Dataiku is an online data science platform that can be run on a server or local virtual machine. It allows both business users and data scientists to work with data and build machine learning models. For business users, it requires little coding experience. For data scientists, it provides reusable data pipelines and focuses on algorithm development. The platform includes features like data import, projects containing flows, labs, and dashboards. Flows are produced from labs, which define functions, and recipes, which are produced from labs. While efforts to democratize machine learning are beneficial, data scientists must adapt and develop both specialized and generalist skills to remain versatile in their roles.

1. Dataiku Data Science Studio
BP Town Hall
20 March 2019
Norman.Poh@bjss.com

2. What is dataiku?
An online data science platform
Run on server or local VM. Work uniquely
with http://127.0.0.1:10000
For business users (‘clickers’):
Doing data science with very little coding
experience
Another attempt to democratise machine
learning
For data scientists (‘coders’):
Define a data (wrangling) pipeline once and
use everywhere
More efficient codes
Focus on algorithm development
Easy scoring (provided you use one of the
algorithms)

3. The platform

4. Data import

5. Project contains Flow, Lab, Dashboard,…

6. Flow
Produced by
Lab
RecipesRecipesRecipes
Produced by
Visual Analyses
or Labs

7. Recipe
(Lab)
Where you define your ‘function’
or ‘procedure’
(A Lab produces a Recipe)

8. What about coders?

9. My opinion?
Another effort to democratise machine learning technologies
• Good news for anyone who want to access ML technologies
• Embrace the technology and adapt quickly
• Go T-shape – Be industry-specific whilst remaining generalist
• Versatile – Team working: data engineers and analytics developers
https://www.forbes.com/sites/forbestechcouncil/2019/03/01/radical-
change-is-coming-to-data-science-jobs/#1696e1a4dfcc
“I believe the job of data scientist as we know it today will be
barely recognizable in five to 10 years.”

10. Examples of similar efforts
reducing tedious data
preparation work:
• Trifacta, Element Analytics, Kylo
automating algorithm selection
and parameter tuning
• Auto-sklearn, DataRobot
doing data science with
graphical user interface:
• Orange, KNIME, SPSS Modeler, Azure ML
Studio
working with massive data:
• Kafka, Spark, Hive/Impala
building dashboards quickly
• Tableau, Power BI, QlikView