Already Production : Deep Learning on temporal and spatial | text | time series data.

1. PureML/DLProjectsdoneby
my own
-QinzheZhang

2. ProductionExample-1
-Advance modelbuilding example @Prospa
Business Problem:
 Based on internal data, build a model which could predict repayment performance of a specific industry
within a given area at a particular time
Challenges:
 No any idea or directions when received this project
 No any similar situation at Prospa before
 Most of traditional model(s) only use temporal information, without considering spatial information
simultaneously

3. ProductionExample-1
-Advance modelbuilding example @Prospa
Solution:
Why this is good work example,
• Quickly find a very good solution from academic researched
(AAAI 18) which conducted by DiDi(Chinese UBER) in 1
week
• Reproduce it(no code provided in that paper) and validate it
performance by comparing with three other traditional
methods in 2 weeks
• Made validated improvement to original method
• The attitude of embracing challenge, especially start from
scratch to an end to end solution in 4 weeks
https://github.com/ivurs/DL_CNN_LSTM/blob/master/ModelCompare/Project%20Documentation.ipynb

4. ProductionExample-2
-NLP modelforspeeding uprealtime decisioning @Prospa
Business Problem:
 Bank transaction classification(income / non-income) mainly relies on human which affects approval speed
 Existing model is combination of keywords-specified rules which,
• cannot make decision when keywords change or new one comes
• very inefficient to update keywords and rules, as well as version management
Challenges:
 Build up end to end solution which accurate enough to minimize human effort
 Represent and modelling text data in a new way which can reach best performance
 Have to understand whole credit risk side business in very short time and design validation
metrics in model evaluation

5. ProductionExample-2
-NLP modelforspeeding uprealtime decisioning @Prospa
Solution
Why this is good work example,
• Bring up state-of-the-art language model to Prospa which
can achieve very good modelling performance
• Working very well and being very focus in this tight deadline
project and meet high standard criteria
• Demonstrate ownership when implement it with product and
engineering team
• Quickly learn and understand credit risk side business
concepts and well applied these pieces of knowledge

6. ProductionExample-3
-SOTA modelformultiple time series forecasting@Prospa
Business Problem:
 Forecasting customers‘ business performance in a FUTURE PERIOD
 This problem is a “Pain” of Prospa for a while(at least for 1 year)
Challenges:
 Traditional algorithm only predict single step(e.g., ARIMA, MA)
 Not allow multiple time series forecasting, i.e., you can only analysis one time series by one
 No very friendly to add additional features, i.e., consider other features along with time
series itself.

7. ProductionExample-3
-SOTA modelformultiple time series forecasting@Prospa
Solution
Why this is good work example,
• Solve a business problem which existing for a long period
• Enable us to use this kind of mechanism in many similar
scenarios
• Better understand our customers’ potential
• Better serve our customer by considering their servility in
the future
• Provide a new view of how we segment our customers.

8. ProductionExample-3
-SOTA modelformultiple time series forecasting@Prospa
What is probability forecasting looks like, it outputs a range,
rather than only a single number