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. He works mainly on improvements to the Amazon SageMaker platform and helping customers best use its features.

APIs 78

APIs Enterprise Benchmark Metrics 78

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 107

Best practices Engineering Chatbots Enterprise 107

AWS Machine Learning

APRIL 22, 2024

It provides examples of use cases and best practices for using generative AI’s potential to accelerate sustainability and ESG initiatives, as well as insights into the main operational challenges of generative AI for sustainability. Throughout this lifecycle, implementing AWS Well-Architected Framework best practices is recommended.

Best practices 114

Best practices Benchmark Transportation Engineering 114

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 102

Best practices Engineering Metrics APIs 102

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 100

Healthcare Government Best practices Engineering 100

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 77

Benchmark Metrics APIs Engineering 77

AWS Machine Learning

NOVEMBER 22, 2023

It also provides guidance to tackle common challenges, enabling you to architect your IDP workloads according to best practices. Focus areas The design principles and best practices of the Cost Optimization pillar are based on insights gathered from our customers and our IDP technical specialist communities.

Finance 83

Finance Best practices APIs Accountability 83

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

MAY 9, 2023

In this post, we discuss what provisioned concurrency and Application Auto Scaling are, how to use them, and some best practices and guidance for your inference workloads. You can then use that text file when invoking the AWS Command Line Interface (AWS CLI) or the Application Auto Scaling API.

Metrics 67

Metrics Best practices APIs Engineering 67

Xaqt

JUNE 22, 2020

Most cloud based Contact Center providers provide documented APIs to access your data, while with many legacy contact center vendors, it's still a challenge to get your data out of their reporting tools or platforms. It often requires additional licensing, or developing a custom application against their APIs to extract the data.

Call Center 52

Call Center Contact Center Analytics Engineering 52

Pointillist

JULY 25, 2018

Treating every change to customer data as an event saves work for data engineers, as no transformation is required. Pointillist can handle data in all forms, whether it is in tables, excel files, server logs, or 3rd party APIs. 3rd Party APIs: Pointillist has a large number of connectors using 3rd party APIs. Governance.

Analytics 93

Analytics Government APIs Metrics 93

Pointillist

JULY 25, 2018

Treating every change to customer data as an event saves work for data engineers, as no transformation is required. Pointillist can handle data in all forms, whether it is in tables, excel files, server logs, or 3rd party APIs. 3rd Party APIs: Pointillist has a large number of connectors using 3rd party APIs.

Analytics 43

Analytics Metrics Government Journey mapping 43

AWS Machine Learning

MARCH 19, 2024

Furthermore, the use of prompt engineering can notably enhance their performance. With the batch inference API, you can use Amazon Bedrock to run inference with foundation models in batches and get responses more efficiently. The service is in preview as of this writing and only available through the API.

APIs 105

APIs Benchmark SaaS Engineering 105

AWS Machine Learning

NOVEMBER 9, 2022

In this post, we discuss SageMaker multi-variant endpoints and best practices for optimization. Your application simply needs to include an API call with the target model to this endpoint to achieve low-latency, high-throughput inference. Best practices for multi-variant endpoints.

Best practices 52

Best practices Construction Metrics Enterprise 52