Hi everyone, welcome to the A6 and Z podcast. Today's episode, continuing our series on translating

AI into practice, is one of our shorter bytes based on a panel discussion that took place

at our recent annual A6&Z summit event just last month. Operating partner Frank Chen, who put out a

microsite on getting started with AI earlier this year, talks with Janstoyka, co-founder of Databricks,

and Scott Clark, co-founder of Sigopt,

and both have been on this podcast if you want to hear more from them in other episodes,

about the cold start problem for companies getting started with AI,

especially focusing on the role of data scientists and domain experts in this context.

You guys now, between the two of you, have now sort of been with the customer on their journeys

from sort of day one until they have models in production. And so what advice do you have

for people who aren't Google, Amazon, Facebook, Apple to realize machine learning? What do they need

to do on day one?

I have many enterprise companies, and out of them, over 70% actually they have AI projects.

And what we see actually, if you take the step back, there are three stages.

The first stage is to make sure that you have the data.

Many times this takes more than actually

building the machine learning or AI model. The second thing is about once you have the data

to become, so to speak, to operationalize this, to become a data-driven company, to figure

out what are the KP, the key performance indicators which are going to be driving

your business. You need to take these KPI based on the data and operationalize, meaning to have

reports, dashboard, and so forth. And now, once you have this, then you are going to start and

use machine learning and AI to improve these KPIs.

...