Wolfram Private Cloud: Rollout of AWS API Based Micro-Services for AdTech

Bryan M. Minor, Ph.D.
Chief Scientist, Acquisio

Types of Formulations

1.  Algorithm formulations of a simple mathematical nature formulated frequently with various
     input parameters.

2.  Algorithms used to formulate necessary fit parameters used by case #1 that require more 
     extensive mathematical formulations and models. Typical characteristic time between
     updates much longer than case #1.

Micro-Service Algorithms using Wolfram Cloud on AWS

1.  Ability to leverage mathematics and machine learning libraries of Wolfram Research
    (Mathematica)

2.  Accelerating development process due to extreme decoupling of core algorithms away from
     Optimization service code.

3. Expanded ability to test and learn in Production environment.

Architecture of API Micro-service Algorithms

Formulations on Wolfram Cloud

Examples of types of formulations in the Wolfram Cloud:

1.  LookBack interval for each campaign’s devices (Mobile, Other)

2.  CPC target for each campaign’s devices (Mobile, Other)

3.  Daily Budget of each campaign

4.  Classification of campaign advertising categories

5.  Analysis of results collected each day

Algorithm Software Development (before)

1.  Design algorithm prototypes in Mathematica

2.  Produce algorithm documentation for review by science team

3.  Design documentation for software redevelopment, typically for a non-mathematics literate
     software developer (weeks)

4.  Implementation in production software language of choice (C#, Java,...) (1 month)

5. Rack testing (read-only) testing of algorithms in Dev testing environment (1 month)

6.  Deployment of algorithm to Production, first real testing (weeks)
	a. No easy reverse path

7.  QA testing in Production of new algorithm (weeks)

Algorithm Software Development (now on WC)

1.  Design algorithm prototypes in Mathematica with Wolfram Language

2.  Produce algorithm documentation for review by science team

3.  Integrating with existing service API (week)

4.  Rack testing (read-only) testing of algorithms in Development testing environment (week)

5.  Deployment of algorithm to Production, first real testing. This is totally independent of
     normal software Production deployment, just a database endpoint setting change.
	a. Immediately reversible if problem occurs
	b. Limited to a very small number of test cases

Google AdWords - 3 Stage Updater per Device

show["AdWords"];

Bing - 3 Stage Updater per Device

show["Bing"];

Yahoo!Japan - 3 Stage Updater per Device

show["YahooJapan"];

Campaign Optimization - Small case

Budget group stats

GetGraphsForVA[70763,2,startDate,endDate]

Campaign Optimization - Large case

Budget group stats

GetGraphsForVA[71124,4,startDate,endDate]

({{\[Placeholder], , }, {, , }, {, , }, {, , }, {(Legended[, (SwatchLegend["Dead (6.98% )", "Not Classified (0.% )", "Alive (93.02% )"])]), , }})

Before and After BBM

fBuildGCanPickHistogramDates[70769,"2016-04-01","2016-08-05","2016-08-06",endDateBBM]

Campaign Category Classification

({{\[Placeholder], , }, {(Legended[, (SwatchLegend["Physiotherapy"])]), (Legended[, (SwatchLegend["Physiotherapy", "Nursing Home", "Rehab Center", "Psychologist", "Unknown"])]), (Legended[, (SwatchLegend["Physiotherapy", "Nursing Home", "Rehab Center"])])}})

Wolfram Cloud Conclusions

1.  3 times faster implementing algorithms in Production

2.  Significantly increases the number algorithms being tested and supported in Production
     	a.  Algorithms are only testable in Production

3.  No longer need non-mathematics trained software developers implementing algorithms
	a. Algorithm implemented once and used everywhere

4.  Maximizes leveraging Wolfram language Mathematica libraries in Production environment

5. Greatly extends post-analysis capabilities by leveraging WC

6.  Decouples most complex computations from normal software development cycles

7.  Allows scaling of algorithms using WC and AWS

Industry insights - Data Science