January 10, 2020

Episode 114: What Makes a Successful AI Project?

Jon Prial

Artificial Intelligence, Explainability, Trust

A photograph of Taraneh Khazaei.

In this week’s podcast Jon Prial is joined by Tara Khazaei, Chief Data Scientist, National AI Team, Customer Success Unit at Microsoft. Jon and Tara talk about how domain knowledge, as well as statistical intuition, make for more successful outcomes in machine learning projects. They discuss performance through the lens of projects Tara and her team have led at Microsoft. For instance, you’ll hear about projects the Microsoft team led to improve customer service at a bank, to improve bus time departure estimates and to predict opiate overdoses. Through their conversation, you’ll hear what makes a successful AI project.

In this episode you’ll hear:

  • Why you need enough, high-quality data for a successful AI project
  • The importance of iterating and validating your approach to achieve the best performance
  • The challenges bias and explainability pose to ML projects
  • Why domain knowledge is crucial for successful outcomes
  • How to decide when your model is ready to go into  production and why you need to go beyond accuracy

Listen to every episode:
iTunes | Spotify | Google Play | SoundCloud | Stitcher | RSS

Who is Taraneh Khazaei?

Taraneh Khazaei is Chief Data Scientist on the National AI Team,  Customer Success Unit at Microsoft. In this role, she advises Microsofts clients on how to adopt machine learning. Working with clients, Tara has researched the state of the art of speech to text methods and technologies, developed deep sequential modeling methods (e.g., use of embeddings, RNNs, and transformer networks) on terabytes of clickstream data to model and predict user online behavior and designed and developed an ML pipeline to predict the market price of a vehicle.

Links to the case studies discussed in this podcast:

1. TD builds on its reputation for excellent customer service with the Microsoft Cloud

2. Artificial intelligence improving bus departure estimates

3. Predicting Illicit Opiate Drug Overdoses in the City of Surrey

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