AWS Machine Learning

DECEMBER 7, 2022

The success of any machine learning (ML) pipeline depends not just on the quality of model used, but also the ability to train and iterate upon this model. However, doing this tuning manually can often be cumbersome due to the size of the search space, sometimes involving thousands of training iterations. Solution overview.

Scripts 72

Scripts Metrics Healthcare Accountability 72

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 110

APIs Healthcare Scripts Enterprise 110

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 109

Metrics APIs Scripts Construction 109

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

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 168

Customer Experience Scripts Self service outsourcing 168

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

AWS Machine Learning

MAY 31, 2024

In this blog post and open source project , we show you how you can pre-train a genomics language model, HyenaDNA , using your genomic data in the AWS Cloud. Amazon SageMaker Amazon SageMaker is a fully managed ML service offered by AWS, designed to reduce the time and cost associated with training and tuning ML models at scale.

Biotechnology 90

Biotechnology Scripts Healthcare APIs 90

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

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 103

Scripts APIs Metrics Management 103

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

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

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

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 100

Scripts Finance Telecommunications Enterprise 100

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 100

Scripts Engineering Best practices Construction 100

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

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 113

Engineering Scripts Metrics APIs 113

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

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 101

Scripts Engineering Metrics Best practices 101

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 104

Customer Service Coaching Contact Center Banking 104

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

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 89

Scripts Metrics Construction Accountability 89

Callminer

SEPTEMBER 23, 2020

Outbound call centers depend on skilled and well-trained agents as much as useful software to consistently meet business goals. Call centers that implement agent performance management solutions equip their agents with the ongoing coaching and training needed to perform at their best. Keep metrics in mind and up to date.

Best practices 182

Best practices Call Center Outbound sales Telemarketing 182

Steve DiGioia

JANUARY 6, 2020

Can Go Off Script. ? Nobody needs to write your words. Next on your list is training him/her on your company’s best practices, available methods of customer contact, service metrics, and overall expectations, just to name a few. You have little need for coaxing to get it done and seldom need help from others.

Customer Service 200

Customer Service Scripts Best practices Metrics 200

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 89

Scripts Metrics Engineering Analytics 89

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 106

Scripts Engineering Metrics Big data 106

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 93

Scripts Metrics Best practices Management 93

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 77

Scripts Metrics Finance Enterprise 77

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 113

Scripts Engineering Metrics Management 113

AWS Machine Learning

OCTOBER 18, 2024

SageMaker is an excellent choice for model training, because it reduces the time and cost to train and tune ML models at scale without the need to manage infrastructure. Because you pay only for what you use, you can manage your training costs more effectively. The final model artifact is saved to an S3 bucket.

Scripts 88

Scripts APIs Engineering Marketing 88

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 82

Best practices APIs Transportation Scripts 82

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 77

Scripts Engineering Metrics Management 77

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 69

Scripts Benchmark Metrics Transportation 69

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 77

Metrics Engineering Scripts Management 77

AWS Machine Learning

SEPTEMBER 16, 2024

Benefiting from recent advance in large language models (LLMs), a variety of computational biology tasks can be solved by fine-tuning biological LLMs pre-trained on billions of known biological sequences. In this post, we adopt a pre-trained genomic LLMs for gRNA efficiency prediction. Use the CRISPRspec_CRISPRoff_pipeline.py

Scripts 105

Scripts Metrics Healthcare Education 105

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 112

Scripts Healthcare Metrics Finance 112

SharpenCX

JUNE 28, 2023

Improving your customer service metrics requires a deeper look at which KPIs make sense for your contact center and the strategies you use to achieve them. What Call Center Metrics Should You Measure? You can use this metric to identify peak volume as well. You can use this metric to identify peak volume as well.

Call Center 67

Call Center Metrics call center software Contact Center 67

Fonolo

DECEMBER 15, 2022

The Executive Guide to Improving 6 Contact Center Metrics. How to Use Customer Profiles to Improve Call Center Training. Still, these tools aren’t ready to handle fully complex queries — that’s where your highly trained agents come into play. Invest in Training and Onboarding. Training isn’t just for new hires.

Call Center 138

Call Center Contact center software Abandon rate Self service 138