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.

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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.

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Vistio

JULY 1, 2024

In the contact center, one would assume that extensive training leads to confident and well-prepared agents. However, a perplexing paradox has emerged: as agents undergo more training, their anxiety levels often increase. This seems counterintuitive—shouldn’t more training result in better preparedness and confidence?

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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.

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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.

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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.

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Scripts Metrics Engineering Analytics 101

Fonolo

JANUARY 31, 2023

Call center training has always been one of the key pillars of running a successful call center. A strong call center training program should not just be part of your onboarding process. Still have questions about call center training? What is Call Center Training? Don’t just pick one.

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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.

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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.

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Balto

JULY 7, 2022

When agents intentionally go off script, it’s because they are improvising to get a better call outcome and should be encouraged. In 2022, we published our findings on why agents intentionally go off their scripts. For agents in this stage of their job, training is the most important resource managers can offer to agents.

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TeleDirect

MAY 12, 2023

Metrics and KPI tracking will help you do this with ease. Measuring and examining your call center metrics and KPIs (key performance indicators) will provide valuable information about your outbound call center’s performance and identify any weak spots. Do they use the proper scripting and verbiage? We love a good phone call.

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Balto

JUNE 23, 2022

“The anti-script doesn’t mean that you should wing it on every call… what anti-script means is, think about a physical paper script and an agent who is reading it off word for word… you’re taking the most powerful part of the human out of the human.” Share on Twitter. Share on Facebook.

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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.

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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.

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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")

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Scripts Metrics Best practices Management 104

aircall

MARCH 10, 2021

The right people, training, and call center training materials ensure that your call center agents are working hard to enhance the customer experience, even when tasks are piling up. The problem is figuring out exactly which call center training materials you should use to achieve this. What Are Call Center Training Materials?

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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.

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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

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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.

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Customer Service Coaching Contact Center Banking 107

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.

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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.

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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.

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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.

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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.

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Serenova

JULY 7, 2020

Six Call Center Training Tips & Best Practices for Better Customer Experience. To do that effectively takes well-trained agents and ongoing, consistent agent training programs—from onboarding and beyond. 6 Steps and Best Practices for Your Best Call Center Staff Training Program. Embrace continuous training.

Call Center 96

Call Center Scripts contact center solutions Coaching 96

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.

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AWS Machine Learning

NOVEMBER 21, 2024

Fine-tuning foundation models (FMs) is a process that involves exposing a pre-trained FM to task-specific data and fine-tuning its parameters. In this post, we show how to use an Amazon SageMaker Autopilot training job with the AutoMLV2 SDK to fine-tune a Meta Llama2-7B model on question answering tasks. Check the training job status.

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Metrics APIs Healthcare Scripts 82

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?

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Scripts Accountability Transportation Benchmark 77

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.

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Calltools

FEBRUARY 16, 2022

It may sound complicated, but a fairly simple set of KPI metrics can help you measure your lead source ROI. The most important KPI metrics for monitoring your lead source ROI are: Cost per Acquisition. This metric assesses the quality of your lead lists. Agents that need further training. Connection Rate.

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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.

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AWS Machine Learning

NOVEMBER 22, 2024

However, building or fine-tuning these pre-trained LLMs on extensive datasets demands substantial computational resources and engineering effort. Mixtral employs a sparse mixture of experts (SMoE) architecture, selectively activating only a subset of its parameters for each input during model training. QLoRA operates on a large FM.

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AWS Machine Learning

NOVEMBER 14, 2024

The DS uses SageMaker Training jobs to generate metrics captured by , selects a candidate model, and registers the model version inside the shared model group in their local model registry. Optionally, this model group can also be shared with their test and production accounts if local account access to model versions is needed.

Government 88

Government Management APIs Accountability 88

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.

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NobelBiz

SEPTEMBER 12, 2023

For call centers, metrics provide insights that shape strategies and determine operational efficiency. Among these metrics, the “Talk” metric stands out as a vital instrument. Understanding the Average Talk Time metric Average Talk Time represents the actual duration an agent spends conversing with a customer.

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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.

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AWS Machine Learning

APRIL 12, 2023

In this post, we share how Games24x7 improved their training pipelines for their responsible gaming platform using Amazon SageMaker. This step-function instantiated a cluster of instances to extract and process data from S3 and the further steps of pre-processing, training, evaluation would run on a single large EC2 instance.

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