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Listen "Machine Learning Made Simple Podcasts - Episode 17"
Episode Synopsis
Topic: Next-Level AI: Fine-Tuning with PEFT and LoRA for Industry Leaders
Summary:
AI Agent Fine-Tuning: Strategies for fine-tuning AI agents to perform specific tasks akin to employees within an organization, enhancing role-specific functionalities.
Collaborative AI Functionality: How multiple AI agents can work in tandem, simulating a cohesive workforce to streamline operations and increase efficiency.
LoRA's Role in Efficiency: Exploring Low-Rank Adaptation (LoRA) as a method to facilitate fine-tuning with limited resources, such as 1 or 2 GPUs, by reducing the rank of training weights for more efficient model training.
Productivity Enhancement: The impact of fine-tuned AI agents on workforce augmentation, demonstrating how AI can complement human personnel to boost productivity and drive innovation.
Discover the innovative methods behind speeding up Large Language Model training, including the integration of PEFT and LoRA, and how they can be applied to create and manage efficient AI agents within the business environment.
If you enjoy this podcast, please:
Subscribe to get notifications for new episodes.
Follow me on LinkedIn for the latest in AI/ML papers and discussions.
About:
Dive into the magic of machine learning with our podcast, where we unravel the mysteries in a language everyone can groove to! Ideal for the movers and shakers in the tech world – from top-tier execs shaping ML strategies to tech leads leading squads of MLEs. Whether you're an IT pro on the brink of a ML adventure or just someone itching to ride the ML wave, we've got your backstage pass to the world of ML hype! Tune in, turn up, and let's demystify machine learning together! 🚀✨ #MLGroove #DecodeTheHype 🎙️
Legal Disclaimer for Machine Learning Made Simple
Summary:
AI Agent Fine-Tuning: Strategies for fine-tuning AI agents to perform specific tasks akin to employees within an organization, enhancing role-specific functionalities.
Collaborative AI Functionality: How multiple AI agents can work in tandem, simulating a cohesive workforce to streamline operations and increase efficiency.
LoRA's Role in Efficiency: Exploring Low-Rank Adaptation (LoRA) as a method to facilitate fine-tuning with limited resources, such as 1 or 2 GPUs, by reducing the rank of training weights for more efficient model training.
Productivity Enhancement: The impact of fine-tuned AI agents on workforce augmentation, demonstrating how AI can complement human personnel to boost productivity and drive innovation.
Discover the innovative methods behind speeding up Large Language Model training, including the integration of PEFT and LoRA, and how they can be applied to create and manage efficient AI agents within the business environment.
If you enjoy this podcast, please:
Subscribe to get notifications for new episodes.
Follow me on LinkedIn for the latest in AI/ML papers and discussions.
About:
Dive into the magic of machine learning with our podcast, where we unravel the mysteries in a language everyone can groove to! Ideal for the movers and shakers in the tech world – from top-tier execs shaping ML strategies to tech leads leading squads of MLEs. Whether you're an IT pro on the brink of a ML adventure or just someone itching to ride the ML wave, we've got your backstage pass to the world of ML hype! Tune in, turn up, and let's demystify machine learning together! 🚀✨ #MLGroove #DecodeTheHype 🎙️
Legal Disclaimer for Machine Learning Made Simple
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