AI Superstream: Data-centric AI.
If you're a data scientist, machine learning engineer, or another decision maker overseeing the development and deployment of machine learning systems and you've already experienced the limits of a model-centric approach, this event is for you. Join us for expert-led sessions to discover t...
| Format: | eBook |
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| Language: | English |
| Published: |
[Place of publication not identified]
O'Reilly Media, Inc.
2023
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| Edition: | [First edition] |
| Subjects: | |
| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
| Summary: | If you're a data scientist, machine learning engineer, or another decision maker overseeing the development and deployment of machine learning systems and you've already experienced the limits of a model-centric approach, this event is for you. Join us for expert-led sessions to discover the untapped potential of data-centric AI. What you'll learn and how you can apply it Understand the principles of data-centric AI and how they can improve your machine learning systems Learn how to enhance your machine learning system through data iterability and quality, data labeling and curation, and by recentering subject matter experts This recording of a live event is for you because... You're working with data for machine learning systems as a data scientist, data/machine learning engineer, data/machine learning architect, or machine learning team leader. You want to leverage your data effectively and efficiently to get the most out of your machine learning system. Over the past decade, the field of AI has achieved incredible results by focusing on building and training powerful deep learning models, from convolutional neural networks to state-of-the-art transformers. While the results of this model-centric approach have been inspiring, a growing number of experts have recognized the importance of ensuring the quality of the data used to train these models in order to build real-world machine learning systems that address the business and social needs of today. AI pioneer Andrew Ng has spearheaded the effort to move away from a model-centric approach to what he calls a "data-centric" approach to solving today's AI challenges. Data-centric AI renews focus on improving the data that makes AI systems work, through data iterability and quality, by embracing programmatic approaches to data labeling and curation, and by recentering subject matter experts as key players within the AI system development process. Prerequisites Basic knowledge of machine learning systems Recommended follow-up: Read Training Data for Machine Learning (early release book) Read Practical Weak Supervision (book) Watch Best Practices for Automated Data Labeling in NLP (event video) Please note that slides or supplemental materials are not available for download from this recording. Resources are only provided at the time of the live event |
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| Physical Description: | 1 video file (3 hr., 49 min.) sound, color |