KNIME Data Science Learnathon: From Raw Data To Deployment - Dublin - June 2019

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This document discusses the data analytics process from data preparation to model deployment. It describes how data is partitioned into training, validation, and test sets. It also outlines the typical steps in data preparation, model training and optimization, and deployment. Examples of classification problems and datasets are provided to illustrate applying this process. The document promotes resources from KNIME for learning and connecting with the community.

Software

© 2019 KNIME AG. All rights reserved.
From Raw Data to Deployment
KNIMEr: Kathrin.Melcher@knime.com
KNIMEr: Maarit.Widmann@knime.com
@KNIME

© 2019 KNIME AG. All rights reserved.
Do you recognize this?
2
https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining

© 2019 KNIME AG. All rights reserved.
Let’s unroll it!
It always starts
with some data …
3
Data
Preparation
Model
Training
Model
Optimization
Deployment
Data Manipulation
Data Blending
Missing Values Handling
Feature Generation
Dimensionality Reduction
Feature Selection
Outlier Removal
Normalization
Partitioning
…
Model Training
Bag of Models
Model Selection
Ensemble Models
Own Ensemble Model
External Models
Import Existing Models
Model Factory
…
Parameter Tuning
Parameter Optimization
Regularization
Model Size
No. Iterations
…
Performance Measures
Accuracy
ROC Curve
Cross-Validation
…
Files & DBs
Dashboards
REST API
SQL Code Export
Reporting
…
Model
Evaluation

© 2019 KNIME AG. All rights reserved.
The many Lives of a Dataset
4
Data
Preparation
Model
Training
Model
Optimization
Model
Evaluation
Deployment
Partitioning:
• Training Set
• Validation Set
• Test Set
Training Set Validation Set Test Set New Data from Real
World Applications
Original Data
Set with Past
Observations

© 2019 KNIME AG. All rights reserved.
Data Exploration
• Sometimes in between Data Access and Data
Preparation there is a Data Exploration phase
• The Data Exploration phase is useful to get to
know the data
• KNIME offers a few visualization nodes to build
dashboards to explore the data
5

© 2019 KNIME AG. All rights reserved.
What about Big Data?
• Big Data serves Scalability
• The whole Analytics Process is no different on
Big Data
• You need:
– a Big Data Platform
– The KNIME Big Data (Spark & Hive) Extension
6

© 2019 KNIME AG. All rights reserved.
One Example for Every Need – on KNIME EXAMPLES Server
The KNIME EXAMPLES Server
7
50_Applications

© 2019 KNIME AG. All rights reserved.
Classification Problem & Data Set
• Airline Dataset: http://stat-computing.org/dataexpo/2009/the-data.html
• Smaller dataset (Jan 2007) (AirlineDataset.table)
• Challenge:
Predict Departure Delays
If on original airline dataset, only flights from airport ORD
Output Class = “delay” if depdelay > 15min
otherwise “no delay”
Available features: date, dep time, arr time, carrier, destination, cancelled, …
14

© 2019 KNIME AG. All rights reserved.
Challenges
• Group 1. Data Access and Data Preparation
• Group 2. ML Model Training
• Group 3. Model Deployment
• Import file Learnathon_2019.knar into your workspace
15

© 2019 KNIME AG. All rights reserved.
Group 1. Data Access and Data Preparation
16

© 2019 KNIME AG. All rights reserved.
Group 2. Model Training & Optimization
17

© 2019 KNIME AG. All rights reserved.
Group 3. Deployment
18
• Deployment Options – Multiple challenges:
– Workflow deployment to KNIME Server
– Remote/Scheduled execution from KNIME
Server
– KNIME RESTful Web Services
– Build a Composite Interactive Dashboard and
make it available on KNIME Web Portal
– Generate a report with BIRT
– Write Prediction Results to a Database

© 2019 KNIME AG. All rights reserved.
KNIME Fall Summit 2019
November 5 – 8 at AT&T Executive Education and Conference Center,
Austin, Texas
• Tuesday & Wednesday: One-day courses
• Thursday & Friday: Summit sessions
Register by October 1 for
10 % Early Bird Discount
with this code:
LEARNATHON-DUBLIN
Register at
knime.com/summits

