Remove Categorization Remove Chatbots Remove Data Quality
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With generative AI, don’t believe the hype (or the anti-hype)

IBM Journey to AI blog

Hay argues that part of the problem is that the media often conflates gen AI with a narrower application of LLM-powered chatbots such as ChatGPT, which might indeed not be equipped to solve every problem that enterprises face. In this context, data quality often outweighs quantity.

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IT Service Desk Chatbot: Automate your Service Desk

Chatbots Life

A chatbot is a technological genie that uses intelligent automation, ML, and NLP to automate tasks. Chatbots are transforming the IT service desk's workplace support and service delivery procedures to make them more efficient and successful in serving employees. Chatbots connect employees to support agents only when it is needed.

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Building Domain-Specific Custom LLM Models: Harnessing the Power of Open Source Foundation Models

Towards AI

Challenges of building custom LLMs Building custom Large Language Models (LLMs) presents an array of challenges to organizations that can be broadly categorized under data, technical, ethical, and resource-related issues. Ensuring data quality during collection is also important.

LLM 98
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What are AI Agents? Demystifying Autonomous Software with a Human Touch

Marktechpost

Resources from DigitalOcean and GitHub help us categorize these agents based on their capabilities and operational approaches. Customer Service and Virtual Assistance One practical application is in customer service, where AI-powered chatbots and virtual assistants handle routine inquiries, offer recommendations, and even troubleshoot issues.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

It includes processes for monitoring model performance, managing risks, ensuring data quality, and maintaining transparency and accountability throughout the model’s lifecycle. Model risk : Risk categorization of the model version. Model stage : Stage where the model version is deployed. For example, pending or approved.

ML 89
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Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction

Towards AI

If you want an overview of the Machine Learning Process, it can be categorized into 3 wide buckets: Collection of Data: Collection of Relevant data is key for building a Machine learning model. It isn't easy to collect a good amount of quality data. How Machine Learning Works?

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5 Key Open-Source Datasets for Named Entity Recognition

Becoming Human

The goal of NER is to automatically identify and categorize specific information from vast amounts of text. In AI, entities refer to tangible and intangible elements like people, organizations, locations, and dates embedded in text data. These datasets act as training data for machine learning models. Disadvantages 1.Data