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Design SDLC - Software Development Life Cycle


ALC type3 Low code Analytics, Business

Automatization of decisions using ICT.

Vmap layers choices ALC type3, BPM ALC type3 security Changing an organizaton can have big impact, even change the core business during transitions.

At the end of the day, there is no obligation to things as they always have been done. Switching back to small research is an option.

🔰 Most logical back reference.

Contents

Reference Topic Squad
Intro Automatization of decisions using ICT. 01.01
V3onV2 Building on the ALC-v2 into an ALC-v3 proces. 02.01
alcv3_1 ALC-v3, 1 data provide. 03.01
alcv3_2-3 ALC-v3, 2 - 3 ABT & model. 04.01
alcv3_4-5 ALC-v3, 4 - 5 Delivery & model information. 05.01
What next Step by step, Travelling the unexplored. 06.00
Following steps 06.02

Combined links
Combined pages as single topic.
👓 deep dive layers , VMAP
👓 Multiple Dimensions by layers
👓 ALC type2 Business Apllications - 3GL
🚧 ALC type3 Low code Analytics, Business
🕶 ALC type3 Security Access (Meta)


Progress Conversion


Building on the ALC-v2 into an ALC-v3 proces.

The business process life cycle has three moments where release are implemented: 🕒 IT input -Data prepare (DS) releasing to DS 🎭
🕕 DS 🎭 proces - releasing: abt,models to IT approved by BU ⚙ (⚖)
🕘 IT Score- deliver (BU) operational to BU informing DS ⚖ 📚 🎭


The moving circle with four stages.


The difernce between ALC-v2 and ALC-v3 is going from a single code line into managing multiple ones. All promotion lines and release lines: applicable to release management according the ALC type2 3gl software development lifecycle.
Just doing the same thing more often.
Vmap layers choices
Promotion lines bij dashed vertical green:
1 input data,
2 abt,
3 score model.


Release lines to add for:
4 report score,
* validations,
5 delivery score,
* monitoring.



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ALC-v3, 1 data provide.

Not quite classical data preparation (ETL/ELT). The data doesn't need to be in a 3nf star diagram (one fact). ER-star,allowing not relevant data in dimensions is sufficiënt.

retrieve & verify
Involved proces steps are:
-00- ⚙ Input data from the dwh can be mass data.
Attention: performance issues while processing them.
-10- ⚙ Input data from direct resources (streaming lambda) should be able to process fast enough not using too much resources.
The performance to be aligned with mass data processing.
-80- ⚙ Validation of the input data (ER relations star):
  1. logical number of changes within expectations
  2. Differences with original source systems.
Notes:

The first bundle for release management: three layers building on the previous production version.


Parallel development - test
The development of the input data connections, data mart - score mart can connect to test (fake) business data. The technical connectivity and data descriptions (metadata) are functionalities in scope.
Builing models (data mart) is only possible using real production data extracts. As a result an analytics environment is needing parallel development, parallel testing.
DS 🎭 DS Models (D) on delivered IT validated data (datamart - score mart = T)
IT IT Develops "score reporting", delivery using the ABT and model code and data (score mart = T) handed over from DS. The score delivery is developed and tested with a Test business data connection

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ALC-v3, 2 - 3 ABT & model.

Model development & releasing:

Involved proces steps are:
-20- 🎭 Optional: split iteration start.
-30- 🎭 Data preparation, features.   (first vertical line)
-40- 🎭 Scrings (decision logic).
-50- 🎭 Optional: split iteration end.    (second vertical lines)
The second release management bundle: three layers building on the previous production version.
Involved proces steps are (continued):
-60- ⚙ Prepare data, documents. Change detection current - previous results.
-70- ⚙ Archive changed score results by cumulation cases.
    Marked on timestamps and score-model versions.
-80- ⚙ Validate results: logical value expectations, number of changes.
These are not visible as green vertical lines, marked activities (bottom right).

Parallel development - test
The metadata for "edata provide" and the model developping is to be segregated.
parallel develop parallel develop
cooperation:
parallel development
parallel test

An easy wasy to do that is wihtin the shared devlopement. Duplicating those data defintions and connecting them by dummy copy jobs, completing data lineage.
A good collaboration between server (analytics operations) and sas mining (analytics development) is a prerequisite for planning the scheduling of the processes.

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ALC-v3, 4 - 5 Delivery & model information.

Model deployment & running:
Involved proces steps are:
-80- ⚙ Validation of results (interation optional).
-90- ⚙ ⚖ 📚 🎭 Preparing datasets documents extracted from score resulst and previous run results. Change detection.
Delivering operational score results is a shared responsability.

These are not visible as green vertical lines, marked activities inside circles.
The third release management bundle:
Score model deleivery.

Model explanation

The production data - datamart being used for the modelling should be archived wiht the modelling tool version wiht all intermediate steps.
💣 The sizing of the data makes it impossible to do that. The size is easily 10-60Gb or far higher. Release management tools used with code artifact are not able to support this. A backup/restore approach is realizable.

right-metric-evaluating ML Basic model performance:
right-metric-evaluating ML (kdnuggets confusion matrix).
There are many metrics to explain what is the balance. The same kind of numbers to be made using the operational score results for the real perfomance (BI Business Intelligence).
Alternatives:
dmg org  logo. dmg org (4.3 model explanation.) PMML provides applications a vendor-independent method of defining models so that proprietary issues and incompatibilities are no longer a barrier to the exchange of models between applications. It allows users to develop models within one vendors application, and use other vendors applications to visualize, analyze, evaluate or otherwise use the models.

MLmatters (wagstaff) This paper identiļ¬es six examples of Impact Challenges and several real obstacles in the hope of inspiring a lively discussion of how ML can best make a difference. Aiming for real impact does not just increase our job satisfaction (though it may well do that); it is the only way to get the rest of the world to notice, recognize, value, and adopt ML solutions.

Step by step, Travelling the unexplored.

 horse sense
Challenge: remaining at human only invented decisions.
The ALC-v3 is more complicated as giving an order to creating change code (software). The decision on simple required business logic is moving away from human decison makers. Those changes are disturbing for decision makers.

Challenge: human understandable explanations of ML decisions.
The underpinnig of human guided machine learning decisions is not well settled. Neither by ICT neither by business, neither by data-scientists. The acceptance of profiling is a public generic aversions. Legal guidelines when natural persons are involved are mentioned eg in the GDPR.

Challenge: change in sizing computer resources, technical performance & tuning.
The old paradigm was that machines for operational production are better than the ones used at development - test. When the hardware was much more expensive than human working hours, software was human coded, that was true.
The machines used for modelling are needing far more computer resources than at production layers. As the hardware has become cheap, the cost argument is gone. The isse: technical design paradigma´s not adjusted conform modern requirements.

Unexplored joureney Enterprise
Transforming processes.
Change is a the only constant factor of a journey. Never knowing for sure what is next. Changing fast is exploring where no one has gone before.
The people around ICT are a different kind of species than the ones running the business. Understanding the business during fast transitions is another world.

Combined pages as single topic.
Combined links
👓 deep dive layers , VMAP
👓 Multiple Dimensions by layers
👓 ALC type2 Business Apllications - 3GL
ALC type3 Low code Analytics, Business
🕶 ALC type3 Security Access (Meta)


🔰 Most logical back reference.



⚒    Intro     V3onV2     alcv3_1     alcv3_2-3     alcv3_4-5     What next     ⚒ 👐    top bottom   👐
📚    BPM    SDLC    BIAanl    Data    Meta    Math    📚 👐 🎭 index - references    elucidation    metier 🎭

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