H2O Label Genie Starter Course will show you how to save time and effort on data labeling using H2O Label Genie, an intelligent platform that makes labeling text, images, and audio faster, smarter, and more accurate.
Through hands‑on exercises and real‑world examples, you will learn to use its annotation tools, apply advanced techniques like zero‑shot labeling, dataset clustering, and LLM‑assisted text labeling, and connect your labeled data seamlessly with H2O.ai’s ML platforms.
By the end, you will have a good understanding and grounded confidence to streamline labeling and improve data quality with H2O Label Genie.
What you'll learn
Introduction to Data Labeling
Learn the role and importance of data labeling in AI and machine learning.
Environment Setup
Set up H2O Label Genie and connect to H2O.ai Aquarium for hands‑on practice.
Advanced Labeling Strategies
Use multi‑class annotation, dataset exploration, and AI‑assisted tools like zero‑shot prediction and LLMs to speed up labeling.
H2O Label Genie Basics Get introduced to H2O Label Genie and learn its core concepts and features.
Annotation Techniques Learnig how to create, manage, and export text, image, and audio labels.
Real‑World Application
Build hands‑on experience applying H2O Label Genie to labeling tasks and creating high‑quality datasets for machine learning projects.
Course Playlist on YouTube
In this intro video, we’ll show you how to quickly categorize and summarize text using AI.
Watch how Label Genie makes data labeling faster, smarter, and effortless.
🔗 Try H2O Label Genie using Aquarium : https://aquarium.h2o.ai/
📖 Learn More: https://h2o.ai/university/
Learn the fundamentals of data labeling and why it’s critical for building accurate, reliable machine learning models.
In this video, you will be introduced to what data labeling is, its role in AI systems, common challenges teams face, and how tools like H2O Label Genie can streamline the process with AI-powered assistance.
🔗 Learn more about H2O Label Genie: https://h2o.ai/platform/ai-cloud/make...
🔗 H2O.ai Label Genie Documentation: https://docs.h2o.ai/wave-apps/h2o-lab...
🔗 Explore H2O.ai University: https://h2o.ai/university/
In this video, Andreea will walk you through text annotation, showing how AI-powered labeling can speed up workflows and enhance model performance. Follow along as we connect to the Aquarium lab environment and get hands-on with text classification and summarization.
🔗 Try H2O Label Genie using Aquarium : https://aquarium.h2o.ai/
📖 Learn More: https://h2o.ai/university/
Take a look at the latest updates in H2O.ai Aquarium, featuring a refreshed user interface and seamless integration with H2O.ai University.
These enhancements make it easier than ever to access hands-on labs and learning resources, all in one place.
▶ Start exploring Aquarium here: https://aquarium.h2o.ai/
▶ Learn more at H2O.ai University: https://h2o.ai/university/
In this video, we will explore the H2O Label Genie Homepage and Datasets Tabs.
You will learn how to navigate key sections, manage datasets, and prepare data for annotation.
We’ll guide you through importing datasets, exploring the amazon-reviews-demo dataset, and setting up sentiment classification tasks.
🔗 Try H2O Label Genie using Aquarium : https://aquarium.h2o.ai/
📖 Learn More: https://h2o.ai/university/
This step-by-step guide will teach you how to create new annotations in H2O Label Genie .
We’ll walk you through setting up an annotation task, defining classification labels, and using zero-shot learning to streamline sentiment analysis. See how automatic predictions simplify the labeling process and how to verify and export your results efficiently.
🔗 Try H2O Label Genie using Aquarium : https://aquarium.h2o.ai/
📖 Learn More: https://h2o.ai/university/
Learn how to export annotation outcomes in H2O Label Genie with this quick tutorial.
We’ll guide you through downloading approved samples and zero-shot predictions as ZIP files and explain the difference between categorical and numeric outputs.
You will understand how these files can be used for further analysis and machine learning workflows.
🔗 Documentation: https://docs.h2o.ai/wave-apps/h2o-label-genie/
📖 Learn More: https://h2o.ai/university/
Learn how to annotate customer reviews with multiple sentiment categories—such as satisfaction or dissatisfaction with different products—to capture nuanced feedback.
