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The Agile Data Science Playbook: Quarterly Sprints to ML Success featuring Lauren Creedon artwork
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The Agile Data Science Playbook: Quarterly Sprints to ML Success featuring Lauren Creedon

Product Manager Hub (PM Hub) by Cyrus Shirazian

Jul 7, 202444:26Technology

Summary Summary In this conversation, Cyrus and Lauren discuss the intersection of Agile and data science, specifically focusing on the challenges of shipping AI-enabled products quickly. They emphasize the importance of...

About This Episode

The Agile Data Science Playbook: Quarterly Sprints to ML Success featuring Lauren Creedon is an episode from Product Manager Hub (PM Hub) by Cyrus Shirazian. Summary Summary In this conversation, Cyrus and Lauren discuss the intersection of...

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This episode belongs to Product Manager Hub (PM Hub).

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Episode Details

Published Jul 7, 2024, 44:26 long, audio available.

Questions About This Episode

What is The Agile Data Science Playbook: Quarterly Sprints to ML Success featuring Lauren Creedon about?

Summary Summary In this conversation, Cyrus and Lauren discuss the intersection of Agile and data science, specifically focusing on the challenges of shipping AI-enabled products quickly. They emphasize the importance of democratizing AI within organizations and the need for product managers to understand AI and ML concepts. They also discuss the prioritization of AI ML feature sets per quarter and the balance between quick wins and long-term strategic initiatives. Lauren shares her recommendations for getting buy-in and support from leadership, including listening, scenario planning, and making informed decisions. Takeaways Democratizing AI within organizations is crucial for enabling more people to understand and work with AI and ML. Product managers should prioritize AI ML feature sets based on business goals and market expectations. Balancing quick wins and long-term strategic initiatives is important for delivering outcomes and driving growth. Getting buy-in and support from leadership requires listening, scenario planning, and making informed decisions. Understanding the constraints and goals of different teams and stakeholders is essential for successful product management in the AI ML space. Chapters 00:00 Introduction and Background 03:25 Challenges of Delivering Business Value Quickly 06:52 Democratizing AI within Organizations 11:05 Scoping AI/ML Feature Sets for Revenue Outcomes 14:12 Staying Up-to-Date with New Technologies 27:40 Incorporating AI into Product Strategies 28:54 Aligning Organizational Expectations and Goals 30:09 Understanding Constraints and Goals 33:10 Planning and Execution 36:04 Balancing Quick Wins and Long-Term Strategic Initiatives 40:17 Gaining Buy-In from Leadership 43:10 Democratizing Knowledge about AI and ML Keywords Agile, data science, intersection, challenges, shipping, AI-enabled products, democratizing AI, product managers, prioritization, feature sets, quick wins, long-term strategic initiatives, buy-in, leadership

Where can I listen to The Agile Data Science Playbook: Quarterly Sprints to ML Success featuring Lauren Creedon?

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Which podcast is The Agile Data Science Playbook: Quarterly Sprints to ML Success featuring Lauren Creedon from?

The Agile Data Science Playbook: Quarterly Sprints to ML Success featuring Lauren Creedon is an episode from Product Manager Hub (PM Hub) by Cyrus Shirazian.

How long is this episode?

This episode is 44:26 long.

When was this episode published?

This episode was published on Jul 7, 2024.

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Are there related episodes from Product Manager Hub (PM Hub)?

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Where can I listen to The Agile Data Science Playbook: Quarterly Sprints to ML Success featuring Lauren Creedon?

You can listen to The Agile Data Science Playbook: Quarterly Sprints to ML Success featuring Lauren Creedon on this page when the episode audio is available from the podcast feed.

Which podcast is this episode from?

The Agile Data Science Playbook: Quarterly Sprints to ML Success featuring Lauren Creedon is from Product Manager Hub (PM Hub) by Cyrus Shirazian.

What are the episode details?

Published Jul 7, 2024 and 44:26 long