), then use the torch_neuronx.DataParallel API to load this single batch model onto each core. Load the UNet model onto two Neuron cores using the torch_neuronx.DataParallel API. If you have a custom inference script, you need to provide that instead.

Scripts 98

Scripts APIs Benchmark Engineering 98

AWS Machine Learning

APRIL 17, 2025

API Builder API Builder creates virtualized APIs for early frontend testing by using various LLMs from Amazon Bedrock. These models interpret API specifications and generate accurate mock responses, facilitating effective testing before full backend implementation. These test cases are refined by Anthropics Claude 3.5

APIs 64

APIs Engineering Benchmark Scripts 64

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. For more information, refer to Custom Operators API Reference Guide [Experimental].

APIs 96

APIs Engineering Scripts Benchmark 96

AWS Machine Learning

MARCH 20, 2023

Refer to the appendix for instance details and benchmark data. Use the supplied Python scripts for quantization. Run the provided Python test scripts to invoke the SageMaker endpoint for both INT8 and FP32 versions. Quantizing the model in PyTorch is possible with a few APIs from Intel PyTorch extensions.

Calibration 97

Calibration Scripts Benchmark APIs 97

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. A ready-to-use training script for GPT-2 model can be found at train_gpt_simple.py. For training a different model type, you can follow the API document to learn about how to apply SMP APIs.

Benchmark 93

Benchmark Engineering APIs Scripts 93

AWS Machine Learning

OCTOBER 27, 2022

Our benchmarks show up to 46% price performance benefit after enabling heterogeneous clusters in a CPU-bound TensorFlow computer vision model training. Performance benchmark results. For more information, refer to Using the SageMaker Python SDK and Using the Low-Level SageMaker APIs. Heterogeneous clusters at Mobileye.

Scripts 93

Scripts Benchmark Metrics Transportation 93

AWS Machine Learning

JUNE 6, 2023

The following figure shows a performance benchmark of fine-tuning a RoBERTa model on Amazon EC2 p4d.24xlarge inference with AWS Graviton processors for details on AWS Graviton-based instance inference performance benchmarks for PyTorch 2.0. Run your DLC container with a model training script to fine-tune the RoBERTa model.

Scripts 93

Scripts APIs Benchmark Management 93

AWS Machine Learning

SEPTEMBER 26, 2024

This text-to-video API generates high-quality, realistic videos quickly from text and images. Customizable environment – SageMaker HyperPod offers the flexibility to customize your cluster environment using lifecycle scripts. Video generation has become the latest frontier in AI research, following the success of text-to-image models.

Scripts 121

Scripts APIs Metrics Management 121

AWS Machine Learning

MAY 22, 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, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

Scripts 136

Scripts Engineering Accountability Benchmark 136

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. In this post, we walk you through the benchmarking process and the results we obtained while working on subsampled datasets.

Benchmark 91

Benchmark APIs Metrics Scripts 91

AWS Machine Learning

NOVEMBER 17, 2022

In this post, we provide an overview of how to deploy and run inference with the AlexaTM 20B model programmatically through JumpStart APIs, available in the SageMaker Python SDK. To use a large language model in SageMaker, you need an inferencing script specific for the model, which includes steps like model loading, parallelization and more.

Scripts 101

Scripts APIs Government Engineering 101

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 129

Enterprise APIs Benchmark Scripts 129

AWS Machine Learning

FEBRUARY 16, 2023

Briefly, this is made possible by an installation script specified by CustomActions in the YAML file used for creating the ParallelCluster (see Create ParallelCluster ). You can invoke neuron-top during the training script run to inspect NeuronCore utilization at each node. Complete instructions can be found on GitHub.

Scripts 101

Scripts Benchmark APIs Engineering 101

AWS Machine Learning

JANUARY 19, 2024

We use the Recognizing Textual Entailment dataset from the GLUE benchmarking suite. Phrase 2: A bearded man pulls a rope We load the textual recognizing entailment dataset from the GLUE benchmarking suite via the dataset library from Hugging Face within our training script (./training.py training.py ).

Metrics 130

Metrics Scripts Benchmark Enterprise 130

AWS Machine Learning

OCTOBER 24, 2022

DL scripts often require boilerplate code, notably the aforementioned double for loop structure that splits the dataset into minibatches and the training into epochs. At the time of this writing, it supports PyTorch and includes 25 techniques—called methods in the MosaicML world—along with standard models, datasets, and benchmarks.

