AWS Machine Learning

MAY 2, 2024

As new embedding models are released with incremental quality improvements, organizations must weigh the potential benefits against the associated costs of upgrading, considering factors like computational resources, data reprocessing, integration efforts, and projected performance gains impacting business metrics.

Benchmark 130

Benchmark Metrics Enterprise APIs 130

AWS Machine Learning

JULY 2, 2024

You can see that for the 45 models we benchmarked, there is a 1.35x latency improvement (geomean for the 45 models). You can see that for the 33 models we benchmarked, there is around 2x performance improvement (geomean for the 33 models). We benchmarked 45 models using the scripts from the TorchBench repo.

Benchmark 123

Benchmark Scripts Metrics APIs 123

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 74

APIs Benchmark Metrics Healthcare 74

AWS Machine Learning

FEBRUARY 8, 2023

Although you can integrate the model directly into an application, the approach that works well for production-grade applications is to deploy the model behind an endpoint and then invoke the endpoint via a RESTful API call to obtain the inference. However, you can use any other benchmarking tool.

Benchmark 104

Benchmark Metrics APIs Engineering 104

ChurnZero

MARCH 18, 2022

With so many SaaS metrics floating around, and even more opinions on when and how to use them, it can be hard to know if you’re measuring what really matters. Leading SaaS expert, Dave Kellogg, and ChurnZero CEO, You Mon Tsang, sat down to answer all the questions you want to know about SaaS metrics like ARR, NRR, GRR, LTV, and CAC (i.e.,

SaaS 98

SaaS Metrics Surveys Enterprise 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 97

APIs Scripts Metrics Benchmark 97

AWS Machine Learning

APRIL 20, 2023

Each machine learning (ML) system has a unique service level agreement (SLA) requirement with respect to latency, throughput, and cost metrics. 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.

APIs 101

APIs Metrics Benchmark Engineering 101

AWS Machine Learning

NOVEMBER 22, 2023

You can save time, money, and labor by implementing classifications in your workflow, and documents go to downstream applications and APIs based on document type. This helps you avoid throttling limits on API calls due to polling the Get* APIs. Model monitoring The performance of ML models is monitored for degradation over time.

APIs 119

APIs Metrics Benchmark Enterprise 119

AWS Machine Learning

JANUARY 10, 2023

From there, we dive into how you can track and understand the metrics and performance of the SageMaker endpoint utilizing Amazon CloudWatch metrics. We first benchmark the performance of our model on a single instance to identify the TPS it can handle per our acceptable latency requirements. Metrics to track.

Best practices 100

Best practices Scripts APIs Metrics 100

Spearline

JULY 21, 2022

Consequently, no other testing solution can provide the range and depth of testing metrics and analytics. And testingRTC offers multiple ways to export these metrics, from direct collection from webhooks, to downloading results in CSV format using the REST API. Happy days! You can check framerate information for video here too.

Scripts 98

Scripts APIs Metrics Analytics 98

AWS Machine Learning

JUNE 20, 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.

Metrics 138

Metrics Engineering Accountability Benchmark 138

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 95

Benchmark APIs Metrics Scripts 95

AWS Machine Learning

JUNE 13, 2023

In addition, all SageMaker real-time endpoints benefit from built-in capabilities to manage and monitor models, such as including shadow variants , auto scaling , and native integration with Amazon CloudWatch (for more information, refer to CloudWatch Metrics for Multi-Model Endpoint Deployments ).

APIs 95

APIs Benchmark Engineering Management 95

AWS Machine Learning

APRIL 12, 2023

All the training and evaluation metrics were inspected manually from Amazon Simple Storage Service (Amazon S3). For every epoch in our training, we were already sending our training metrics through stdOut in the script. This allows us to compare training metrics like accuracy and precision across multiple runs as shown below.

Scripts 109

Scripts APIs Metrics Best practices 109

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 100

Best practices APIs Metrics Accountability 100

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. These metrics will assess how well a machine-generated summary compares to one or more reference summaries.

Healthcare 137

Healthcare Engineering APIs Benchmark 137

AWS Machine Learning

APRIL 22, 2024

Examples of tools you can use to advance sustainability initiatives are: Amazon Bedrock – a fully managed service that provides access to high-performing FMs from leading AI companies through a single API, enabling you to choose the right model for your sustainability use cases.

Best practices 140

Best practices Benchmark Transportation Engineering 140

AWS Machine Learning

JANUARY 19, 2024

The goal of NAS is to find the optimal architecture for a given problem by searching over a large set of candidate architectures using techniques such as gradient-free optimization or by optimizing the desired metrics. The performance of the architecture is typically measured using metrics such as validation loss. training.py ).

Metrics 132

Metrics Scripts Benchmark Enterprise 132

AWS Machine Learning

NOVEMBER 1, 2024

We also provide insights on how to achieve optimal results for different dataset sizes and use cases, backed by experimental data and performance metrics. Tools and APIs – For example, when you need to teach Anthropic’s Claude 3 Haiku how to use your APIs well. We focus on the task of answering questions about the table.

Best practices 99

Best practices APIs Finance Metrics 99

Pointillist

JULY 25, 2018

Success Metrics for the Team. Ultimately, the biggest success metric for the Champion is to be able to show the Executive Sponsor and key Stakeholders that real business value has been gained through the use of customer journey analytics. Success Metrics for the Project. Success Metrics for the Business. Churn Rate.

