Machine learning algorithms have evolved from knowledge-driven expert systems to data-driven systems enabled by machine learning. Hundreds of machine learning methods now exist, including various types of neural networks, genetic algorithms, decision trees, and more. Automated machine learning (AutoML) is now possible due to advancements in expert systems, computing power, data manipulation techniques, and sophisticated learning algorithms. AutoML involves automated data cleansing, transformations, model training and evaluation, and can deploy models for use in various applications.

1. Automated Machine Learning (AutoML)
Putting AI to Work in Data Science
Gene Ferruzza
Sr. Manager Data Science – Valassis Digital
PyData
Triangle

2. The Evolution of Machine Learning
Machine Learning Algorithms Drive Data-Driven AI Systems
Statistics
Neural
Networks
Expert
Systems
Genetic
Algorithms
AI
Source: 1990 International Joint Conference on Neural Networks (IJCNN)
• In the 1980’s the focus in AI
transitioned from Knowledge-
Driven Systems to Data-Driven
Systems enabled through Machine
Learning
• Decision Trees emerged using ID3
and CART statistical classifying
algorithms for rule induction
methods
• Multiple Neural Network paradigms
and Genetic Algorithms proved to
be successful at learning from data

3. The Evolution of Machine Learning
Statistics
• Linear Regression
• Logistic Regression
• Non-Linear Least Squares
• Many Other Math Based Methods
Neural
Networks
• BackProp
• SOM,CNN
• Deep Learning
• Visual Recognition
• Speech Recognition
Rule-Based
Decisions
• Decision Trees
• Rule Induction
• Expert Systems
• Rule Strategies
Genetic Algorithms
• Model Evolution (crossovers & mutations)
• Directed-Random Search
• Hyperparamter Configuration
• Fitness Function
Artificial
Intelligence
• Decision Processing
• Adaptive Learning
• Digital/Mechanical
Integration
• Feature Engineering
• NLP
• Bayesian Networks
• K-Network
• SVM Network
• XGBoost
• Random Forest
• Boosted Trees
• ID3, C4.5
• Chaid, CART
• Model Seeding
• Decision Tree Induction
• Search Techniques
• Genetic Representation
• Feature Selection
• Model Seeding
• Today AI is pervasive and data-
driven, raising the importance of
machine learning
• 100’s of machine learning methods
exist and continue to drive AI
capabilities
• Expert Systems still play a role but
not always a primary role for data-
driven processes.

4. The Evolution of Machine Learning
EXPECTATIONS
TIME
Innovation
Triggers
Peak of Inflated
Expectations
Trough of
Disillusionment
Slope of
Enlightenment
Plateau of
Productivity
20102000’s1990’s1980’s
Desktop Computing
Recurrent NN (Hopfield)
SAS Desktop
Digital Insurgence (IoT)
Intelligent Agents
Reinforcement
Learning
Data Siloes
Poor Data Access
Inadequate Compute Power
Back Propagation
Deep Learning
Deep Blue – Chess Champ
Watson Wins Jeopardy
Facial Recognition
Natural Language Processing
Knowledge-Driven
to Data-Driven
Speech Recognition
Neocognitron (CNN)
GPUs
Random Forest
Deep Face
AlexNet (CNN)
ImageNet
AI Expert Systems (Knowledge Driven)
ID3 – Tree Algorithm
NetTalk
Fall of Expert Systems
High Cost of Expert Systems
Loss of Major AI Funding
Expert System Popularity
Revival of Connectionism
ML Solutions
Incognito
Collapse of Lisp Machines
AutoML is Made Possible
through Expert Systems,
Compute Power, Data
Manipulation and Advanced
Learning Algorithms
AI WINTER

5. An AutoML Process Walk Through
Cleansing/
Analysis
Data
Transformations
Exploratory
Data Analysis
Machine
Learning
Model
Evaluation
Model
Deployment
• Automated Field Analysis.
Field Statistics and
Distributions
• Data Input Using .csv, .txt or
.xlsx Files Only
• Missing Data Imputation
• Partitions Data Into
Train/Test Datasets
SOLVING FOR SINGLE MISSING FIELDS

6. An AutoML Process Walk Through
Cleansing/
Analysis
Data
Transformations
Exploratory
Data Analysis
Machine
Learning
Model
Evaluation
Model
Deployment
• Automated Field Analysis.
Field Statistics and
Distributions
• Data Input Using .csv, .txt or
.xlsx Files
• Modeled Missing Data
Imputation
• Partitions Data Into
Train/Test Datasets

7. An AutoML Process Walk Through
Cleansing/
Analysis
Data
Transformations
Exploratory
Data Analysis
Machine
Learning
Model
Evaluation
Model
Deployment
• Automated Data
Representations
& Entire Set of
Transformations
• Genetic Algorithm
Drives Feature
Selection Sets

8. An AutoML Process Walk Through
Cleansing/
Analysis
Data
Transformations
Exploratory
Data Analysis
Machine
Learning
Model
Evaluation
Model
Deployment
Neural Network
(Single Layer)
Neural Network
(Deep Learning)
Linear Model
(Regression)
Neural Network
(Multi-Class & SOM)

9. An AutoML Process Walk Through
Cleansing/
Analysis
Data
Transformations
Exploratory
Data Analysis
Machine
Learning
Model
Evaluation
Model
Deployment
Primary Process
Builds hundreds of
models using pre-
identified feature sets
using Cascade Learning
and default hyper-
parameters
Secondary Process
• Runs the Primary Process
with a GA driving hyper-
parameters interations
• Store top 100 performing
models and uses a GA for
final ensemble testing

10. Model Deployment Options
Embedded
Application
Python, “C”
Flashcode
Model Deployed in
MSExcel
An AutoML Process Walk Through
Cleansing/
Analysis
Data
Transformations
Exploratory
Data Analysis
Machine
Learning
Model
Evaluation
Model
Deployment
Model Deployed
Through a Web
Service

11. Thank You
Gene Ferruzza
ferruzzag@Valassis.com
PyData
Triangle