Post-hype Machine Learning

 Piero Savastano - pieroit

Why trust me?

• I have a fair experience in both academy and industry and worked on data for at least 10 years
   
• I was dealing with AI and data science when very few people were interested in it

Drop the expectations

We are currently in a bubble similar to the dotcom one.

When the bubble will pop, we will finally leave exaggerated expectations and profit from AI/ML as tools to partecipate in a data economy.

The bare minimum

you need to know about ML

• Implicit programming starting from data
   
• Statistical optimization, cyclic development
   
• Again, we are in a data economy

Data is king

• Data > algorithm
   
• Garbage in, garbage out
   
• Challenges in cleaning, integrating and standardizing data from different sources (also within an organization)

Good is enough

• “I want 99% accuracy”
  
  
  
  
  
  
  
  
  
   
• R&D is expensive
• Tip: start simply. Then search for a research lab or a startup that is already working on the problem, otherwise be prepared for a long term effort

The mith of the Data Scientist

• At least 4 different jobs:
  • System admin (building and managing clusters)
  • Researcher (testing and composing algorithms)
  • Analyst (vertical domain expert)
  • Developer (integrating in existing architectures)
  • PR (viz and communications)
• If you find one, get ready to pay him big money
• A more mature approach is to set up a team

A step by step process

to dominate and take value

out of data

1. Build a team with a data scientist* and a domain expert

2. Clarify in great detail what are the problems to solve

3. Establish business-sound metrics

4. Build, validate and cycle

Thank you

&

good luck