Callminer

JANUARY 18, 2021

Use call recordings and ongoing training to nurture emotional competence among agents. Training agents to excel at their positions falls largely on teaching them to calmly coax positive results from negative situations. Emotional intelligence can be trained most effectively by refocusing your agents’ attention on their own behaviors.

Best practices 226

Best practices Call Center Contact Center Scripts 226

AWS Machine Learning

APRIL 19, 2024

In this post, we focus on how we used Karpenter on Amazon Elastic Kubernetes Service (Amazon EKS) to scale AI training and inference, which are core elements of the Iambic discovery platform. We wanted to build a scalable system to support AI training and inference. Here we use the number of requests per second as a custom metric.

Metrics 123

Metrics APIs Scripts Construction 123

AWS Machine Learning

APRIL 29, 2024

In recent years, large language models (LLMs) have gained attention for their effectiveness, leading various industries to adapt general LLMs to their data for improved results, making efficient training and hardware availability crucial. In this post, we show you how efficient we make our continual pre-training by using Trainium chips.

APIs 121

APIs Healthcare Scripts Enterprise 121

AWS Machine Learning

SEPTEMBER 26, 2024

Trained on the Amazon SageMaker HyperPod , Dream Machine excels in creating consistent characters, smooth motion, and dynamic camera movements. Model parallel training becomes necessary when the total model footprint (model weights, gradients, and optimizer states) exceeds the memory of a single GPU.

Scripts 118

Scripts APIs Metrics Management 118

AWS Machine Learning

NOVEMBER 20, 2023

This vision model developed by KT relies on a model pre-trained with a large amount of unlabeled image data to analyze the nutritional content and calorie information of various foods. The teacher model remains unchanged during KD, but the student model is trained using the output logits of the teacher model as labels to calculate loss.

Scripts 119

Scripts Finance Telecommunications Enterprise 119

ProProfs Blog

APRIL 17, 2020

But without numbers or metric data in hand, coming up with any new strategy would only consume your valuable time. For example, you need access to metrics like NPS, average response time and others like it to make sure you come up with relevant strategies that help you retain more customers. So, buckle up. 1: Customer Churn Rate. #2:

Metrics 142

Metrics Customer retention SaaS Surveys 142

AWS Machine Learning

FEBRUARY 29, 2024

The framework code and examples presented here only cover model training pipelines, but can be readily extended to batch inference pipelines as well. Configuration files (YAML and JSON) allow ML practitioners to specify undifferentiated code for orchestrating training pipelines using declarative syntax.

Scripts 124

Scripts Engineering Metrics Best practices 124

Callminer

APRIL 24, 2020

In essence, this structured interview process allows a group of candidates to work through tasks and assessments; it also gives those in charge of hiring the opportunity to select the best performers in the group and train them together to become new call center agents. Focus on the Metrics that Matter Most. Avoid Negative Language.

Best practices 182

Best practices Call Center Coaching Schedule adherence 182

AWS Machine Learning

NOVEMBER 22, 2023

Like many ML organizations, accelerators are largely used to accelerate DL training and inference. In this post, we discuss how M5 was able to reduce the cost to train their models by 30%, and share some of the best practices we learned along the way. To use accelerators, you need a software layer to support them.

Scripts 112

Scripts Engineering Best practices Construction 112

Callminer

JULY 16, 2018

Contact centers especially struggle with how to train, manage, and engage agents properly. The metrics used to measure an at-home agent’s performance will probably be different since they may work flexible hours or handle a specific type of incoming call. Personalize their training. Use gamification. Recognize their efforts.

At home agents 196

At home agents Call Center Gamification Average Handle Time 196

ShepHyken

NOVEMBER 4, 2022

This week, we feature an article by Baphira Wahlang Shylla, a digital marketer at Knowmax , a SaaS company that provides knowledge management solutions for various industries that are seeking to improve their customer service metrics. For example, it can take up to 5-6 weeks to provide training to new agents at a call center.

Customer Experience 166

Customer Experience Scripts Self service outsourcing 166

AWS Machine Learning

AUGUST 2, 2024

Investors and analysts closely watch key metrics like revenue growth, earnings per share, margins, cash flow, and projections to assess performance against peers and industry trends. Traditionally, earnings call scripts have followed similar templates, making it a repeatable task to generate them from scratch each time.

