Cengiz Özemli
Akademisyen
- Thread Author
- #1
MLHub is a new module designed to bridge the gap between centralized data teams and operational experts. With MLHub, operational experts and data scientists gain the ability to collaborate on a machine learning application. MLHub enables the creation, training, and deployment of machine learning models for deeper operational insights.
Advanced Machine Learning Capabilities: MLHub allows data scientists to import data from TrendHub and ContextHub views, validate hypotheses using Python code, and deploy machine learning models. These models become accessible to all software users as Machine Learning tags in TrendHub or as notebook output tiles in DashHub.
New ML Environment: This release includes a new Jupyter notebook ecosystem, kernel isolation and resource management for improved performance, and a fully protected security layer. Additionally, notebook output cells for easy visualization and Machine Learning Model tags based on PMML models have been introduced.
Plotting Contextual Data: Multivariate scatter plots provide more powerful visualizations and allow process experts to classify the relationship between contextual events and attributes. These plots help in understanding correlations and distributions for further analysis.
Enhanced Operational Dashboards: Text tiles and notebook output tiles are now visible on the dashboard, allowing operational experts to present explanations and comments alongside visual data sources.
Advanced Machine Learning Capabilities: MLHub allows data scientists to import data from TrendHub and ContextHub views, validate hypotheses using Python code, and deploy machine learning models. These models become accessible to all software users as Machine Learning tags in TrendHub or as notebook output tiles in DashHub.
New ML Environment: This release includes a new Jupyter notebook ecosystem, kernel isolation and resource management for improved performance, and a fully protected security layer. Additionally, notebook output cells for easy visualization and Machine Learning Model tags based on PMML models have been introduced.
Plotting Contextual Data: Multivariate scatter plots provide more powerful visualizations and allow process experts to classify the relationship between contextual events and attributes. These plots help in understanding correlations and distributions for further analysis.
Enhanced Operational Dashboards: Text tiles and notebook output tiles are now visible on the dashboard, allowing operational experts to present explanations and comments alongside visual data sources.
