Thoughtworks Technology Podcast

Machine learning in the wild

Sep 24, 2021

46:49

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Highlights

AI Chapters

Episode notes

The Machine Learning Approach to Machine Learning

01:19

Reinforcement Learning for Dynamic Pricing

03:26

Machine Learning - What Are the Similarities and Differences?

05:00

Introduction

2min

The Difference Between Traditional and Machine Learning Models

3min

Machine Learning - What Are the Similarities and Differences?

5min

How to Deploy Machine Learning Models in the Wild?

3min

Using on Line Models to Make a Better Prediction

4min

The Business Case of Modeling Churn Risk Scores

2min

What if Instead of States, We Would Predict Actions?

5min

The Machine Learning Approach to Machine Learning

1min

Reinforcement Learning for Dynamic Pricing

3min

10.

Is There a Difference Between a Micro Service and an API?

3min

11.

Machine Learning

2min

12.

Can Machine Learning Be Used to Automate Marketing?

4min

13.

How to Make a Blended Whisky?

3min

14.

How to Build a Discriminative Model?

3min

15.

Machine Learning and Optimization

5min

From creating novel solutions for parking airplanes or identifying the winning formula for single malt whiskeys, our colleagues at Fourkind have extensive experience in building machine learning systems. Here, Max Pagels and Jarno Kartela highlight why deploying ML is different, how edge cases can confound well-trained models and the unexpected areas where ML can deliver better than human-levels of performance.