Engineering 130

Engineering Scripts Metrics APIs 130

AWS Machine Learning

OCTOBER 27, 2022

Certain machine learning (ML) workloads, such as training computer vision models or reinforcement learning, often involve combining the GPU- or accelerator-intensive task of neural network model training with the CPU-intensive task of data preprocessing, like image augmentation. This post is co-written with Chaim Rand from Mobileye.

Scripts 97

Scripts Benchmark Metrics Transportation 97

CCNG

AUGUST 23, 2023

Performance Optimization: Data analytics can reveal key performance metrics such as call resolution times, average handling times, and first-call resolution rates. Analyzing these metrics helps contact centers identify bottlenecks and areas for improvement. This optimization leads to enhanced operational efficiency and reduced costs.

Contact Center 195

Contact Center Analytics CCNG Average Handle Time 195

Callminer

FEBRUARY 4, 2021

Firstly, contact centers can make use of call analytics software to analyze past call recordings and use them to train agents how to identify vulnerable customers. Now more than ever, organizations need to actively manage the Average-Speed-of-Answer (ASA) metric. Addressing increased vulnerability will take training…”.

Call Center 345

Call Center Contact Center Finance Consulting 345

AWS Machine Learning

DECEMBER 13, 2024

This helps with data preparation and feature engineering tasks and model training and deployment automation. Pipelines also integrates with Amazon SageMaker Automatic Model Tuning which can automatically find the hyperparameter values that result in the best performing model, as determined by your chosen metric.

Scripts 97

Scripts Engineering Analytics Sales 97

Calltools

NOVEMBER 13, 2024

This is where dynamic scripting comes in. It customizes call scripts in real time, ensuring every single conversation is more relevant and personal. Dynamic scripting lets you cater scripts for different customers, demographics, and campaigns. What Is Dynamic Scripting? Dynamic scripting can help with all this.

Scripts 52

Scripts CRM call center software Technical Support 52

AWS Machine Learning

OCTOBER 28, 2024

SageMaker Model Monitor adapts well to common AI/ML use cases and provides advanced capabilities given edge case requirements such as monitoring custom metrics, handling ground truth data, or processing inference data capture. For example, users can save the accuracy score of a model, or create custom metrics, to validate model quality.

Scripts 113

Scripts Metrics Engineering Analytics 113

AWS Machine Learning

AUGUST 24, 2023

Today, we’re pleased to announce the preview of Amazon SageMaker Profiler , a capability of Amazon SageMaker that provides a detailed view into the AWS compute resources provisioned during training deep learning models on SageMaker. There are two approaches that you can take to profile your training scripts with SageMaker Profiler.

Scripts 98

Scripts Metrics Finance Enterprise 98

AWS Machine Learning

JULY 1, 2024

Amazon SageMaker is a machine learning (ML) platform designed to simplify the process of building, training, deploying, and managing ML models at scale. Additionally, we walk through a Python script that automates the identification of idle endpoints using Amazon CloudWatch metrics. client("cloudwatch") sagemaker = boto3.client("sagemaker")

Scripts 117

Scripts Metrics Best practices Management 117

Callminer

APRIL 21, 2020

Call center managers may be involved with hiring and training call center agents , monitoring call center metrics tied to agent performance , using speech analytics tools for ongoing quality monitoring , providing ongoing feedback and coaching, and more. The presentation is shown to everyone in the training. William Taylor.

Call Center 182

Call Center Management Scripts Coaching 182

AWS Machine Learning

MARCH 6, 2023

Large language models (LLMs) are neural network-based language models with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical. The size of an LLM and its training data is a double-edged sword: it brings modeling quality, but entails infrastructure challenges.

Best practices 105

Best practices APIs Transportation Scripts 105

AWS Machine Learning

MARCH 9, 2023

Applying these techniques allows ML practitioners to reduce the amount of data required to train an ML model. As part of this approach, advanced data subset selection techniques have surfaced to speed up training by reducing input data quantity. Applying this type of technique reduces the amount of time required to train an ML model.

Scripts 98

Scripts Engineering Metrics Management 98

AWS Machine Learning

DECEMBER 16, 2022

This is achieved by organizing and managing ML experiments in an effortless way to draw conclusions from them, for example, finding the training run with the best accuracy. Run : Each execution step of a model training process. For SageMaker Training, Processing and. Prerequisites. Prerequisites. medium instance type.

Metrics 98

Metrics Engineering Scripts Management 98

AWS Machine Learning

AUGUST 11, 2022

This post shows you how to use an integrated solution with Amazon Lookout for Metrics and Amazon Kinesis Data Firehose to break these barriers by quickly and easily ingesting streaming data, and subsequently detecting anomalies in the key performance indicators of your interest. You don’t need ML experience to use Lookout for Metrics.

