Becoming a machine learning practitioner
Update: 2019-08-29
Description
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.
<figure>
<figcaption>Anatomy of an Alexa skill. Image by Kesha Williams, used with permission.</figcaption></figure>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?”
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