Developing an AI First Draft instead of an AI MVPs, an approach to incremental usefulness. This work pushes the concept of "First draft instead of Minimum viable product" when it comes to an AI related project. This is mainly because an AI MVP may never see the light if we are looking for a "Viable" first version. There are some design principles and lessons that I have learned from industry, academia, and the startup world.

1. Changing paradigms in
AI prototyping
From Minimum Viable Product to a First Draft approach
Carlos Toxtli
@ctoxtli

3. Minimum
“Viable”
Product

4. Expectations in AI
Reality
@ctoxtli

5. Why?
● Conventional solutions are deterministic
1 + 1 = 2 (100% sure)
● AI solutions are stochastic (randomness)
There is a cat in the image (86% sure) ???
@ctoxtli

6. So how can we effectively prototype AI?
Lets learn from academia
@ctoxtli

10. In fact, Data scientists currently code in notebooks

11. Hypothesis and baseline definition
You want to prove that your proposed
algorithm is better than existing ones.
Try with the simplest algorithms first.
@ctoxtli

13. Common AI pipeline in Industry
● Collect data (tons of data)
● Pre process data (refine the data to its most
optimal representation)
● Find best approach to implement (usually
the state-of-the-art)
● Implement the algorithm (usually using a
published code) @ctoxtli

14. First Draft AI pipeline
● Collect data (small data but well stratified
and the most discriminative)
● Pre process data (simplest data cleaning)
● Find the simplest approach (use vanilla
versions of algorithms or one-shot learning)
● Implement the algorithm (use a widely and
easy to use framework i.e. sklearn, keras)

16. Quick iterations
● Fine tune it (hyper parameters)
● Try several already implemented ML
algorithms and ensembled models.
● Evaluate and create a benchmark
@ctoxtli

17. Define your error mitigation strategy
● Step 1: Try a mechanism to give a solution when
accuracy is low (i.e. human-in-the-loop, heuristics)
● Step 2: Try both (AI model and mitigation
strategy) triggered by a certain accuracy
threshold.
● Step 3: Try the UX when the system is only driven
by AI.
@ctoxtli

18. Hey! now you have a working solution
● This version 0 would be super inefficient
but the model and the mitigation process
are implemented.
● Implementing the state-of-the-art algorithm
will be a simple module replacement task.
@ctoxtli

19. Conclusions
● An AI MVP may never see the light if we
are looking for a "Viable" first version.
● Having an implemented first draft of an AI
model and a mitigation strategy can boost
the development of robust AI solutions.

20. Thanks
http://www.carlostoxtli.com
@ctoxtli