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 114

Best practices Engineering Chatbots Enterprise 114

AWS Machine Learning

NOVEMBER 29, 2024

Prompt engineering refers to the practice of writing instructions to get the desired responses from foundation models (FMs). This manual effort required for prompt engineering can slow down your ability to test different models. Some example performance benchmarks for several tasks were conducted and are discussed.

Benchmark 112

Benchmark Engineering Construction APIs 112

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

NOVEMBER 18, 2024

Sonnet, also newly released, setting new industry benchmarks for graduate-level reasoning and improvements in grasping complex instructions. It exposes an API endpoint through Amazon API Gateway that proxies the initial prompt request to a Python-based AWS Lambda function, which calls Amazon Bedrock twice. Choose Next again.

APIs 92

APIs Entertainment Benchmark Advertising 92

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. in Mechanical Engineering from the University of Notre Dame. Max Goff is a data scientist/data engineer with over 30 years of software development experience. She received her Ph.D.

Engineering 104

Engineering Entertainment Big data APIs 104

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 119

Best practices Benchmark Transportation Engineering 119

AWS Machine Learning

JUNE 21, 2024

The application’s frontend is accessible through Amazon API Gateway , using both edge and private gateways. To emulate intricate thought processes akin to those of a human investigator, eSentire engineered a system of chained agent actions. He focuses on advancing cybersecurity with expertise in machine learning and data engineering.

Engineering 106

Engineering Construction APIs Benchmark 106

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 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 80

APIs Metrics Benchmark Engineering 80

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 68

APIs Benchmark Engineering Management 68

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. Deepti Ragha is a Software Development Engineer in the Amazon SageMaker team.

Engineering 98

Engineering APIs Benchmark Enterprise 98

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 73

APIs Engineering Scripts Benchmark 73

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. In this post, we present a solution that D2L.ai

APIs 77

APIs Benchmark Best practices Engineering 77

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 92

Engineering Calibration APIs Enterprise 92

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. Evaluator considerations By default, evaluators use the InvokeModel API with On-Demand mode, which will incur AWS charges based on input tokens processed and output tokens generated.

APIs 115

APIs Engineering Best practices Virtual Agent 115

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 99

Enterprise Engineering Accountability APIs 99

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. Each GPC has a raster engine for graphics and several TPCs. The CUDA API and SDK were first released by NVIDIA in 2007.

Benchmark 85

Benchmark Banking Analytics Big data 85

JivoChat

JUNE 20, 2022

Data and time API. Rest API Testing (Automation) from Scratch-Rest Assured Java. With the Rest API Testing (Automation) from Scratch-Rest Assured Java, you will learn how to design and implement structured API automation, and also generate excellent client reports for API test execution results. . JVM internals.

APIs 75

APIs Benchmark Best practices Construction 75

AWS Machine Learning

MAY 22, 2024

Customers can more easily locate products that have correct descriptions, because it allows the search engine to identify products that match not just the general category but also the specific attributes mentioned in the product description. Lun Yeh is a Machine Learning Engineer at AWS Professional Services.

Scripts 118

Scripts Engineering Accountability APIs 118

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.

APIs 101

APIs Metrics Benchmark Enterprise 101

AWS Machine Learning

SEPTEMBER 12, 2024

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. medium instance to demonstrate deploying the model as an API endpoint using an SDK through SageMaker JumpStart.

APIs 115

APIs Benchmark Enterprise Construction 115

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 69

Benchmark Engineering APIs Scripts 69

AWS Machine Learning

MAY 7, 2024

Rather than requiring extensive feature engineering and dataset labeling, LLMs can be fine-tuned on small amounts of domain-specific data to quickly adapt to new use cases. This post walks through examples of building information extraction use cases by combining LLMs with prompt engineering and frameworks such as LangChain.

Engineering 105

Engineering Technical Support Chatbots Best practices 105

AWS Machine Learning

NOVEMBER 26, 2024

We used prompt engineering guidelines to tailor our prompts to generate better responses from the LLM. We used the InvokeModel API to interact with the model with minimal latency. We wrote the following function that let us call different models by passing the necessary inference parameters to the API.

APIs 80

APIs Metrics SaaS Engineering 80

AWS Machine Learning

NOVEMBER 18, 2024

Amazon Bedrock is a fully managed service that provides a single API to access and use various high-performing foundation models (FMs) from leading AI companies. It supports exact and approximate nearest-neighbor algorithms and multiple storage and matching engines. Binary embeddings maintained 98.5% without reranking).

Benchmark 100

Benchmark APIs Engineering Analytics 100

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 73

Engineering APIs Benchmark Advertising 73

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 85

Engineering APIs Best practices Scripts 85

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 115

Benchmark Scripts APIs Feedback 115

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 112

Engineering Accountability Construction APIs 112

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. Fine-tuning Anthropic’s Claude 3 Haiku has demonstrated superior performance compared to few-shot prompt engineering on base Anthropic’s Claude 3 Haiku, Anthropic’s Claude 3 Sonnet, and Anthropic’s Claude 3.5

Best practices 83

Best practices APIs Finance Engineering 83

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 65

Scripts Benchmark APIs Engineering 65

AWS Machine Learning

JUNE 20, 2024

Additionally, evaluation can identify potential biases, hallucinations, inconsistencies, or factual errors that may arise from the integration of external sources or from sub-optimal prompt engineering. In this case, the model choice needs to be revisited or further prompt engineering needs to be done.

Metrics 117

Metrics Engineering Accountability Benchmark 117

AWS Machine Learning

NOVEMBER 18, 2024

Amazon Bedrock provided the flexibility to explore various leading LLM models using a single API, reducing the undifferentiated heavy lifting associated with hosting third-party models. Leveraging this, InsuranceDekho developed the industry’s first Health Pro Genie with the most efficient engine.

APIs 90

APIs Benchmark Engineering Sales 90

AWS Machine Learning

NOVEMBER 15, 2024

Use cases we have worked on include: Technical assistance for field engineers – We built a system that aggregates information about a company’s specific products and field expertise. A chatbot enables field engineers to quickly access relevant information, troubleshoot issues more effectively, and share knowledge across the organization.

APIs 104

APIs Engineering Chatbots Construction 104

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 80

Scripts APIs Benchmark Engineering 80

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 107

Enterprise APIs Benchmark Scripts 107

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 98

Healthcare Government Engineering APIs 98