From Research to Industry, in Ten Not-So-Easy Steps(Bridging the Gap Between Academia and Industry)

Radim Řehůřek, Ph.D.rare-technologies.com

2005

2007

2010

2011

Respect for open source?

http://wstein.org/talks/2016-06-sage-bp/bp.pdf

RaRe Technologies Ltd.

2016 – Academic partnerships

#1 Every company is a shit show

#2 Nobody cares about experts showing off, or ivory towers

“The purpose of computation is insight, not numbers.”

Richard Hamming● clear processes: scoping, error analysis● always eye toward business impact

○ vs. technologist mindset, tinkering for fun

Pragmatic applications of academic results worth gold!

http://rare-technologies.com/performance-shootout-of-nearest-neighbours-querying/

#3 Pricing● beware of hidden costs of consulting!● expect to bill ~50% hours: not 1:1 salary

○ the rest: keeping up with tech, research○ prospecting, accounting, proposals, marketing ○ internal trainings, code reviews○ +open source “free support”...

● charge for business analysis!○ that bit to understand client’s business, goals & data○ before any implementation and before SoW

Cambrian explosion

“Do one thing and do it well.”Doug McIlroy

"Every program attempts to expand until it can read mail. Those programs which cannot so expand are replaced by ones which can."

Zawinski’s law of software development

#4 Master your tools

Business value >> tech stack

http://www.slideshare.net/ryanorban/how-to-become-a-data-scientist

Lots of existing resources

#5 Prepare for difficult, poorly scoped “challenges”

Good● get experience w/ various

verticals, industries● learn the process: how to

scope, price, analyze, deliver

● develop ancillary skills: accounting, taxes, IP law...

Bad● uncertainty not for

everyone● transfer of domain

expertise difficult● expectations to master

all new tech instantly, constantly

#6 Take progress easy

● paper into production = massive investment!

#7 Expect non-glamorous work

1. problem discovery, scoping, APIs2. data collection, cleaning3. evaluation, reporting, visualizations4. coding, integration, automationvs. what novices expect: models, math, algorithmsvs. suits & armchair AI philosophers

#8 Avoid wasting time on “unrealistic” clients

Bad● enterprise “demonstrate innovation

to execs” projects... unsatisfying● idea stage start-ups “golden

goose… on $5k budget!”... naive● veiled attempts at hiring... a

flattering distraction

Good● smaller/medium companies with

established market fit● reporting to CTO / CIO / project lead● ideally longer term collaboration:

sense of ownership

How?● Price yourself out of “I have an idea” range● Manage expectations clearly, terminate dead ends quickly

#9 Be wary of working for equity

● too risky○ do you really want to be an early investor?

● core UVP tech designed and developed by an external consultant... tricky○ esp. remote consulting

● leverage & impact mismatch

Navigating the hype● It’s your responsibility to

manage expectations and advise on tech stack

● Diplomacy needed: “big data” & co

● Cultural differences

Rare Brain Trust

#10 Scaling up consulting sucks

Good● people help scaling up● take on larger projects,

with larger teams

Bad● people don’t help scaling

up● gold rush, “instant

billionaires”

...Teach yourself “data science” in ten years

Bonus tip #11: take time to appreciate your luck

Radim ŘehůřekRaRe Technologies Ltd.

http://rare-technologies.com(previously radimrehurek.com)

@radimrehurek

Blog (mostly tech+machine learning, some travel):http://radimrehurek.com/blog/