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Published Jun 22, 2020

Roles to play in the AI dev workflow

Chris Benson and Daniel Whitenack delve into the multifaceted roles within AI development, showcasing how diverse skill sets can contribute, while dissecting AI workflows with an emphasis on agile collaboration. They also tackle the ethical challenges surrounding AI, focusing on bias and the critical need for fairness in developing robust solutions.
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Episode Highlights

  • AI Workflow

    AI development is a complex process that requires careful planning and scoping before diving into model creation. explains the initial phase involves defining the problem, exploring data, and validating proof of concepts, which is often iterative and requires domain expertise 1. emphasizes that AI development should be viewed as a component of software development, integrating various activities around it 2.

    Before you even get to exploring in the data context, you've got to figure out what is it that you think you want to build and why.

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    This approach ensures that AI solutions are not only technically sound but also aligned with business goals and resource constraints.

       

    Agile Practices

    Integrating AI development into agile practices enhances adaptability and learning. notes the advantage of overlapping teams in exploratory and production phases, which fosters ownership and robustness in AI solutions 3. argues that AI development fits well within agile frameworks, allowing for iterative improvements and adjustments 4.

    AI development fits very well into an agile software development process where you're having to iterate and you learn from that iteration.

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    This iterative approach is crucial for refining models and deployment strategies, ensuring that AI systems are effective and efficient.

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