33 min
Becoming a machine learning practitioner O'Reilly Data Show Podcast
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- Business
In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.
Anatomy of an Alexa skill. Image by Kesha Williams, used with permission.
We had a great conversation spanning many topics, including:
How she got started and made the transition into a full-fledged machine learning practitioner.
We discussed the evolution of ML tools and learning resources, and how accessible they’ve become for developers.
How to build and monetize Alexa skills. Along the way, we took a deep dive and discussed some of the more interesting Alexa skills she has built, as well as one that she really admires.
Related resources:
“Product management in the machine learning era”: a new tutorial session at the Artificial Intelligence Conference in London
Cassie Kozyrkov: “Make data science more useful”
Kartik Hosanagar: “Algorithms are shaping our lives—here’s how we wrest back control”
Francesca Lazzeri and Jaya Mathew: “Lessons learned while helping enterprises adopt machine learning”
Jerry Overton: “Teaching and implementing data science and AI in the enterprise”
“Becoming a machine learning company means investing in foundational technologies”
“Managing risk in machine learning”
“What are model governance and model operations?”
In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.
Anatomy of an Alexa skill. Image by Kesha Williams, used with permission.
We had a great conversation spanning many topics, including:
How she got started and made the transition into a full-fledged machine learning practitioner.
We discussed the evolution of ML tools and learning resources, and how accessible they’ve become for developers.
How to build and monetize Alexa skills. Along the way, we took a deep dive and discussed some of the more interesting Alexa skills she has built, as well as one that she really admires.
Related resources:
“Product management in the machine learning era”: a new tutorial session at the Artificial Intelligence Conference in London
Cassie Kozyrkov: “Make data science more useful”
Kartik Hosanagar: “Algorithms are shaping our lives—here’s how we wrest back control”
Francesca Lazzeri and Jaya Mathew: “Lessons learned while helping enterprises adopt machine learning”
Jerry Overton: “Teaching and implementing data science and AI in the enterprise”
“Becoming a machine learning company means investing in foundational technologies”
“Managing risk in machine learning”
“What are model governance and model operations?”
33 min