AWS Machine Learning

NOVEMBER 16, 2023

With SageMaker MLOps tools, teams can easily train, test, troubleshoot, deploy, and govern ML models at scale to boost productivity of data scientists and ML engineers while maintaining model performance in production. Enable a data science team to manage a family of classic ML models for benchmarking statistics across multiple medical units.

Healthcare 93

Healthcare Government Engineering APIs 93

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 70

Benchmark APIs Metrics Scripts 70

AWS Machine Learning

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

Engineering 107

Engineering Accountability Construction APIs 107

AWS Machine Learning

JUNE 5, 2023

Autotune uses best practices as well as internal benchmarks for selecting the appropriate ranges. Iaroslav Shcherbatyi is a Machine Learning Engineer at AWS. Autotune is a new feature of automatic model tuning that helps save you time and reduce wasted resources on finding optimal hyperparameter ranges.

APIs 80

APIs Enterprise Benchmark Metrics 80

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.

Calibration 82

Calibration Scripts Benchmark APIs 82

AWS Machine Learning

OCTOBER 5, 2022

Based on 10 years of historical data, hundreds of thousands of face-offs were used to engineer over 70 features fed into the model to provide real-time probabilities. By continuously listening to NHL’s expertise and testing hypotheses, AWS’s scientists engineered over 100 features that correlate to the face-off event.

Calibration 94

Calibration Engineering Automotive Analytics 94

AWS Machine Learning

DECEMBER 6, 2022

In real-world toxicity detection applications, toxicity filtering is mostly used in security-relevant industries like gaming platforms, where models are constantly being challenged by social engineering and adversarial attacks. Social engineers can use this type of characteristic of NLP models to bypass toxicity filtering systems.

Engineering 77

Engineering APIs Construction Metrics 77

AWS Machine Learning

NOVEMBER 30, 2022

For a complete description of Jurassic-1, including benchmarks and quantitative comparisons with other models, refer to the following technical paper. J1 can be applied to virtually any language task by crafting a suitable prompt containing a description of the task and a few examples, a process commonly known as prompt engineering.

APIs 75

APIs Benchmark Chatbots Accountability 75

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. We also use Vector Engine for Amazon OpenSearch Serverless (currently in preview) as the vector data store to store embeddings. Lewis et al.

Engineering 97

Engineering Chatbots APIs Benchmark 97

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. TensorRT-LLM requires models to be compiled into efficient engines before deployment. For more details, refer to the GitHub repo.

Engineering 89

Engineering Calibration APIs Enterprise 89

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 116

Best practices Benchmark Transportation Engineering 116

AWS Machine Learning

APRIL 20, 2023

Also, you can build these ML systems with a combination of ML models, tasks, frameworks, libraries, tools, and inference engines, making it important to evaluate the ML system performance for the best possible deployment configurations. Inference Recommender uses this information to run a performance benchmark load test.

APIs 82

APIs Metrics Benchmark Engineering 82

AWS Machine Learning

MARCH 14, 2024

We provide an overview of key generative AI approaches, including prompt engineering, Retrieval Augmented Generation (RAG), and model customization. Building large language models (LLMs) from scratch or customizing pre-trained models requires substantial compute resources, expert data scientists, and months of engineering work.

Best practices 110

Best practices Engineering Chatbots Enterprise 110

Whitepages Pro

JANUARY 25, 2016

From there, you’ll know how to benchmark the data sources you’re reviewing. Whether the data is delivered as an outdated API, a slow batch service, or flat file that only updates once every six months, the format in which it is delivered can have a big impact on your sourcing decision. These two qualities can sometimes be at odds (e.g.,

APIs 40

APIs Marketing Benchmark Engineering 40

AWS Machine Learning

APRIL 10, 2023

For benchmark performance figures, refer to AWS Neuron Performance. Each NeuronCore-v2 is an independent, heterogenous compute-unit, with four main engines: Tensor, Vector, Scalar, and GPSIMD engines. engine=Python because the handles are implement in python. This is particularly useful for large language models.

Engineering 75

Engineering APIs Benchmark Advertising 75

AWS Machine Learning

JUNE 13, 2023

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. Forethought had to manage model inference on Amazon EKS ourselves, which was a burden on engineering efficiency.

APIs 67

APIs Benchmark Engineering Management 67

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. Benchmarking performance. Finally, we benchmark SMP with both of the latest features enabled.

Benchmark 72

Benchmark Engineering APIs Scripts 72

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. large two-core machine.

Benchmark 79

Benchmark Metrics APIs Engineering 79

AWS Machine Learning

APRIL 27, 2023

Trainium support for custom operators Trainium (and AWS Inferentia2) supports CustomOps in software through the Neuron SDK and accelerates them in hardware using the GPSIMD engine (General Purpose Single Instruction Multiple Data engine). The scalar and vector engines are highly parallelized and optimized for floating-point operations.

APIs 76

APIs Engineering Scripts Benchmark 76

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 108

Benchmark APIs Scripts Engineering 108

AWS Machine Learning

SEPTEMBER 21, 2023

In particular, we provide practical best practices for different customization scenarios, including training models from scratch, fine-tuning with additional data using full or parameter-efficient techniques, Retrieval Augmented Generation (RAG), and prompt engineering.

Best practices 104

Best practices Engineering Metrics APIs 104

AWS Machine Learning

OCTOBER 31, 2022

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. To get started, follow Modify a PyTorch Training Script to adapt SMPs’ APIs in your training script.

Scripts 67

Scripts Benchmark APIs Engineering 67

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 111

Benchmark Scripts APIs Feedback 111

AWS Machine Learning

MARCH 15, 2024

Federated learning: An introduction Federated learning (FL) is a decentralized form of ML—a dynamic engineering approach. Furthermore, model hosting on Amazon SageMaker JumpStart can help by exposing the endpoint API without sharing model weights.

Healthcare 103

Healthcare Government Best practices Engineering 103

AWS Machine Learning

SEPTEMBER 13, 2022

To validate the drawbacks of this existing MVP architecture, Medidata performed some initial benchmarking to understand and prioritize the bottlenecks of this pipeline. Your model can then be consumed by client applications through a real-time invoke API request.

Engineering 74

Engineering APIs Benchmark Management 74

AWS Machine Learning

AUGUST 18, 2022

Machine learning (ML) experts, data scientists, engineers and enthusiasts have encountered this problem the world over. 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.

Scripts 80

Scripts APIs Benchmark Engineering 80

AWS Machine Learning

MAY 8, 2023

To serve models, Triton supports various backends as engines to support the running and serving of various ML models for inference. With kernel auto-tuning, the engine selects the best algorithm for the target GPU, maximizing hardware utilization. Import the ONNX model into TensorRT and generate the TensorRT engine.

Engineering 87

Engineering APIs Best practices Scripts 87

AWS Machine Learning

JUNE 13, 2023

GPSIMD-Engine – Has eight fully programmable 512-bit wide general-purpose processors for you to create your custom operators with standard PyTorch custom C++ operators API. A custom operator is composed of low-level primitives available in the Tensor Factory Functions and accelerated by GPSIMD-Engine. xlarge: $0.7582/hr; inf1.xlarge:

Benchmark 75

Benchmark APIs Engineering Technology 75

Netomi

SEPTEMBER 20, 2021

It has the highest accuracy of any customer service chatbot due to its advanced Natural Language Understanding (NLU) engine. CSML is the first open-source programming language and chatbot engine dedicated to developing powerful and interoperable chatbots. Self-service APIs to help you create, manage, test and publish custom skills.

Chatbots 126

Chatbots APIs Surveys Analytics 126

Fonolo

NOVEMBER 14, 2018

It’s important for all departments to have benchmarks for success that can be easily measured and tracked. Call center and customer service teams have a variety of KPIs to choose from, but as each company and support department is different, their benchmarks will vary. He leads product management for Nexmo, the Vonage API Platform.

Benchmark 85

Benchmark Abandon rate Call Center Abandon Call 85

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 93

Benchmark Engineering Enterprise APIs 93

AWS Machine Learning

JUNE 24, 2022

To overcome this, enterprises needs to shape a clear operating model defining how multiple personas, such as data scientists, data engineers, ML engineers, IT, and business stakeholders, should collaborate and interact; how to separate the concerns, responsibilities, and skills; and how to use AWS services optimally.

Enterprise 94

Enterprise Engineering Accountability APIs 94

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 71

Scripts Engineering Accountability APIs 71

AWS Machine Learning

MAY 9, 2023

You can then use that text file when invoking the AWS Command Line Interface (AWS CLI) or the Application Auto Scaling API. SageMaker endpoint configuration – Create an endpoint configuration using the CreateEndpointConfig API and the new configuration ServerlessConfig options or by selecting the serverless option on the SageMaker console.

Metrics 69

Metrics Best practices APIs Engineering 69

AWS Machine Learning

NOVEMBER 22, 2023

Split documents into single pages for specific FeatureType processing – FeatureType is a parameter for the Document Analysis API calls (both synchronous and asynchronous ) in Amazon Textract. With cost categories, you can organize your costs using a rule-based engine.

Finance 85

Finance Best practices APIs Accountability 85

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. Create the SageMaker endpoint We use Boto3 APIs to create a SageMaker endpoint.

Scripts 84

Scripts APIs Benchmark Engineering 84

AWS Machine Learning

JANUARY 19, 2024

The concepts illustrated in this post can be applied to applications that use PLM features, such as recommendation systems, sentiment analysis, and search engines. We use the Recognizing Textual Entailment dataset from the GLUE benchmarking suite. He specializes in Generative AI and Machine Learning Data Engineering.

Metrics 101

Metrics Scripts Benchmark Enterprise 101