Demystifying the Buzz in Machine Learning! (This time for real)

When I started my data science career in 2013, everyone was into big data. In fact, big data was at the peak of inflated expectations (Source: Gartner). You had to use tools like Hadoop and Spark to be one of the cool kids. Many data prophets out there told you that data is the new oil or even gold. Year 2018, things haven’t changed. Data is still cool and going strong. It’s eating the world and yes you still need big data and now also deep deep very deep learning. There’s a lot of bullshit bingo out there.

In this talk, I want to demystify the buzz in machine learning by presenting some simple guidelines for successful data projects and real practical use cases. In fact, I will share some of the stuff that we’re working on at idealo (, Germany’s largest price comparison service. And yes it involves deep learning and yes it can be quite technical sometimes as well.

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Outline/Structure of the Talk

  • Introduction main problem -> too much hype
  • Goal of the talk is to show some guidelines for successful data projects
  • Guidelines:
    • MVP approach to data products: Medium blog post
    • Use the cloud
    • Use the right tool for the right problem
    • Data product prioritizations
    • Measure your model and improve it from time to time
    • Good software engineering practices
    • etc..
  • Summary

Learning Outcome

- What are good practices for successful data projects

- Some realistic use cases with deep learning applied to (e.g. image classification, aesthetic ranking to hotel images etc.)

- Lean Startup thinking for Data projects

Target Audience

data scientists, machine learners, software engineers, data analyst

Prerequisites for Attendees

- Experience with basic/intermediate machine learning

schedule Submitted 1 month ago

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