AWS Machine Learning

SEPTEMBER 20, 2023

For example, you might have acquired a company that was already running on a different cloud provider, or you may have a workload that generates value from unique capabilities provided by AWS. We show how you can build and train an ML model in AWS and deploy the model in another platform.

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AWS Machine Learning

MARCH 8, 2023

With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. This post shows a way to do this using Snowflake as the data source and by downloading the data directly from Snowflake into a SageMaker Training job instance.

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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.

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AWS Machine Learning

APRIL 29, 2024

To build a production-grade AI system today (for example, to do multilingual sentiment analysis of customer support conversations), what are the primary technical challenges? First, the AWS Trainium accelerator provides a high-performance, cost-effective, and readily available solution for training and fine-tuning large models.

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ProProfs Blog

DECEMBER 24, 2019

What makes live chat scripts so important for sales and customer service? To realize all the benefits of live chat scripts, you need to understand the importance of chat etiquette for your customers’ experience and satisfaction. Useful Customer Service Scripts Templates And Examples. Customer Service Greetings Scripts.

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ChurnZero

MARCH 10, 2023

We’ll walk through how to write prompts to help you better serve your customers, with examples you can add to your CS toolkit. Use examples : Generative AI relies on preexisting data for context, so it’s helpful to guide them in the right direction from the start. Are you interested in using AI to make your Customer Success job easier?

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AWS Machine Learning

MAY 1, 2024

Llama2 by Meta is an example of an LLM offered by AWS. It comes in a range of parameter sizes—7 billion, 13 billion, and 70 billion—as well as pre-trained and fine-tuned variations. Many practitioners fine-tune or pre-train these Llama 2 models with their own text data to improve accuracy for their specific use case.

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AWS Machine Learning

JULY 20, 2023

We then provide an example of how you can train, optimize, and deploy your custom ensembles using Amazon SageMaker. We tend to categorize ensembles by the techniques used to train them, their composition, and the way they merge the different predictions into a single inference. Examples include Random Forest and Extra Trees.

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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

FEBRUARY 1, 2023

Although larger models tend to be more powerful, training such models requires significant computational resources. Even with the use of advanced distributed training libraries like FSDP and DeepSpeed, it’s common for training jobs to require hundreds of accelerator devices for several weeks or months at a time.

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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. In industries like retail, for example, the past couple of years have raised the bar on customer service expectations. Customers want empathy. Really heard.

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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.

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AWS Machine Learning

NOVEMBER 16, 2022

When designing production CI/CD pipelines, AWS recommends leveraging multiple accounts to isolate resources, contain security threats and simplify billing-and data science pipelines are no different. Some things to note in the preceding architecture: Accounts follow a principle of least privilege to follow security best practices.

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ChurnZero

MARCH 10, 2023

We’ll walk through how to write prompts to help you better serve your customers, with examples you can add to your CS toolkit. Use examples : Generative AI relies on preexisting data for context, so it’s helpful to guide them in the right direction from the start. Are you interested in using AI to make your Customer Success job easier?

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Scripts Accountability Feedback Marketing 75

AWS Machine Learning

FEBRUARY 16, 2023

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. At the server level, such training workloads demand faster compute and increased memory allocation. As models grow to hundreds of billions of parameters, they require a distributed training mechanism that spans multiple nodes (instances).

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AWS Machine Learning

SEPTEMBER 2, 2022

Distributed deep learning model training is becoming increasingly important as data sizes are growing in many industries. Many applications in computer vision and natural language processing now require training of deep learning models, which are growing exponentially in complexity and are often trained with hundreds of terabytes of data.

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Fonolo

OCTOBER 21, 2021

The question is, can call centers improve agents’ emotional intelligence through training and employee engagement strategies ? A customer service agent trained to use empathy in their interactions has the power to develop a relationship with you even over the phone. Train your agents’ empathy skills with these 5 tips: 1.

