//Abstract

Starting the AI adoption with AI Proof-of-Concepts (PoCs) is the most common choice for most companies. Yet, a significant percentage of AI PoCs do not make it into production whether they were successful or not. Furthermore, running yet another AI PoC follows the law of diminishing returns in various aspects. This talk will revolve around this theme.

//Bio

Oguzhan "Ouz" Gencoglu is the Co-founder and Head of AI at Top Data Science, a Helsinki-based AI consultancy. With his team, he delivered more than 70 machine learning solutions in numerous industries for the past 5 years. Before that, he used to conduct machine learning research in several countries including the USA, Czech Republic, Turkey, Denmark, and Finland. Oguzhan has given more than 40 talks on machine learning to audiences of various backgrounds.

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Timestamps:

[00:00] Introduction to Oguzhan Gencoglu

[00:47] Ouz's background

[01:47] Recurring/repetitive problem patterns

[03:16] "When you solve a repetitive task in an automatic way, that's Scalability."

[04:32] Evolution expected of Machine Learning

[05:10] "People are quite confused about the titles and what's worst, those titles don't have a common definition in different companies. If you feel a little bit overwhelmed, that's normal."

[08:04] Proof-of-Concepts

[10:35] Successful PoCs but not Productionized

[16:03] Productionize as soon as possible

[16:47] "In your Proof-of-Concepts, it's not only technical, but it's also a mindset."

[20:00] Framework of a successful PoCs

[24:28] Taking too much on PoCs

[28:05] Proof-of-Concepts after Proof-of-Concepts and Proof-of-Concepts hell

[31:30] Wholistic view

[34:00] Operationalizing PoCs

[37:17] "The teams also need to adjust themselves to these new tools, new paradigms, and the different needs of the whole industry."  

[37:26] Horror stories

[39:54] Open communication tips

43:31] "Open communication should not only be from the technical perspective but also down to the business and strategy perspective."

[44:20] Translation tips

[44:39] "I believe the most crucial part of today's ML scientists' role is not building a machine learning model but translating a real-life problem into a machine learning problem. It's crucial because it's a scarce talent and skill."

[49:30] Realistic budget for small PoCs

[50:18] "You need at least 1 month of work of proof of value but that doesn't mean things will go to production."

[51:40] Understanding the questions fully

[52:55] "That translation skill is the greatest skill to have in this industry because you can't auto ML that or whatever. It stands the test of time because that will be needed all the time."