APIs, Best practices, Metrics and Service level

Build well-architected IDP solutions with a custom lens – Part 1: Operational excellence

AWS Machine Learning

NOVEMBER 22, 2023

By using the Framework, you will learn operational and architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable workloads in the cloud. Pursue Metrics-Driven Quality and Continuous Improvement In IDP, what gets measured gets improved.

Best practices

Best practices Metrics Feedback Enterprise

The Omnichannel Contact Center: A Guide for 2020 (with Actionable Tips)

Serenova

JUNE 24, 2020

Integration must happen at the time a channel is launched— not after—for optimal omnichannel customer service interactions. The most valuable contact center solutions are designed to fit into your ecosystem with pre-built integrations and also offer integrations using APIs. Constant Information Gathering Along the Customer Journey.

Contact Center

Contact Center Multichannel contact center solutions Cloud contact

The Omnichannel Contact Center: A Guide for 2020 (with Actionable Tips)

Serenova

JUNE 24, 2020

Integration must happen at the time a channel is launched— not after—for optimal omnichannel customer service interactions. The most valuable contact center solutions are designed to fit into your ecosystem with pre-built integrations and also offer integrations using APIs. Constant Information Gathering Along the Customer Journey.

Contact Center

Contact Center Multichannel contact center solutions Cloud contact

Webinars

Manual Call Reviews? There's a Better Way!

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Omnichannel Call Center: A Guide for 2020 (with Actionable Tips)

Serenova

JUNE 24, 2020

Integration must happen at the time a channel is launched— not after—for optimal omnichannel customer service interactions. The most valuable contact center solutions are designed to fit into your ecosystem with pre-built integrations and also offer integrations using APIs. Constant Information Gathering Along the Customer Journey.

Call Center

Call Center Multichannel Contact Center contact center solutions

Optimize generative AI workloads for environmental sustainability

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

Best practices Engineering Metrics APIs

Build well-architected IDP solutions with a custom lens – Part 5: Cost optimization

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

Finance Best practices APIs Accountability

Host ML models on Amazon SageMaker using Triton: CV model with PyTorch backend

AWS Machine Learning

MAY 31, 2023

Real-time workloads can have varying levels of performance expectations and service level agreements (SLAs), which materialize as latency and throughput requirements. Triton with PyTorch backend The PyTorch backend is designed to run TorchScript models using the PyTorch C++ API. This is the key differentiator.

APIs

APIs Scripts Engineering Accountability

The Top 9 Aircall Alternatives: How Do They Compare?

aircall

SEPTEMBER 12, 2022

As a result, you can monitor individual and team metrics in real-time so you can start making measurable improvements and boost productivity. 100+ Integrations and API access. Access to API developer support. Service-level agreement (SLA). Check out our developer portal for more info on our open API.

voip

voip APIs Interactive Voice Response Analytics

How to Improve Customer Service – A Complete Guide of Tools, Tech & Tips

Comm100

NOVEMBER 13, 2022

With this all in mind, this guide will explore customer service from top to bottom, exploring the state of customer service today, revealing best practices, and recommending the best software you can adopt to help your customer service operations become the envy of your competitors. Table of Contents.

Customer Service

Customer Service Chatbots Best practices Surveys

Host ML models on Amazon SageMaker using Triton: Python backend

AWS Machine Learning

MAY 9, 2023

Real-time inference workloads can have varying levels of requirements and service level agreements (SLAs) in terms of latency and throughput. Inference requests arrive at the server via either HTTP/REST or by the C API and are then routed to the appropriate per-model scheduler.

APIs

APIs Scripts Best practices Enterprise

Call Center Reporting - A New Paradigm

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

Call Center Contact Center Analytics Engineering

Hosting ML Models on Amazon SageMaker using Triton: XGBoost, LightGBM, and Treelite Models

AWS Machine Learning

MAY 2, 2023

Real-time inference workloads can have varying levels of requirements and service level agreements (SLAs) in terms of latency and throughput, and can be met using SageMaker real-time endpoints. In this post, we dive deep to see how Amazon SageMaker can serve these models using NVIDIA Triton Inference Server. astype(str).tolist()

Scripts

Scripts Engineering Enterprise Construction

Model hosting patterns in Amazon SageMaker, Part 1: Common design patterns for building ML applications on Amazon SageMaker

AWS Machine Learning

JANUARY 9, 2023

Strict service level agreements (SLAs) need to be met, and a typical request may require multiple steps such as preprocessing, data transformation, feature engineering, model selection logic, model aggregation, and postprocessing. You can do this by choosing a specific metric and setting threshold values. Inference latency.

Metrics

Metrics Best practices Engineering Management

Scale foundation model inference to hundreds of models with Amazon SageMaker – Part 1

AWS Machine Learning

NOVEMBER 30, 2023

In addition, customers are looking for solutions that help them deploy a best-practice inferencing architecture without needing to build everything from scratch. SageMaker Inference is a fully managed ML hosting service. It supports building generative AI applications while meeting regulatory standards like FedRAMP.

Chatbots

Chatbots SaaS Engineering APIs

Customer Contact Central

Build well-architected IDP solutions with a custom lens – Part 1: Operational excellence

The Omnichannel Contact Center: A Guide for 2020 (with Actionable Tips)

Webinars

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The Omnichannel Contact Center: A Guide for 2020 (with Actionable Tips)

Webinars

Omnichannel Call Center: A Guide for 2020 (with Actionable Tips)

Optimize generative AI workloads for environmental sustainability

Build well-architected IDP solutions with a custom lens – Part 5: Cost optimization

Host ML models on Amazon SageMaker using Triton: CV model with PyTorch backend

The Top 9 Aircall Alternatives: How Do They Compare?

How to Improve Customer Service – A Complete Guide of Tools, Tech & Tips

Host ML models on Amazon SageMaker using Triton: Python backend

Call Center Reporting - A New Paradigm

Hosting ML Models on Amazon SageMaker using Triton: XGBoost, LightGBM, and Treelite Models

Model hosting patterns in Amazon SageMaker, Part 1: Common design patterns for building ML applications on Amazon SageMaker

Scale foundation model inference to hundreds of models with Amazon SageMaker – Part 1

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