Seminar: "Intelligently Automating Machine Learning, Artificial Intelligence, and Data Science Processes" Ali Alkan | Kadir Has Univ. | 07 Dec. 2018

1. Intelligently Automating
Machine Learning, Artificial
Intelligence, and Data
Science Processes
Ali ALKAN
Co-Founder & Principal Data Scientist
ADVANCETICS B.V.
ali.alkan@advancetics.com
Twitter / Ali_Alkan
7 December 2018

2. Agenda
Machine Learning, Artificial Intelligence, and Data Science
Phases of Data Science Projects and CRISP-DM
Guided Analytics Approach for Data Science Processes
A Guided Analytics Application with KNIME Analytics Platform
Q&A Session

3. ML vs. AI vs. DS?
Data Science produces insights
Machine Learning produces predictions

4. ML vs. AI vs. DS?
Data Science produces insights
Machine Learning produces predictions
Artificial Intelligence produces actions

5. What is Artificial Intelligence?
• Artificial Narrow Intelligence (ANI): Machine
intelligence that equals or exceeds human
intelligence or efficiency at a specific task.
• Artificial General Intelligence (AGI): A machine
with the ability to apply intelligence to any
problem, rather than just one specific problem
(human-level intelligence).
• Artificial Superintelligence (ASI): An intellect that
is much smarter than the best human brains in
practically every field, including scientific
creativity, general wisdom and social skills.

6. Machine Learning | Introduction
• Machine Learning is a type of Artificial Intelligence that provides
computers with the ability to learn without being explicitly programmed.
• Provides various techniques that can learn from and make predictions on
data.

7. Machine Learning | Learning Approaches
Supervised Learning: Learning with a labeled
training set
• Example: email spam detector with training set
of already labeled emails
Unsupervised Learning: Discovering patterns
in unlabeled data
• Example: cluster similar documents based on
the text content
Reinforcement Learning: learning based on
feedback or reward
• Example: learn to play chess by winning or
losing

8. Outlook | Traditional Programming

9. Outlook | Machine Learning

10. Outlook | Goal-based AI

11. CRISP - DMCross Industry Standard for Data Mining

12. The CRISP-DM methodology provides a
structured approach to planning a data mining
project.
It is a robust and well-proven methodology.
It is powerful practical, flexible and useful
when using analytics to solve business issues.
This model is an idealised sequence of events.
In practice many of the tasks can be performed
in a different order and it will often be
necessary to backtrack to previous tasks and
repeat certain actions.
CRISP-DM | Definition

13. CRISP-DM | Business Understanding
The first stage of the CRISP-DM process
is to understand what you want to
accomplish from a business
perspective.
The goal of this stage of the process is to
uncover important factors that
could influence the outcome of the
project.
Neglecting this step can mean that a
great deal of effort is put into producing
the right answers to the wrong questions.

14. CRISP-DM | Data Understanding
The second stage of the CRISP-DM
process requires you to acquire the data
listed in the project resources.
This initial collection includes data loading,
if this is necessary for data understanding.
• For example, if you use a specific tool for
data understanding, it makes perfect
sense to load your data into this tool.
• If you acquire multiple data sources then
you need to consider how and when
you're going to integrate these.

15. All steps from the raw data to the final dataset
Final dataset:
used for statistical modeling
sometimes called ADS (analytical dataset)
Includes or can include:
• data source selection and loading
• table selection and loading
• joining data sources
• data cleaning (missing values, outliers, ...)
• feature generation and data transformation
• taking samples of data
• …
CRISP-DM | Data Preparation

16. CRISP-DM | Modeling

17. CRISP-DM | Evaluation

18. CRISP-DM | Deployment

19. CRISP - DM
Cross Industry Standard for Data Mining
80 - 20 Rule!
Time Consuming : %20
Success Factor : %80
Source: Berthold, Borgelt, Höppner, Klawonn: Guide to Intelligent Data Analysis, Springer 2011

20. Sharing Tools
Sharing Skills
Sharing Responsibility
A new generation of tools
They can build their own reports
A recipe for disaster
Data is viral - everybody wants it
Start small and just do it

21. Source: Phil Winters
Machine
Learning

22. Guided Analytics

23. Guided Analytics | Introduction
• Systems that automate the data science cycle
have been gaining a lot of attention recently.
• Those tools often automate only a few phases
of the cycle, have a tendency to consider just a
small subset of available models, and are limited
to relatively straightforward, simple data formats.
• Automation should not result in black boxes,
hiding the interesting pieces from everyone; the
modern data science environment should allow
automation and interaction to be combined
flexibly.

24. Guided Analytics | Definition
• Allowing data scientists to build
interactive systems, interactively
assisting the business analyst in her
quest to find new insights in data and
predict future outcomes.