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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

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

OCTOBER 1, 2022

Containers offer a solution, because they can fully encapsulate not just the training code, but the entire dependency stack down to the hardware libraries. This ensures an ML environment that is consistent and portable, and facilitates reproducibility of the training environment on each individual node of the training cluster.

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AWS Machine Learning

JULY 29, 2022

In part 1 , we addressed the data steward persona and showcased a data mesh setup with multiple AWS data producer and consumer accounts. The data scientists in this team use Amazon SageMaker to build and train a credit risk prediction model using the shared credit risk data product from the consumer banking LoB.

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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. You also need to add domain user profiles for individual users to access the SageMaker Profiler UI application.

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AWS Machine Learning

JUNE 5, 2023

Large models, when trained over massive datasets and several tasks, are also able to generalize well over tasks that they aren’t trained specifically for. One approach to improve performance of a large model for a specific domain or task is to further train the model with a smaller, task-specific dataset.

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AWS Machine Learning

JUNE 9, 2022

SageMaker JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. In this post, we’re excited to announce that all trainable JumpStart models now support incremental training.

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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.

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AWS Machine Learning

APRIL 26, 2024

The DBRX LLM employs a fine-grained mixture-of-experts (MoE) architecture, pre-trained on 12 trillion tokens of carefully curated data and a maximum context length of 32,000 tokens. The model underwent pre-training using a dataset consisting of 12 trillion tokens of text and code.

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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

MAY 22, 2024

For example, a product that has a description that includes words such as “long sleeve” and “cotton neck” will be returned if a consumer is looking for a “long sleeve cotton shirt.” Then the Sagemaker client is used to launch a Sagemaker Training job, again a wrapper for an HTTP call.

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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

APRIL 7, 2023

As an example, smart venue solutions can use near-real-time computer vision for crowd analytics over 5G networks, all while minimizing investment in on-premises hardware networking equipment. The following diagram shows an example of this architecture. The following diagram illustrates this architecture.

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Nicereply

NOVEMBER 7, 2023

Our guide and examples transform criticism into a tool for growth and collaboration. Here’s how to give constructive and actionable negative feedback with many real-world negative performance feedback examples, so you can improve how you phrase and approach it. 3) “You rely too heavily on macros / scripted responses.”

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AWS Machine Learning

NOVEMBER 15, 2023

Understanding up front which preprocessing techniques and algorithm types provide best results reduces the time to develop, train, and deploy the right model. For example, it can scale the data, perform univariate feature selection, conduct PCA at different variance threshold levels, and apply clustering.

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Integrity Solutions

MAY 24, 2023

What Is Sales Training? Sales Training – at its heart – should be about learning to uncover and identify customer needs, thus creating value and providing service. And this is where sales training should spend the largest proportion of its focus. Why are you doing the training? What role will managers play?

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AWS Machine Learning

JULY 11, 2023

With Amazon SageMaker , you can use the @remote decorator to run a SageMaker training job simply by annotating your Python code with an @remote decorator. Train a classification model based on the MNIST dataset using an @remote decorator from the open-source SageMaker Python SDK. For details, refer to Creating an AWS account.

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VocalCom

JULY 22, 2019

Call scripts help agents feel prepared when customers call your brand for service. Here are six golden rules for creating call scripts that satisfy your customers’ needs while still providing a gentle human touch. Abandon the script when necessary. Test call scripts regularly. Inform customers when there is a pause.

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AWS Machine Learning

DECEMBER 22, 2023

The agent can assist users with finding their account information, completing a loan application, or answering natural language questions while also citing sources for the provided answers. This memory allows the agent to provide responses that take into account the context of the ongoing conversation.

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AWS Machine Learning

AUGUST 29, 2023

SageMaker Python SDK is used to create or update SageMaker pipelines for training, training with hyperparameter optimization (HPO), and batch inference. SageMaker Pipelines serves as the orchestrator for ML model training and inference workflows.

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