Automated machine learning solutions can help address problems with data-driven activities by selecting optimal machine learning models and techniques without requiring deep machine learning expertise. Vitriol is one such solution that uses meta-learning to leverage knowledge from previous learning tasks to select imputation methods, models, and hyperparameters for new problems. It has a web application interface that allows users to easily connect databases, preprocess and complete data, select modeling tasks, and visualize results without training or space limitations. With each new problem solved, Vitriol's meta-learner continues to improve its model selection abilities.

1. Automated Machine Learning Solutions

2. "People worry that computers will gettoo smart and take overthe world, butthe real
problem is that they’re too stupid and they’ve already taken over the world."
- PedroDomingos
IntelligentMachines?

3. Problems with Data Driven Activities
Business Perspective
We have several data streams, but what can we do with them?
More importantly, how?
Good data scientists are hard to find.
Creating a machine learning team in the organization is too costly.
Data Scientist Perspective
Experimenting with many models to find a solution is inconvenient and time-
consuming.

4. No free lunch
No universal learning algorithm (for now?)
There is not a learning algorithm which can be guaranteed to succeed on all learnable
tasks.
Any learning algorithm has a limited scope
There is always a trade off
Bias - variance

5. Basis for all: Meta-Learning
Transfer Learning
Inductive Transfer (learning to learn)
Hundreds of thousands of learning episodes
Learning process improves progressively

6. Data Imputation
Multiple imputation methods to enrich solution space
● Statistical methods
● Machine learning based methods
➔ Thousands of possible imputation methods
• Methods employ a combination of algorithms

7. Meta-Learner
➔ A rich set of meta-data representing the information about
previous learning tasks
• Analysis of the problem
➢ More than 20 features to describe the dataset
• Selected ML model
➢ Imputation method
➢ Model algorithm
➢ Values of tunable parameters
• Evaluation results in multiple metrics
➢ Regression metrics (R2, accuracy…)
➢ Classification metrics (WeightedFMeasure, ...)

8. Meta-Learner
➔Experiments proved intra-domain transfer learning is more feasible
• Meta-data is categorized according to their data domains.
➔ How is meta-data exploited?
• Selection of the most suitable model *
➔ Meta-Learner improves by each problem solved.
➔ Online Learning for instant update.

9. Why Web Application
 Easy to Use
 Does not need training to learn
 Globally Accessible
 No space limitations
 Easy to update

10. Web Service Architecture
 Single page application
 Componet-based design
 Non-blocking single threaded
IO
 Caching for session
management

11. Visualization
➔ Dataset visualization
• Data distribution (2D & 3D)
• Analysis results
➔ Model visualization
• Classification
• Clustering

12. Vitriol in Action 1: Data Preprocessing
I. User provides DB
credentials
II. Chooses the table to work
on
III. He/She can update the table
everytime
IV. He/She can see the results of
preprocessing right after
connecting the db

13. Vitriol in Action 2: Model Selection
I. User selects the table that
he/she wants Vitriol to work
on
II. Chooses pre-process or
Model Creation
III. For pre-proccessing the tow
options are clean and
complete
IV. To create a modal chooses
the column to define the
modal tag

14. Conclusion: Why is this better?
➔ Complete set of method selections (including preprocessing)
➔ Machine learning knowledge is not required
➔ More candidate models are evaluated in less time
➔ Continually improving decisions