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Neptune.ai > 实例探究 > 利用 Neptune 扩大机器学习研究:ailslab 案例研究
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Implementing Neptune for Efficient Machine Learning in Bioinformatics: A Case Study of ailslab

技术
  • 分析与建模 - 机器学习
适用行业
  • 水泥
  • 教育
适用功能
  • 产品研发
用例
  • 实验自动化
  • 预测性维护
服务
  • 数据科学服务
挑战
ailslab 在扩大机器学习研究规模时面临着多项挑战,包括数据隐私、工作流程标准化、特征和模型选择、实验管理和信息记录。
关于客户
ailslab 是一个小型生物信息学研究小组,专注于利用机器学习根据临床、成像和遗传学数据预测心血管疾病的发展。他们需要一个解决方案来扩大研究规模并克服各种挑战。
解决方案
ailslab 选择 Neptune 作为应对这些挑战的解决方案,因为它可以节省时间并简化研究过程。 Neptune 帮助管理多个实验、标准化工作流程、选择功能和模型、管理实验和记录信息。
运营影响
  • With the implementation of Neptune, ailslab researchers now have a unified platform where their results are presented in a standardized manner, reducing the potential for errors. The process of comparing and managing experiments has become less time-consuming, with the ability to track the history of experiments, make changes, and observe the impact of these changes on the results. Building complex models, such as deep learning models for images, has become somewhat easier, as Neptune stores data about the environment setup, the underlying code, and the model architecture. Neptune also aids in organization, with ailslab adding experiment URLs from Neptune to cards in their Kanban board in Notion, providing easy access to experiment information and helping keep everything organized. This has resulted in a better understanding of factors such as the effect of hyperparameters on the model.

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