LogML Pipeline
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When you provide LogML with data and configuration file, it first determines what individual steps are
to be executed and their dependencies.

Typical dependency line for each analysis looks like:

- Data preprocessing: prepare data for modeling. Result is LogML Dataset entity.

- Specific analysis: accept dataset and perform analsys:

    - For example, Feature Importance analysis performs modeling and extraction of
      features essential for target explanation.

- Report generation: use artifacts generated by analysis step to render results visualization.

- Result packaging: archive report and important analysis artifacts.



Essentially an "analysis" is a set of LogML components (module) which receives incoming data,
builds a model of some sort - statistical or machine learning model - and then makes a conclusion
about the data.

In general there are three types of questions LogML analyses try to answer:

- What is the relation between covariates and target variables? (Modeling and Survival Analysis).
- Given groups of samples, what features define separation? (Expression analysis).
- Is there anything specific about statistical properties of data (Exploratory analysis).

See following sections for details of analyses kinds provided by LogML.