Remove Benchmark Remove Engineering Remove Feedback Remove Scripts
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Integrate HyperPod clusters with Active Directory for seamless multi-user login

AWS Machine Learning

Typically, HyperPod clusters are used by multiple users: machine learning (ML) researchers, software engineers, data scientists, and cluster administrators. To achieve this multi-user environment, you can take advantage of Linux’s user and group mechanism and statically create multiple users on each instance through lifecycle scripts.

Scripts 88
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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning

This was the perfect place to start for our prototype—not only would Axfood gain a new AI/ML platform, but we would also get a chance to benchmark our ML capabilities and learn from leading AWS experts. If discrepancies arise, a business logic within the postprocessing script assesses whether retraining the model is necessary.

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Improving your LLMs with RLHF on Amazon SageMaker

AWS Machine Learning

Reinforcement Learning from Human Feedback (RLHF) is recognized as the industry standard technique for ensuring large language models (LLMs) produce content that is truthful, harmless, and helpful. Gone are the days when you need unnatural prompt engineering to get base models, such as GPT-3, to solve your tasks. yaml ppo_hh.py

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25 Call Center Leaders Share the Most Effective Ways to Boost Contact Center Efficiency

Callminer

They don’t do anything else except maybe monitor a few calls and give some feedback. Bill Dettering is the CEO and Founder of Zingtree , a SaaS solution for building interactive decision trees and agent scripts for contact centers (and many other industries). Interactive agent scripts from Zingtree solve this problem.

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Prioritize Performance Over Presence Daily to Manage an Effective Call Center Team

SharpenCX

The Agile Manifesto , which is usually your engineering team’s go-to guide for management, can help leaders in every department. How do they like to receive feedback? Set benchmarks and measure your team on how they perform against them. Yes, including your contact center. If you stick by your team, they’ll repay the favor.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

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New technical deep dive course: Generative AI Foundations on AWS

AWS Machine Learning

We’ll cover fine-tuning your foundation models, evaluating recent techniques, and understanding how to run these with your scripts and models. We’ll dive into reinforcement learning with human feedback, exploring how to use it skillfully and at scale to truly maximize your foundation model performance.

Scripts 82