PyCharm 是最强大的 Python 开发环境,最强大。智能代码补全和调试工具显著缩短了开发时间,让我可以更多地专注于数据分析和模型构建。如果你正在使用 Python,无论是数据还是其他,你都应该考虑使用 PyCharm。 Lysandre Debut Hugging Face 首席开源官 作为Hugging Face 用户,而不仅仅是 Hugging Face 团队成员,PyCharm 中...
将跳转至支付宝完成支付 确定 取消 编辑仓库简介 简介内容 ZenML是一个可扩展的开源 MLOps 框架,用于创建生产就绪的机器学习管道 主页 取消保存更改 Python 1 https://gitee.com/mirrors/zenml.git git@gitee.com:mirrors/zenml.git mirrors zenml zenml main...
The workspace image can also be used to execute arbitrary Python code without starting any of the pre-installed tools. This provides a seamless way to productize your ML projects since the code that has been developed interactively within the workspace will have the same environment and configuratio...
Projects Packages People7 More PinnedLoading doubleml-for-pydoubleml-for-pyPublic DoubleML - Double Machine Learning in Python Python57086 doubleml-for-rdoubleml-for-rPublic DoubleML - Double Machine Learning in R R14426 doubleml-serverlessdoubleml-serverlessPublic ...
Projects Structure 下面从项目结构理解MLOps。 整个Project包含MLOps整个生命周期需要的所有文件,核心算法包在telco_churn文件夹中。其中红色框是ML Engineer需要使用的文件,绿色框是Data Scientist需要使用的文件。 telco_churn算法文件夹中包含了feature-table-creation、model-train、model-deployment、model-inference-batch...
Projects Structure 下面从项目结构理解MLOps。 整个Project包含MLOps整个生命周期需要的所有文件,核心算法包在telco_churn文件夹中。其中红色框是ML Engineer需要使用的文件,绿色框是Data Scientist需要使用的文件。 telco_churn算法文件夹中包含了feature-table-creation、model-train、model-deployment、model-inference-batch...
Keras是一个由Python编写的开源人工神经网络库,可以作为Tensorflow、Microsoft-CNTK和Theano的高阶应用程序接口,进行深度学习模型的设计、调试、评估、应用和可视化。 Keras在代码结构上由面向对象方法编写,完全模块化并具有可扩展性,其运行机制和说明文档有将用户体验和使用难度纳入考虑,并试图简化复杂算法的实现难度。Keras...
Seeinformation on moving machine learning projects from ML Studio (classic) to Azure Machine Learning. Learn more aboutAzure Machine Learning ML Studio (classic) documentation is being retired and may not be updated in the future. Python is a valuable tool in the tool chest of many data scienti...
Azure Machine Learning environments ensure that builds are reproducible without using manual software configurations. Environments can track and reproduce the pip and conda software dependencies for your projects. You can use environments for model training and deployment. For more information on environments...
Although Kafka also allows obtaining information about the performance of ML models (automatically), it will take ideas from these projects to improve the information provenance and to have a better control of ML model versions. Finally, Kafka-ML is related to some extent to AutoML projects such...