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H2O Label Genie Starter Track

H2O.ai University

In this beginner-friendly course, you will learn how to dramatically accelerate this process using H2O Label Genie, H2O.ai’s intelligent data annotation platform designed to simplify, automate, and scale your labeling workflows.

The H2O Label Genie Starter Track equips you with foundational and practical knowledge to start labeling text, image, and audio data using AI-powered tools.

Whether you’re a data scientist, ML engineer, analyst, or AI enthusiast, this course provides hands-on guidance on creating and exporting annotations, exploring datasets, and applying advanced techniques such as zero-shot labeling, dataset clustering, and LLM integration for text labeling.

Through structured walkthroughs and real-world examples, you’ll gain experience with:

• Label Genie’s user interface and annotation workflows

• Text summarization and classification

• Efficient dataset exploration and task management

• Seamless integration with H2O’s ML tools like Driverless AI and LLM Studio

By the end of this course, you’ll have the skills and confidence to reduce manual effort, improve data quality, and enhance your AI model performance using H2O Label Genie.

Take your first step toward faster, smarter, and AI-assisted data labeling with H2O Label Genie.

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What's inside

Syllabus

Introduction to AI-Powered Data Labeling

Getting Started with Label Genie

Basic Annotation Workflows

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Career center

Learners who complete H2O Label Genie Starter Track will develop knowledge and skills that may be useful to these careers:

Data Labeling Specialist

A Data Labeling Specialist is at the forefront of preparing high-quality datasets essential for training robust AI and machine learning models. This course directly equips an aspiring Data Labeling Specialist with foundational and practical knowledge to dramatically accelerate the data annotation process using H2O Label Genie, H2O.ai’s intelligent platform. Learners gain hands-on guidance on creating and exporting annotations for text, image, and audio data, exploring datasets efficiently, and applying advanced techniques like zero-shot labeling and LLM integration. By mastering these skills and workflows, individuals can significantly reduce manual effort, improve data quality, and enhance overall AI model performance, leading to success in this critical role.

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Data Annotator

A Data Annotator performs the essential task of precisely labeling data, which is the cornerstone for developing accurate and reliable AI and machine learning models. This course provides an individual seeking to become a Data Annotator with foundational and practical knowledge for efficient and automated data annotation using H2O Label Genie. You will gain hands-on experience with the platform's user interface, learn basic and advanced annotation workflows for text, image, and audio data, and apply techniques such as dataset clustering. This focused training helps improve data quality and reduce manual effort, making one highly proficient in this increasingly in-demand and specialized field.

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Machine Learning Data Curator

A Machine Learning Data Curator is responsible for managing the lifecycle of data used in machine learning projects, ensuring its quality, integrity, and accessibility. This course offers practical knowledge in accelerating the crucial data labeling process using H2O Label Genie, which is vital for a Machine Learning Data Curator. Gaining experience with efficient dataset exploration and task management, alongside techniques such as zero-shot labeling and LLM integration, helps in curating high-quality, well-organized datasets. Understanding these AI-powered tools aids in streamlining data pipelines, ensuring that models are trained on reliable data, and ultimately enhancing overall AI model performance.

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Artificial Intelligence Data Preparation Engineer

An Artificial Intelligence Data Preparation Engineer focuses on designing and implementing processes to prepare raw data for use in AI and machine learning models. This course provides foundational and practical knowledge that directly benefits an Artificial Intelligence Data Preparation Engineer by demonstrating how to dramatically accelerate the data labeling process using H2O Label Genie. You will learn about efficient annotation workflows, text summarization and classification, and seamless integration with other H2O ML tools. This hands-on experience in improving data quality and reducing manual effort for text, image, and audio data annotation is essential for building robust and scalable data pipelines for AI applications.

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Quality Assurance Engineer for Machine Learning Data

A Quality Assurance Engineer for Machine Learning Data specializes in ensuring the accuracy, consistency, and overall suitability of labeled datasets for AI model training. This course provides foundational and practical knowledge that is highly relevant for a Quality Assurance Engineer for Machine Learning Data, as it focuses on improving data quality and enhancing AI model performance through efficient data labeling. Learners gain experience with H2O Label Genie’s user interface and annotation workflows, which include advanced techniques like dataset clustering. This understanding allows one to identify potential issues in labeled data more effectively and implement best practices to maintain high standards for machine learning datasets.

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Computer Vision Data Annotator

A Computer Vision Data Annotator specializes in accurately labeling image and video data, which forms the training bedrock for computer vision models. This course directly contributes to the skills needed by a Computer Vision Data Annotator, providing foundational and practical knowledge for labeling image data using AI-powered tools like H2O Label Genie. Learners gain hands-on guidance on creating and exporting annotations, exploring datasets, and applying advanced techniques such as dataset clustering. This experience helps reduce manual effort, improve data quality, and enhance the performance of AI models that rely on visual inputs, making one proficient in a critical aspect of computer vision development.

