利用 Neptune 改善蛋白质设计的实验管理和生产力
技术
- 分析与建模 - 机器学习
- 平台即服务 (PaaS) - 应用开发平台
适用行业
- 教育
- 设备与机械
适用功能
- 产品研发
用例
- 施工管理
- 实验自动化
服务
- 数据科学服务
- 培训
关于客户
InstaDeep 的 BioAI 团队是一群生物学和人工智能领域的专家。他们使用 DeepChain™ 蛋白质设计平台，利用强化学习和进化算法等优化技术来设计蛋白质靶标的新序列。他们还利用对数百万个蛋白质序列进行预训练的语言模型，并训练自己的内部蛋白质语言模型。他们的目标是通过生物学和机器学习专业知识的结合来推进医疗保健并突破医学科学的界限。
挑战
构建 DeepChain™（蛋白质设计平台）等复杂软件需要对不同的活动部件进行大量研究。客户需要各种类型的解决方案，每次都需要新的实验和研究。由于为不同的客户运行多个实验，对于任何规模的团队来说，在确保实验保持高效的同时跟踪实验都将不可避免地令人畏惧。
解决方案
Nicolas 决定寻找一种解决方案，帮助团队解决实验管理问题，更有效地利用他们的时间和精力。他们采用了 Neptune，这是一种实验管理工具，符合他们的标准：易于使用、易于连接到 TensorFlow 和 PyTorch 日志，而且价格也不太贵。
运营影响
数量效益
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