Gives a overview about GraphLab/Dato capabilities.

2.  python package
 SFrame: data frame/tabular data, capable of
storing data to disk and stream it during
computations
 SGraph for graph analysis
 neat integration with iPython
 Graphlab Canvas for interactive GUIs

3.  Can infer nested data. eg. dict is a valid
feature type, GraphLab will explode it to form
features on it’s own

4. Standard algorithms already implemented
connected component
graph coloring
K-Core decomposition
PageRank
Single-source shortest path
Triangle count

5.  Linear regression
feature scaling, missing value imputation
 Boosted Decision Trees
hyperparameter (max_iterations, max_depth,
step_size, min_child_weight, min_loss_reduction ..)
 wrapper ‘regression.create' method which selects
best regression model based on ‘some’ parameter
 wrapper over Vowpal Wabbit is present
 Regularisation support is there (lasso, ridge, enet)
 No randomForest !

6.  Logistic regression
 SVM
 Boosted Decision Trees
 Neural network classifier (deep learning)
selects a default network architecture (2-layer
Perceptron Network for dense numeric input, 1-layer
Convolution Network for image)
custom architecture possible
pre-built models trained on imageNet present, can be
used directly to extract features
 model selector present 'classifier.create'

7.  Bag of words
 TF-IDF
 Topic model

8. Out of box ‘recommender.create’ available

9. Single click push to deployment to multiple
environments (EC2, etc)

10. Single click push to deployment to multiple
environments (EC2, etc)