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Natural Language Processing Data Annotator

A Natural Language Processing Data Annotator focuses on meticulously labeling text data, which is crucial for training effective natural language processing models. This course is highly relevant for a Natural Language Processing Data Annotator, equipping them with foundational and practical knowledge for labeling text data using AI-powered tools, including LLM integration for text labeling. Learners will gain hands-on experience with text summarization and classification, as well as efficient dataset exploration and task management using H2O Label Genie. This training helps improve data quality and reduce manual effort, which is essential for building robust NLP applications and fostering success in this specialized field.

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Speech Recognition Data Annotator

A Speech Recognition Data Annotator is responsible for accurately labeling audio data, which is fundamental for training and improving speech recognition and audio processing models. This course provides foundational and practical knowledge for a Speech Recognition Data Annotator by equipping them to label audio data using AI-powered tools like H2O Label Genie. Learners will gain hands-on guidance on creating and exporting annotations, exploring datasets efficiently, and managing tasks within the platform's user interface. This experience helps reduce manual effort, improve the quality of audio datasets, and enhance the performance of AI models in speech recognition, making one skilled in this specialized domain.

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Machine Learning Engineer

A Machine Learning Engineer designs, builds, and deploys machine learning models, critically relying on high-quality labeled data for effective training. This course provides hands-on guidance on reducing manual effort and improving data quality using H2O Label Genie, directly enhancing AI model performance. Understanding these data annotation workflows helps a Machine Learning Engineer optimize model training pipelines and troubleshoot data-related issues. This course offers practical experience invaluable for building robust AI systems. This role often benefits from a master's degree.

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Data Scientist

A Data Scientist extracts insights and builds predictive models from data. While their role is broad, the quality of the data they work with is paramount for successful analysis and model development. This course provides foundational and practical knowledge in accelerating the crucial data labeling process using H2O Label Genie. Understanding how data is annotated, including techniques like dataset clustering and LLM integration for text labeling, helps a Data Scientist ensure the integrity and relevance of their datasets, ultimately leading to more reliable and impactful insights. This role often benefits from a master's or doctorate degree.

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Research Assistant Artificial Intelligence

A Research Assistant Artificial Intelligence supports cutting-edge AI research projects, which often involve extensive data collection and preparation. This course equips a Research Assistant Artificial Intelligence with foundational and practical knowledge to label text, image, and audio data using AI-powered tools like H2O Label Genie. Hands-on guidance on creating and exporting annotations, exploring datasets, and applying advanced techniques such as zero-shot labeling and dataset clustering, directly supports experimental AI research. This experience helps reduce manual effort and improve data quality, crucial for developing and validating new AI models. This role often benefits from a master's degree.

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Prompt Engineer

A Prompt Engineer designs and optimizes prompts for large language models to achieve desired outputs. This course may be useful for a Prompt Engineer as it provides experience with LLM integration for text labeling, including text summarization and classification. Understanding how labeled text data informs LLM behavior helps in crafting more effective and precise prompts. Hands-on guidance on exploring datasets and applying advanced labeling techniques directly correlates with developing a deeper intuition for how LLMs process and generate text based on structured annotations. This role often benefits from a master's degree.

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Technical Project Manager Artificial Intelligence

A Technical Project Manager Artificial Intelligence oversees the planning, execution, and delivery of AI-driven projects. This course may be useful for a Technical Project Manager Artificial Intelligence as it provides a practical understanding of a critical and time-consuming step in any AI or machine learning pipeline: data labeling. Experience with H2O Label Genie's user interface, annotation workflows, and automation features helps a manager better understand project timelines, resource allocation, and potential bottlenecks. This knowledge aids in making informed decisions and ensuring the efficient progression of AI initiatives. This role often benefits from a master's degree.

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AI Ethics Specialist

An AI Ethics Specialist ensures that AI systems are developed and used responsibly, addressing issues like bias and fairness. This course may be useful for an AI Ethics Specialist because it offers practical knowledge in data annotation workflows, which are critical in identifying and mitigating biases present in training data. Understanding how data is labeled, and the potential pitfalls in that process, can help specialists audit datasets more effectively. Gaining experience with improving data quality through H2O Label Genie contributes to advocating for robust labeling practices that lead to more equitable and transparent AI systems.

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Data Analyst

A Data Analyst interprets complex datasets to identify trends and inform business decisions. While not typically a direct labeling role, this course may be useful for a Data Analyst as it provides foundational knowledge in a critical upstream process for many data projects. Understanding data annotation workflows, efficient dataset exploration, and how data quality is enhanced using tools like H2O Label Genie can provide a broader perspective on data preparation. This insight helps an analyst better understand data origins, potential biases, and the efforts involved in preparing data for robust analysis, leading to more informed interpretations of results.

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