Apple's Overton: Enhancing Data Labeling with Snorkel's Weak Supervision Framework
公司规模
1,000+
地区
- America
国家
- United States
产品
- Overton
- Snorkel
技术栈
- Snorkel
- Weak Supervision
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- Professional Service
- Software
适用功能
- 商业运营
- 产品研发
服务
- 软件设计与工程服务
- 系统集成
关于客户
Apple Inc. is a global technology leader known for its innovative products and services, including the iPhone, iPad, Mac, Apple Watch, and Apple TV. The company also offers a range of software applications, operating systems, and digital content. With a strong focus on research and development, Apple continuously seeks to enhance its technological capabilities to deliver superior user experiences. The company operates in various regions worldwide, with a significant presence in the United States. Apple employs thousands of engineers and developers who work on cutting-edge technologies to maintain its competitive edge in the market.
挑战
Apple faced a significant challenge in supporting engineers who were dealing with contradictory or incomplete supervision data. This issue was critical as it impacted the accuracy and efficiency of their machine learning models. The traditional methods of data labeling were not only time-consuming but also prone to errors, which further complicated the problem. Apple needed a robust system that could handle these complexities while ensuring data privacy and reducing costs. The existing solutions in the market did not meet these requirements, prompting Apple to develop an in-house solution that could address these specific challenges effectively.
解决方案
To tackle the challenge, Apple developed a solution called Overton, which leveraged Snorkel's framework of weak supervision. This approach allowed Apple to overcome the issues of cost, privacy, and cold-start that were inherent in traditional data labeling methods. Overton utilized weak supervision to generate large volumes of labeled data efficiently, significantly reducing the time and effort required for manual labeling. The system was designed to handle multiple languages and process vast amounts of data, making it highly scalable and adaptable to various use cases. By integrating Snorkel's technology, Apple was able to create a more accurate and reliable data labeling system that met the needs of its engineers.
运营影响
数量效益
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