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Build on your foundation in Large Language Models (LLMs) with practical techniques for fine-tuning, optimizing, and deploying NLP models.

This course focuses on hands-on experience with H2O LLM Studio, guiding you through model customization, experiment monitoring, and deployment.

Explore advanced topics like quantization and LoRA to make your models efficient and production-ready. Designed for learners with prior exposure to LLMs, this course helps deepen your understanding and awards you a certification upon completion.

 

 

LLM Level 2 Certified Badge LLM Level 2 Certified Badge
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What you'll learn

Introduction-to-ai-agents Introduction-to-ai-agents


Importance of Clean Data for NLP

Understand why data quality matters and how well-prepared datasets drive reliable, fair, and high-performing language models.

Introduction-to-ai-agents Introduction-to-ai-agents


Hands-On with LLM Studio
 

Navigate the no-code interface to configure, launch, monitor, and compare fine-tuning experiments with ease.

Introduction-to-ai-agents Introduction-to-ai-agents


Fine-Tuning Techniques
 

Learn when and why to fine-tune LLMs, and how specialization, transfer learning, and task-specific data shape model performance.

Introduction-to-ai-agents Introduction-to-ai-agents


Choosing the Right Backbone
 

Explore how to select pre-trained model architectures based on task, domain, data size, and hardware constraints.

Introduction-to-ai-agents Introduction-to-ai-agents


Model Compression Essentials
 

Apply advanced techniques to reduce model size and speed up inference without sacrificing performance.

Introduction-to-ai-agents Introduction-to-ai-agents


Deploying to Hugging Face
 

Export your fine-tuned model and share it with the broader AI community through one-click deployment.

Course Playlist on YouTube

1
Key Functions in Language Model Data Preparation!
8:59
Key Functions in Language Model Data Preparation!
2
Fine-Tuning Principles for Large Language Models
12:42
Fine-Tuning Principles for Large Language Models
3
Synthetic Datasets and Language Model Backbones
13:36
Synthetic Datasets and Language Model Backbones
4
Fine-Tuning with Quantization and LoRA
8:55
Fine-Tuning with Quantization and LoRA
5
LLM Optimization - Techniques and Insights
3:36
LLM Optimization - Techniques and Insights
6
A Journey through H2O.ai's LLM Studio
37:43
A Journey through H2O.ai's LLM Studio
7
Deploying LLM Models with H2O LLM Studio
5:49
Deploying LLM Models with H2O LLM Studio

 

Quiz Me if You Can!

Additional Course Resources & Access Links

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Andreea Turcu

Head of Global Training

Andreea is a data scientist with over 7 years of experience in demystifying AI and Data Science concepts for anyone keen on working in this exciting field using cutting-edge technology. Having obtained a Master’s Degree in Quantitative Economics and Econometrics from Lumière Lyon 2 University, she enjoys integrating machine learning principles with real-world applications. Andreea’s passion lies in developing engaging training programs and ensuring an optimal customer education journey. As she frequently likes to remark, “AI is essentially Economics turbocharged by data, with a sprinkle of innovation.”

You can view her LinkedIn profile HERE.

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Learn Hands On

Follow structured learning paths designed to build real, production-ready AI skills. Learn at your own pace, practice on real environments, and validate your knowledge through certification.

EXPLORE LEARNING PATHS