MarketingSmart Cloud

Building real-time predictive apps from small data vs big data: two use cases

PAPIs Connect

Athmane HAMEL & Cédric HERVET

May 21st, 2015

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• Introduction: general concepts

• 1st use case with small data: target sales

• 2nd use case with big data: failure detection

Summary

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Two steps

Machine Learning Apps - general concepts

Learning• Use historical data to build a predictive model

• A predicted variable & a set of prediction variables

Prediction• Use the prediction model to make predictions

• Back-end App Or Front-end App

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Real-time Predictive Analytics, does that mean:

Make predictions using a model?

• This is the most common use

Build the predictive model in real-time?

• Model building involves rigorous experimentation, sufficient historical data. It is time consuming process

Update the model in real-time?

• Refresh a model implies other issues: use fresh data, replace existing data and keep the same learning dataset size, use more and

more larger dataset?

Even technologies make it possible, is there a benefit in doing so?

• The model does not match with just one data point

• Yes, we can do! but it’s necessary to define railing rules based on domain expertise (or experts)

Real-time: training or predictions?

Training data

Test data Hypothesis

Machine Learning Algorithm

Performance

Feedback

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• Learn from little is possible• Create new data to enrich the data collection

• Choosing the good model is critical

• Well-defined objectives

• What about Big data • Large volumes and types, large objectives set, large models

library

• Machine learning Algorithms = Oil for knowledge discovery

• Increasing “Open Data” sources

• Additional steps for data integration

• Data extraction: using unstructured datasets, we have to create structured data

• Aggregation step: allowing the creation of new quantitative data

• Evolving objectives in respect with own data knowledge

With which data?

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http://www.np6.co.uk/contact-request/

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