🔹 Step-by-step setup for a new annotation task
🔹 Enabling multi-label selection for complex sentiments
🔹 Leveraging zero-shot predictions for automatic categorization
Watch the full demo to see how H2O Label Genie intelligently assigns multiple labels based on prediction confidence.
🔗 Try H2O Label Genie using Aquarium : https://aquarium.h2o.ai/
📖 Learn More: https://h2o.ai/university/
In this tutorial, discover how to leverage H2O Label Genie’s summarization task to generate concise and meaningful summaries from lengthy text. Learn how to select from various state-of-the-art models, including Facebook’s BartLargeCNN, Google’s PegasusLarge, H2O GPT, and OpenAI’s GPT-3.5 and GPT-4.
🔹 Choosing the right summarization model
🔹 Reviewing and refining AI-generated summaries
🔹 Using zero-shot predictions for automated text processing
🔗 Try H2O Label Genie using Aquarium : https://aquarium.h2o.ai/
📖 Learn More: https://h2o.ai/university/
Label Genie: https://h2o.ai/platform/ai-cloud/make/h2o-label-genie/
In this video, we get an overview of some tricks to exploring our datasets. We take a look at how to visualise Text and Image Datasets using the clustering feature.
This is really great for getting a first "feel" about your dataset before getting our hands into the preparation/annotation which is covered in Video 2
Credits:
Datasets used:
https://www.kaggle.com/datasets/arushchillar/disneyland-reviews
https://www.kaggle.com/datasets/crowww/a-large-scale-fish-dataset
https://www.kaggle.com/datasets/rajneesh231/lex-fridman-podcast-transcript
Label Genie: https://h2o.ai/platform/ai-cloud/make/h2o-label-genie/
Welcome to Part 2 of the course, watch Part 1 first: https://youtu.be/zQ_EX44oz1E
In Part 2, we take a look at how to utilise Keyboard shortcuts and Zero-Shot Predictions to label an image dataset.
The video also showcases the use of Segment Anything implementation in our segmentation example.
The goal is to show that Zero-Shot predictions can be a really great first parse through your datasets
Credits:
Datasets used:
https://www.kaggle.com/datasets/arushchillar/disneyland-reviews
https://www.kaggle.com/datasets/crowww/a-large-scale-fish-dataset
https://www.kaggle.com/datasets/rajneesh231/lex-fridman-podcast-transcript
Label Genie: https://h2o.ai/platform/ai-cloud/make/h2o-label-genie/
Welcome to Part 3 of the course, watch Part 2 here: https://youtu.be/HGncp-HCO9w
In Part 3, we improve our last example from the previous video-where we had use a language model to create summaries.
In this one, we use h2oGPT to create executive overview of our reviews. This feature shows our integrations and ease of using Large Language Models to assist with data labelling.
The goal is to show you how LLMs can further speed up your workflow with text datasets.
Credits:
Datasets used:
https://www.kaggle.com/datasets/arushchillar/disneyland-reviews
https://www.kaggle.com/datasets/crowww/a-large-scale-fish-dataset
https://www.kaggle.com/datasets/rajneesh231/lex-fridman-podcast-transcript
In this starter course, we covered H2O Label Genie, an intuitive AI-powered data labeling tool that simplifies annotation with zero-shot learning.
Many teams have already adopted H2O Label Genie to improve efficiency and accuracy in AI development. Now it's your turn!
📌 Try H2O Label Genie today for free : https://aquarium.h2o.ai/
📌 Learn More: https://h2o.ai/university/
📌Documentation: https://docs.h2o.ai/wave-apps/h2o-label-genie/
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.”
Sanyam Bhutani is a Machine Learning Engineer and AI Content Creator at H2O.ai. He is also an inc42, Economic Times recognized Machine Learning Practitioner. (link to the interviews: inc42, Economic Times) Sanyam is an active Kaggler where he is a Triple Tier Expert, ranked in Global Top 1% in all categories as well as an active AI blogger on the medium, Hackernoon (Medium Blog link) with over 1 Million+ Views overall. Sanyam is also the host of Chai Time Data Science Podcast where he interviews top practitioners, researchers, and Kagglers. You can follow him on Twitter or subscribe to his podcast.