The InfoQ Podcast

Machine Learning for developers: 5 Essential Podcasts


We talked to several well-known software industry figures working within the artificial intelligence (AI) and machine learning (ML) space, and asked what these trends mean for software developers. We also explored how they are building, training, and deploying AI/ML systems in production.

To help you learn more, we’ve compiled a playlist of five essential AI/ML podcasts that will get you up-to-speed in this emerging field. Listen to all five, or enjoy each 30-minute episode individually. Learn from Grady Booch, the co-creator of UML, Emmanuel Ameisen, head of AI for Insight Data Science, and several more experts.

At InfoQ, our goal is to accelerate the software side of human progress, and by sharing valuable knowledge from domain experts we aim to help you unlock your potential, so that you and your team can “stand on the shoulders of giants” and create better software that ultimately delivers even more value to your customers and to the wider community.


Things you will learn from these podcasts

  • AIso will remove the tedium for software developers; however, software developing is (and will remain) a labor-intensive activity for decades to come. AI is another bag of tools in a larger systems activity.
  • ThirdLove’s story shows that you may be able to move towards an ML solution quickly by leveraging your own network of exports to learn more, and by using tools that may already familiar to your team.
  • Federated machine learning is an approach of developing models at an edge device and returning just the model to a centralized location. By taking the average of the edge models, you can protect the privacy and distribute processing of building models.
  • When implementing image classification, you generally want to use a convolutional neural network. You typically use a model pre-trained with a public data set like Imagenet, pre-trained to generate embeddings, using the pre-trained model up to the penultimate layer, and storing the value of the activations.
  • Ethical machine learning is focused on practices and strategies for creating more ethically well-founded ML models that, for example, avoid encoding inherent bias that can be found within a system.


Grady Booch on Today’s Artificial Intelligence Reality and What it Means for Developers

InfoQThe good news is that the systems engineering things that we know and love in building non-AI systems are going to apply for building AI systems as well.

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Grady Booch the co-creator of UML, an original member of the design patterns movement

Megan Cartwright on Building a Machine Learning MVP at an Early Stage Startup

InfoQ You don’t have to be fancy or use deep learning, but you can take a lean approach to machine learning.

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Megan Cartwright Director of Data Science ThirdLove

Mike Lee Williams on Probabilistic Programming, Bayesian Inference, and Languages Like PyMC3

InfoQ You don’t find yourself writing a lot of for loops, or the order in which things need to happen: you simply describe the world and press go, and the probabilistic programming language figures out the implications of that

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Mike Lee William Software Engineer @Cloudera

Emmanuel Ameisen, Head of AI at Insight, on Building a Semantic Search System for Images

InfoQ What you hear about deep learning is that it learns representations - so instead of you having to come up with a collection of rules to describe a road, it can learn it for you.

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Emmanuel Ameisen head of AI for Insight Data Science

Katharine Jarmul and Ethical Machine Learning

InfoQOne of the words for machine learning is to build discriminators - we are asking the machine to make a decision

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Katharine Jarmul Privacy activist, AI dissenter, machine learning engineer.

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Other resources

Go one level further and get ready to implement some of the key takeaways in your teams and jump in front of your competition: