Enghouse Interactive

JULY 19, 2019

Resource Allocations: IT teams can be more effectively deployed to areas that contribute to building the business, their focus shifted to enhancing the customer experience and improving service levels versus running the contact center infrastructure. 3 – Challenges that arise when moving to Cloud?

Cloud contact 40

Cloud contact Contact Center Service level Best practices 40

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 103

Best practices Engineering Metrics APIs 103

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 84

Finance Best practices APIs Accountability 84

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 85

APIs Scripts Engineering Accountability 85

aircall

SEPTEMBER 12, 2022

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. Our in-house onboarding managers also share best practices and review a compliance checklist for internet connection and headset configuration. .

voip 62

voip APIs Interactive Voice Response Analytics 62

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 84

APIs Scripts Best practices Enterprise 84

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 72

Customer Service Chatbots Best practices Surveys 72

Balto

SEPTEMBER 18, 2023

When a customer picks up their phone to call your company, they expect a fast and consistent service level. Integration with Existing Customer Service Systems Established agent assist software solutions offer a wide range of native integrations with popular customer support tools and platforms.

Interactive Voice Response 52

Interactive Voice Response Scripts Contact Center Coaching 52

Avaya

JULY 22, 2019

These processes must be coupled with case management to queue efficiently, distribute smartly and report on back-office service levels. To achieve this, companies need: Open APIs: to increase the value of information systems and platforms by integrating them to drive business requirements; and.

Customer Experience 77

Customer Experience APIs Banking Chatbots 77

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

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 65

Scripts Engineering Enterprise Construction 65

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. For more information about TCO, refer to The total cost of ownership of Amazon SageMaker. Throughput.

Metrics 75

Metrics Best practices Engineering Management 75

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 84

Chatbots SaaS Engineering APIs 84