Scripts 97

Scripts Enterprise Benchmark APIs 97

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 Benchmark 99

AWS Machine Learning

MAY 8, 2023

To use TensorRT as a backend for Triton Inference Server, you need to create a TensorRT engine from your trained model using the TensorRT API. The trtexec tool has three main purposes: Benchmarking networks on random or user-provided input data. script from the following cell. For this post, we use the trtexec CLI tool.

Engineering 101

Engineering APIs Best practices Scripts 101

AWS Machine Learning

JUNE 29, 2022

Syne Tune allows us to find a better hyperparameter configuration that achieves a relative improvement between 1-4% compared to default hyperparameters on popular GLUE benchmark datasets. training script. On a text classification problem, this leads to an additional boost in accuracy of approximately 5% compared to the default model.

Benchmark 100

Benchmark Metrics APIs Scripts 100

AWS Machine Learning

JULY 15, 2024

Welocalize benchmarks the performance of using LLMs and machine translations and recommends using LLMs as a post-editing tool. We use the custom terminology dictionary to compile frequently used terms within video transcription scripts. Amazon Translate has various unique benefits. Here’s an example.

Engineering 125

Engineering Entertainment Big data Benchmark 125

AWS Machine Learning

SEPTEMBER 20, 2022

Even if you already have a pre-trained model, it may still be easier to use its corollary in SageMaker and input the hyperparameters you already know rather than port it over and write a training script yourself. The training and inference scripts for the selected model or algorithm. Benchmarking the trained models.

Scripts 93

Scripts Metrics Telecommunications Benchmark 93

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 135

Best practices Engineering Chatbots Enterprise 135

Pointillist

JULY 25, 2018

Pointillist can handle data in all forms, whether it is in tables, excel files, server logs, or 3rd party APIs. During onboarding, the data will remain on your Pointillist-hosted SFTP server until the customer success team has created and quality-checked the requisite ingestion script. Getting Data into Pointillist.

Analytics 93

Analytics Government Metrics Upselling 93

AWS Machine Learning

MAY 31, 2023

In 2018, BERT-large made its debut with its 340 million parameters and innovative transformer architecture, setting the benchmark for performance on NLP tasks. The endpoint comes pre-loaded with the model and ready to serve queries via an easy-to-use API and Python SDK, so you can hit the ground running.

Scripts 94

Scripts Engineering Accountability Benchmark 94

Enghouse Interactive

OCTOBER 21, 2020

As noted in the 2019 Dimension Data Customer Experience (CX) Benchmarking report: 88% of contact center decision-makers expect self-service volumes to increase over the next 12 months. These interactions will become longer – so traditional productivity measurements and benchmarks will no longer be relevant and will have to be redefined.

Self service 79

Self service Contact Center Benchmark Chatbots 79

AWS Machine Learning

DECEMBER 6, 2022

The Trainer class provides an API for feature-complete training in PyTorch. We observe that the adversarial trained model has a lower ASR, with an 62.21% decrease using the original model ASR as the benchmark. For more information about this up-and-coming topic, we encourage you to explore and test our script on your own.

Engineering 96

Engineering APIs Construction Benchmark 96

AWS Machine Learning

SEPTEMBER 1, 2023

In this scenario, the generative AI application, designed by the consumer, must interact with the fine-tuner backend via APIs to deliver this functionality to the end-users. Some models may be trained on diverse text datasets like internet data, coding scripts, instructions, or human feedback. 15K available FM reference Step 1.

Engineering 128

Engineering Accountability Construction APIs 128

JustCall

FEBRUARY 13, 2023

Provides additional features like calendar management and benchmarking. month for up to 40k emails WhatsApp Business API – Starting at $0.0042 per WhatsApp Template message and $0.005 for session messages SMS, WhatsApp, Chat, and MMS – Starting at $0.05 Call monitoring and scripting are possible with 8×8.

Interactive Voice Response 52

Interactive Voice Response voip Enterprise call center software 52

JustCall

FEBRUARY 28, 2024

And when you think about the range of features the latter offers at $49 per user per month — all 3 dialers, bulk SMS campaigns and workflows, live call monitoring , advanced analytics and reporting, API and webhooks, live call monitoring, and so much more, it is simply astounding. How are JustCall’s Sales Dialer and Mojo Dialer different?

Real estate 52

Real estate CRM APIs Sales 52