H2O ai Large Language Models (LLMs) - Level 2
Master data preparation and model fine-tuning techniques for optimizing LLMs using H2O's specialized tools for NLP applications.
Master data preparation and model fine-tuning techniques for optimizing LLMs using H2O's specialized tools for NLP applications.
Continue your exploration of Large Language Models (LLMs) with this foundational Level 2 course! Specially designed for those with foundational knowledge, this course delves deep into optimizing Natural Language Processing (NLP) models through robust data practices. Discover the critical role of clean data and effective data preparation techniques essential for NLP model quality. Using LLM DataStudio, navigate supported workflows, customize interfaces, and implement quality control measures. Learn to set up projects and leverage collaboration features to enhance team efficiency. Master QnA dataset creation, ensuring accuracy through validation and quality assurance processes. Perfect fine-tuning with H2O LLM Studio, where you'll tailor models to specific tasks. Explore workflows, employ data augmentation strategies, and select optimal architectures from pre-trained models. Delve deeper into advanced techniques like Quantisation and LoRA for model compression, optimizing your NLP applications for real-world deployment.
Instructors:
English
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What you'll learn
Learn to prepare and clean datasets for NLP, ensuring high-quality data to improve model outcomes
Explore LLM DataStudio's workflows, customize interfaces, and implement efficient data management strategies
Apply advanced fine-tuning techniques in H2O LLM Studio to optimize language models for specific NLP tasks
Master QnA dataset creation through validation and quality assurance processes
Utilize data augmentation strategies to enhance model performance
Implement model compression techniques like Quantisation and LoRA for efficient deployment
Skills you'll gain
This course includes:
3.1 Hours PreRecorded video
1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 4 modules in this course
This intermediate-level course focuses on optimizing Natural Language Processing (NLP) models through effective data preparation and model fine-tuning techniques. Students will learn the critical importance of clean data for LLM performance and master practical skills using H2O's specialized tools. The curriculum covers comprehensive workflows in LLM DataStudio for data preparation, curation, and quality control. Students will create and validate QnA datasets, implement fine-tuning techniques in H2O LLM Studio, and explore advanced optimization strategies like Quantisation and LoRA. The course emphasizes practical application through hands-on demonstrations with H2O.ai's suite of tools, providing students with the skills needed to develop and deploy optimized language models for real-world NLP applications.
Getting Started with LLM Data Prep
Module 1 · 25 Minutes to complete
Mastering LLM DataStudio
Module 2 · 1 Hours to complete
Fine-Tuning Your Large Language Models
Module 3 · 55 Minutes to complete
Course Completion Quiz
Module 4 · 15 Minutes to complete
Fee Structure
Instructor
Data Science and AI Strategy Leader
Andreea Turcu has established herself as a Data Scientist with over 7 years of experience specializing in AI platforms and business transformation. Her expertise lies in translating complex AI concepts into practical business advantages, focusing on creating competitive edges through data-driven solutions. Her perspective that "AI is essentially Economics, turbocharged by data, with a sprinkle of innovation" reflects her practical approach to implementing AI solutions. As a specialist in making AI and Data Science accessible, she develops engaging training programs and integrates machine learning into real-world applications. Her work focuses on bridging the gap between technical AI capabilities and business outcomes, helping organizations navigate the transition to AI-driven operations while maintaining a focus on practical, measurable results. Her approach combines technical expertise with business acumen, enabling organizations to leverage AI effectively for sustainable growth and innovation.
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