Ciprian Jichici

General Manager, Genisoft

With host Andrew Brust

Market Strategy Advisor, Io-Tahoe

CEO, Blue Badge Insights

Operationalized AI and

machine learning:

challenges and possible

solutions

For external use

Speaker bios

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Ciprian Jichici (“SIPrian ZEEkitch”)

• Software development virtuoso

• Technology consulting veteran

• PhD in Machine Learning, ABD

Andrew Brust

• Covers Big Data and analytics for ZDNet

• Strategy Advisor to Io-Tahoe

• Data-focused tech career started in 1985

AI is real; those who claim

it’s operational are

disingenuous

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For external use

What makes enterprise AI difficult?

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AI dev differs from mainstream dev in terms of skills sets,

development environments, practices and culture

Management of the code differs significantly

Results in huge drags in productivity, success, adoption

For external use

Anatomy of a

real project

For external use

Four teams

Data science

Data platform

API

Front-end portal

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• Setting: A customer behavior analysis project for large corp. (over €1B in

revenues)

• Not one dev team, but four:

For external use

App and data teams: a rough interface

Versioning, for data science code and

data itself

Integrating data science code and application code:

Source control and build

Aligning infrastructures

API code must call into data science

code

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For external use

Distilling the

challenges

For external use

Versioning

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The data science software stack is prone to churn and versioning

entropy

Data scientists must be tolerant of such conditions

As a result, getting them to conform to versioning guidelines is hard

Culture clash: what’s de riguer for app devs is also alien (or at least

fussy) for data scientists

For external use

Other issues

Integrating data

science code and

application code

Experimental

versus

production

approach

Source

control and

build

Aligning

infrastructures

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For external use

The burden of choice

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Implementation simplicity

Data scientists have so many

deployment choices

Have to move quickly

Pick the simplest to implement

Manageability

Implementation simplicity often

misaligned with manageability

in conflict with automation

in conflict with process management

For external use

Possible

solutions

For external use

Possible Solutions

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Manageability

Jupyter notebooks as

containers for versioning

Tooling for deployment,

testing, retraining must

meet enterprise standards

and integrate with cloud

and container platforms

Stabilize frameworks and

packages

Can we have data science

“distributions” as we do for

Hadoop, etc.?

Platform churn

Visual Studio Code,

Eclipse, PyCharm, IntelliJ

Algorithm selection,

hyperparameter tuning

automated

AI capabilities in

mainstream dev tools

For external use

The most usable AI is embedded AI

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Models are built, trained, deployed

Code integration is done

Retraining is automated

Little to no skill set debt

Flux in platform is vendor’s burden, not yours

Thank you

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