Episode 3 : Machine Learning and Data Science, from model to production!

This time we'll dive into AI and machine learning! We'll explore why is their usage growing, how to put them to production, and why should engineers be interested in them, but also what ethical problems they bring. So come along!

To help me discover the challenges of Machine learning, I'll be joined by 

• Maike Fischer, Machine Learning engineer at ING and one of the creators of the 'Introduction to Machine Learning' workshop there.
• Vaidas Kurlianskas, Chapter Lead Data Science and Data Scientist at ING. Vaidas knows the challenges of developing models, but also managing the expectations of stakeholders !

During this episode, we'll get into the details of the terminology : Data Science, Machine Learning, Artificial Intelligence : How are those different? We'll also look into how machine learning engineers interact with product teams to help them improve their software. Then, we'll look at the different stages of building a model, from requirements all the way up to production! Finally, we'll look into the ethical issues that machine learning bring and how those impact our society.

And of course, just like each time, we'll cover the best resources to get you started; and find some potential problems to solve and learn at the same time. 

Some additional links mentioned during the episode : 

• Andrew Ng's Machine Learning course on Coursera : One of the most completed Coursera courses of all time.
• Kaggle : A website with datasets and challenges to solve, with practical implications.
• TensorFlow.js : A library to do Machine Learning on the frontend.

This podcast is hosted by me, Julien Lengrand-Lambert. Subscribe to this podcast on your preferred platform or follow Julien's Tech Bites on Twitter to learn more about our future episodes!

Episode source