25. Guided Analytics | Definition
• We explicitly do not aim to replace the
driver (or totally automate the process) but
instead offer assistance and carefully
gather feedback whenever needed
throughout the analysis process.
• To make this successful, the data scientist
needs to be able to easily create powerful
analytical applications that allow
interaction with the business user
whenever their expertise and feedback is
needed.

26. Guided Analytics | Environments
Openness
Uniformity
Flexibility
Agility

27. Guided Analytics | Environments
Openness:
• The environment does not post restrictions in terms of
tools used – this also simplifies collaboration between
scripting gurus (such as R or Python) and others who just
want to reuse their expertise without diving into their
code.
• Obviously being able to reach out to other tools for specific
data types (text, images, …) or specialized high
performance or big data algorithms (such as H2O or
Spark) from within the same environment would be a plus;
Uniformity
Flexibility
Agility

28. Guided Analytics | Environments
Openness
Uniformity:
The experts creating data science can do it all in
the same environment:
• blend data,
• run the analysis,
• mix & match tools,
• build the infrastructure to deploy this as analytical
application;
Flexibility
Agility

29. Guided Analytics | Environments
Openness
Uniformity
Flexibility:
• Underneath the analytical application, we
can run simple regression models or
orchestrate complex parameter
optimization and ensemble models –
ranging from one to thousands of models.
Agility

30. Guided Analytics | Environments
Openness
Uniformity
Flexibility
Agility:
• Once the application is used in the wild, new demands
will arise quickly: more automation here, more consumer
feedback there.
• The environment that is used to build these analytical
applications needs to make it intuitive for other members
of the data science team to quickly adapt the existing
analytical applications to new and changing
requirements.

31. Guided Analytics | Auto-what?
• So how do all of those driverless, automatic, automated AI or
machine learning systems fit into this picture?
• Their goal is either to encapsulate (and hide!) existing expert data
scientists’ expertise or apply more or less sophisticated
optimization schemes to the fine-tuning of the data science tasks.

32. Guided Analytics | Auto-what?
• Obviously, this can be useful if no in-house data science expertise is available but in
the end, the business analyst is locked into the pre-packaged expertise and the
limited set of hard coded scenarios.
• Both, data scientist expertise and parameter optimization can easily be part of a
Guided Analytics workflow as well.
• And since automation of whatever kind tends to always miss the important and interesting
piece, adding a Guided Analytics component to this makes it even more powerful: we can
guide the optimization scheme and we can adjust the pre-coded expert knowledge to
the new task at hand.

33. Data Sciense Project | Roles
www.sistek.com.tr
• Data scientists
– Workflow development
– Data Analysis
– Model Development
• Business analysts
– WebPortal
– Data Analysis
• IT administrators
– Enterprise Architecture Mngmt
– Cloud solution provider
5.Data Science Project –Roles

34. Data Science Project | Data Scientist
www.sistek.com.tr
Responsible for:
• Creating, updating workflows
• Creating, maintaining metanode
templates
• Building, evaluating, monitoring data
and using ad hoc developed
workflows
• Development of WebPortal
applications
5.Data Science Project – Data Scientists

35. Demo

36. About KNIME
KNIME is a software for fast, easy and intuitive access to advanced
data science, helping organizations drive innovation.
KNIME Analytics Platform is the leading open solution for data-
driven innovation, designed for discovering the potential hidden in
data, mining for fresh insights, or predicting new futures.
Organizations can take their collaboration, productivity and
performance to the next level with a robust range of commercial
extensions to Knime open source platform.
For over a decade, a thriving community of data scientists in over
60 countries has been working with Knime platform on every kind of
data: from numbers to images, molecules to humans, signals to
complex networks, and simple statistics to big data analytics.
KNIME’s headquarters are based in Zurich, with additional offices
in Konstanz, Berlin, and Austin.

38. Chicago O'Hare International Airport (ORD)

39. Guided Analytics | Design
The workflow defines a fully automated web based application to
select, train, test, and optimize a number of machine learning
models.
The workflow is designed for business analysts to easily create
predictive analytics solutions by applying their domain knowledge.
Each of the wrapped metanodes outputs a web page with which the
business analyst can interact.

40. Guided Analytics | Application

41. Sources
๏ Christian Dietz, Paolo Tamagnini, Simon Schmid, Michael Berthold: Intelligently
Automating Machine Learning, Artificial Intelligence, and Data Science,
https://www.knime.com/blog
๏ Berthold, Borgelt, Höppner, Klawonn: Guide to Intelligent Data Analysis, Springer 2011
๏ Michael Berthold: Principles of Guided Analytics, https://www.knime.com/blog
๏ Ali Alkan: Veri Madenciliği Teknikleri, Eğitim Notları 2017
๏ Ali Alkan: İleri Analitik Teknikler Seminerleri 1-2-3-5-6-7, Seminer Notları 2016-17

42. Ali ALKAN
Twitter @Ali_Alkan
ali.alkan@advancetics.com
Thank you!