Metrics 97

Metrics APIs Scripts Wireless 97

AWS Machine Learning

JANUARY 6, 2025

In this post, we walk you through the process to build an automated mechanism using Amazon SageMaker to process your log data, run training iterations over it to obtain the best-performing anomaly detection model, and register it with the Amazon SageMaker Model Registry for your customers to use it. We discuss both approaches in this post.

Scripts 109

Scripts Metrics Construction Accountability 109

AWS Machine Learning

AUGUST 16, 2023

Training machine learning (ML) models to interpret this data, however, is bottlenecked by costly and time-consuming human annotation efforts. By training on large amounts of unlabeled image data, self-supervised models learn image representations that can be transferred to downstream tasks, such as image classification or segmentation.

Scripts 98

Scripts Analytics Technology Benchmark 98

Callminer

JULY 5, 2018

Call centers that utilize automated call scoring define the metrics they want to track on every call such as script adherence, industry compliance wording, voice inflection, and long spans of silence. Once you input these metrics, the software automatically scores every interaction. Customize trainings.

First call resolution 157

First call resolution Call Center Average Handle Time Contact Center 157

CSM Magazine

JANUARY 20, 2025

Understanding how SEO metrics tie to customer satisfaction is no longer optionalit’s essential. Metrics like bounce rate, time on site, and keyword rankings don’t just track website performance; they reveal how well you’re meeting customer needs.

Metrics 64

Metrics Feedback Analytics Scripts 64

Integrity Solutions

SEPTEMBER 28, 2023

Their emphasis customer service training delivers end-to-end service excellence that is driving strong loyalty, competitive differentiation and direct revenue generation. They don’t want ten minutes of on-hold music only to hear canned, scripted responses that ignore their real issues and needs. Customers want empathy.

Customer Service 106

Customer Service Coaching Contact Center Banking 106

AWS Machine Learning

JUNE 29, 2022

However, training these gigantic networks from scratch requires a tremendous amount of data and compute. For smaller NLP datasets, a simple yet effective strategy is to use a pre-trained transformer, usually trained in an unsupervised fashion on very large datasets, and fine-tune it on the dataset of interest. training script.

Benchmark 100

Benchmark Metrics APIs Scripts 100

AWS Machine Learning

JUNE 17, 2024

This post outlines the steps AWS and Twilio took to migrate Twilio’s existing machine learning operations (MLOps), the implementation of training models, and running batch inferences to Amazon SageMaker. The training data used for this pipeline is made available through PrestoDB and read into Pandas through the PrestoDB Python client.

Scripts 119

Scripts Engineering Metrics Big data 119

Fonolo

SEPTEMBER 16, 2021

How does tone of voice improve call center metrics? There are a few ways tone of voice can improve customer service and positively impact call center metrics: It develops brand loyalty and conveys the values of your company, securing the right type of customers. Top 3 methods for training tone of voice.

Customer Service 167

Customer Service Call Center Scripts Interactive Voice Response 167

AWS Machine Learning

AUGUST 22, 2023

Many ML algorithms train over large datasets, generalizing patterns it finds in the data and inferring results from those patterns as new unseen records are processed. Native support for distributed training is offered through the Amazon SageMaker SDK, along with example notebooks in popular frameworks. What is federated learning?

Scripts 98

Scripts Accountability Transportation Benchmark 98

AWS Machine Learning

APRIL 1, 2024

Machine learning (ML) research has proven that large language models (LLMs) trained with significantly large datasets result in better model quality. In the last few years, the size of current generation models has increased significantly, and they require modern tools and infrastructure to be trained efficiently and at scale.

Scripts 127

Scripts Engineering Metrics Management 127

AWS Machine Learning

MAY 29, 2024

By combining text data with images and other modalities during training, multimodal models such as Claude3, GPT-4V, and Gemini Pro Vision gain more comprehensive understanding and improved ability to process diverse data types. LLaVA training happens in two stages, as shown in Figure 1 that follows. An example training sample follows.

Scripts 132

Scripts Healthcare Metrics Finance 132

AWS Machine Learning

MARCH 6, 2024

Similarly, pLMs are pre-trained on large protein sequence databases using unlabeled, self-supervised learning. This means that it can take a long time to train them to sufficient accuracy. Long training times, plus large instances, equals high cost, which can put this work out of reach for many researchers.

Scripts 125

Scripts Biotechnology Healthcare Accountability 125