How-to guides for data analysis and operationalization
Important
This content is being retired and may not be updated in the future. The support for Machine Learning Server will end on July 1, 2022. For more information, see What's happening to Machine Learning Server?
This section of the Machine Learning Server documentation is for data scientists, analysts, and statisticians. The focus of this content area is on data acquisition, transformation and manipulation, visualization, and analysis in R and Python, as well as the deployment and consumption of models and code. It provides step-by-step guidance for common tasks leveraging the libraries and packages in Machine Learning Server.
If you are new to R, be sure to also use the R Core Team manuals that are part of every R distribution, including An Introduction to R, The R Language Definition, Writing R Extensions and so on. Beyond the standard R manuals, there are many other resources. Learn about them here.
How-to guidance
Data analysis
- Data acquisition
- Data summaries
- Models in ScaleR
- Crosstabs
- Linear models
- Logistic regression
- Generalized linear
- Decision trees
- Decision forest
- Stochastic gradient boosting
- Naïve Bayes classifier
- Correlation and variance/covariance matrices
- Clustering
- Converting RevoScaleR model objects for use in PMML
- Transform functions
- Visualizing huge data sets
Remote code execution on Machine Learning Server
Operationalization: deploy and consume models and code
- Publish & manage
- Consume (request-response)
- Consume (asynchronous)
- Integrate into apps
- Manage access tokens