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Exploring the pros and cons of cloud-based large language models

CIO

The paradigm shift towards the cloud has dominated the technology landscape, providing organizations with stronger connectivity, efficiency, and scalability. In light of this, developer teams are beginning to turn to AI-enabled tools like large language models (LLMs) to simplify and automate tasks.

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The Future of Machine Learning in Cybersecurity

CIO

Machine learning (ML) is a commonly used term across nearly every sector of IT today. This article will share reasons why ML has risen to such importance in cybersecurity, share some of the challenges of this particular application of the technology and describe the future that machine learning enables.

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Artificial Intelligence and Machine Learning in Cloud-Native Environments

Dzone - DevOps

In our industry, few pairings have been as exciting and game-changing as the union of artificial intelligence (AI) and machine learning (ML) with cloud-native environments. It's a union designed for innovation, scalability, and yes, even cost efficiency.

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QuantrolOx uses machine learning to control qubits

TechCrunch

QuantrolOx , a new startup that was spun out of Oxford University last year, wants to use machine learning to control qubits inside of quantum computers. Current methods, QuantrolOx CEO Chatrath argues, aren’t scalable, especially as these machines continue to improve. million (or about $1.9

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MLOps 101: The Foundation for Your AI Strategy

Many organizations are dipping their toes into machine learning and artificial intelligence (AI). Download this comprehensive guide to learn: What is MLOps? How can MLOps tools deliver trusted, scalable, and secure infrastructure for machine learning projects?

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Techniques and approaches for monitoring large language models on AWS

AWS Machine Learning - AI

Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP), improving tasks such as language translation, text summarization, and sentiment analysis. Monitoring the performance and behavior of LLMs is a critical task for ensuring their safety and effectiveness.

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Use zero-shot large language models on Amazon Bedrock for custom named entity recognition

AWS Machine Learning - AI

Traditional neural network models like RNNs and LSTMs and more modern transformer-based models like BERT for NER require costly fine-tuning on labeled data for every custom entity type. By using the model’s broad linguistic understanding, you can perform NER on the fly for any specified entity type.