Analytics 93

Analytics Government Metrics APIs 93

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. Furthermore, we add another callback function to Hugging Face’s Trainer API that reports the validation performance after each epoch back to Syne Tune.

Benchmark 101

Benchmark Metrics APIs Scripts 101

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 130

Sales Accountability Feedback Metrics 130

AWS Machine Learning

NOVEMBER 22, 2023

Define goals and metrics – The function needs to deliver value to the organization in different ways. Establish regular cadence – The group should come together regularly to review their goals and metrics. This allows the workload to be implemented to achieve the desired goals of the organization.

Finance 114

Finance Best practices APIs Accountability 114

AWS Machine Learning

JUNE 5, 2023

Desired target metrics, improvement monitoring, and convergence detection monitors the performance of the model and assists with early stopping if the models don’t improve after a defined number of training jobs. Autotune uses best practices as well as internal benchmarks for selecting the appropriate ranges.

APIs 98

APIs Enterprise Benchmark Metrics 98

Comm100

SEPTEMBER 23, 2021

Ensure you find benchmarks and determine prompt response times for your business for the asynchronous communication channels like Facebook, SMS, and email. You can make your customers feel special through controlled APIs, customer segmentation, and user metrics mapping. Personalize Customer Experience.

Customer Service 98

Customer Service Chatbots APIs Benchmark 98

AWS Machine Learning

MAY 9, 2023

In addition, you can now use Application Auto Scaling with provisioned concurrency to address inference traffic dynamically based on target metrics or a schedule. To specify the metrics and target values for a scaling policy, you can configure a target-tracking scaling policy. Define the scaling policy as a JSON block in a text file.

Metrics 96

Metrics Best practices APIs Engineering 96

ChurnZero

MARCH 18, 2022

With so many SaaS metrics floating around, and even more opinions on when and how to use them, it can be hard to know if youre measuring what really matters. all the metrics your CEO and CFO care about) and set the context for their usage. all the metrics your CEO and CFO care about) and set the context for their usage.

SaaS 52

SaaS Metrics Surveys Enterprise 52

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. The quick way to identify a CPU bottleneck is to monitor CPU and GPU utilization metrics for SageMaker training jobs in Amazon CloudWatch.

Scripts 96

Scripts Benchmark Metrics Transportation 96

AWS Machine Learning

MARCH 20, 2023

Refer to the appendix for instance details and benchmark data. Quantizing the model in PyTorch is possible with a few APIs from Intel PyTorch extensions. Benchmark data The following table compares the cost and relative performance between c5 and c6 instances. times greater with INT8 quantization. Refer to invoke-INT8.py

Calibration 98

Calibration Scripts Benchmark APIs 98

AWS Machine Learning

JULY 24, 2023

as_trt_engine(output_fpath=trt_path, profiles=profiles) gpt2_trt = GPT2TRTDecoder(gpt2_engine, metadata, config, max_sequence_length=42, batch_size=10) Latency comparison: PyTorch vs. TensorRT JMeter is used for performance benchmarking in this project. implement the model and the inference API. model_fp16.onnx gpt2 and predictor.py

APIs 95

APIs Engineering Construction Benchmark 95

CallSource Insights

OCTOBER 3, 2018

If your website has a lot of indexed public facing pages, then this metric might matter a lot more. Site load can vary from pages loading, resources downloading, videos buffering, API calls or requests being made, etc. Audience > Benchmarking > Devices. Audience > Benchmarking > Channels.

Analytics 70

Analytics Benchmark APIs Metrics 70

AWS Machine Learning

JUNE 9, 2023

Furthermore, we benchmark the ResNet50 model and see the performance benefits that ONNX provides when compared to PyTorch and TensorRT versions of the same model, using the same input. The testing benchmark results are as follows: PyTorch – 176 milliseconds, cold start 6 seconds TensorRT – 174 milliseconds, cold start 4.5 seconds to 1.61

Benchmark 123

Benchmark Engineering Enterprise APIs 123

AWS Machine Learning

APRIL 8, 2024

In this post, we explore the latest features introduced in this release, examine performance benchmarks, and provide a detailed guide on deploying new LLMs with LMI DLCs at high performance. Before introducing this API, the KV cache was recomputed for any newly added requests.

Engineering 117

Engineering Calibration APIs Enterprise 117

AWS Machine Learning

OCTOBER 6, 2022

Training and validation loss is just one of the metrics needed to pick the best model for the use case. We trained this model from scratch with the default hyperparameters, so we could have a benchmark to evaluate the rest of the models. Automatic model tuning will monitor the log and parse the objective metrics.

Metrics 98

Metrics APIs Benchmark Accountability 98

AWS Machine Learning

SEPTEMBER 20, 2022

As is explained in the post Amazon SageMaker JumpStart models and algorithms now available via API , the following artifacts are required to train a pre-built algorithm via the SageMaker SDK: Its framework-specific container image, containing all the required dependencies for training and inference. . Benchmarking the trained models.

Scripts 96

Scripts Metrics Telecommunications APIs 96

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. This dataset has been widely used as a topic modeling benchmark. They offer a technical solution that you can further customize based on your own needs.

APIs 121

APIs Benchmark Advertising Banking 121

AWS Machine Learning

JANUARY 20, 2023

After cycles of research and initial benchmarking efforts, CCC determined SageMaker was a perfect fit to meet a majority of their production requirements, especially the guaranteed uptime SageMaker provides for most of its inference components. Step-by-step solution Step 1 A client makes a request to the AWS API Gateway endpoint.

APIs 93

APIs Engineering Telecommunications Construction 93