© 2019 KNIME AG. All rights reserved.
KNIME Beginner’s Luck
Free Copy of KNIME Beginner’s Luck Book from KNIME Press
https://www.knime.com/knimepress
with this code: DUBLIN-0619
20

© 2019 KNIME AG. All rights reserved.
Stay connected with KNIME
Blog: knime.com/blog
Forum: forum.knime.com
KNIME Hub: hub.knime.com
Follow us on social media:
KNIME E-Learning Course:
www.knime.com/e-learning-course

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KNIME Data Science Learnathon: From Raw Data To Deployment - Dublin - June 2019

3. © 2019 KNIME AG. All rights reserved. Let’s unroll it! It always starts with some data … 3 Data Preparation Model Training Model Optimization Deployment Data Manipulation Data Blending Missing Values Handling Feature Generation Dimensionality Reduction Feature Selection Outlier Removal Normalization Partitioning … Model Training Bag of Models Model Selection Ensemble Models Own Ensemble Model External Models Import Existing Models Model Factory … Parameter Tuning Parameter Optimization Regularization Model Size No. Iterations … Performance Measures Accuracy ROC Curve Cross-Validation … Files & DBs Dashboards REST API SQL Code Export Reporting … Model Evaluation

4. © 2019 KNIME AG. All rights reserved. The many Lives of a Dataset 4 Data Preparation Model Training Model Optimization Model Evaluation Deployment Partitioning: • Training Set • Validation Set • Test Set Training Set Validation Set Test Set New Data from Real World Applications Original Data Set with Past Observations

5. © 2019 KNIME AG. All rights reserved. Data Exploration • Sometimes in between Data Access and Data Preparation there is a Data Exploration phase • The Data Exploration phase is useful to get to know the data • KNIME offers a few visualization nodes to build dashboards to explore the data 5

6. © 2019 KNIME AG. All rights reserved. What about Big Data? • Big Data serves Scalability • The whole Analytics Process is no different on Big Data • You need: – a Big Data Platform – The KNIME Big Data (Spark & Hive) Extension 6

8. © 2019 KNIME AG. All rights reserved. Classification Problem & Data Set • Airline Dataset: http://stat-computing.org/dataexpo/2009/the-data.html • Smaller dataset (Jan 2007) (AirlineDataset.table) • Challenge: Predict Departure Delays If on original airline dataset, only flights from airport ORD Output Class = “delay” if depdelay > 15min otherwise “no delay” Available features: date, dep time, arr time, carrier, destination, cancelled, … 14

9. © 2019 KNIME AG. All rights reserved. Challenges • Group 1. Data Access and Data Preparation • Group 2. ML Model Training • Group 3. Model Deployment • Import file Learnathon_2019.knar into your workspace 15

12. © 2019 KNIME AG. All rights reserved. Group 3. Deployment 18 • Deployment Options – Multiple challenges: – Workflow deployment to KNIME Server – Remote/Scheduled execution from KNIME Server – KNIME RESTful Web Services – Build a Composite Interactive Dashboard and make it available on KNIME Web Portal – Generate a report with BIRT – Write Prediction Results to a Database

13. © 2019 KNIME AG. All rights reserved. KNIME Fall Summit 2019 November 5 – 8 at AT&T Executive Education and Conference Center, Austin, Texas • Tuesday & Wednesday: One-day courses • Thursday & Friday: Summit sessions Register by October 1 for 10 % Early Bird Discount with this code: LEARNATHON-DUBLIN Register at knime.com/summits

15. © 2019 KNIME AG. All rights reserved. Stay connected with KNIME Blog: knime.com/blog Forum: forum.knime.com KNIME Hub: hub.knime.com Follow us on social media: KNIME E-Learning Course: www.knime.com/e-learning-course

16. © 2019 KNIME AG. All rights reserved. The KNIME® trademark and logo and OPEN FOR INNOVATION® trademark are used by KNIME.com AG under license from KNIME GmbH, and are registered in the United States. KNIME® is also registered in Germany. Thank You! #KNIME #Learnathon

KNIME Data Science Learnathon: From Raw Data To Deployment - Dublin - June 2019

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KNIME Data Science Learnathon: From Raw Data To Deployment - Dublin - June 2019