Using Agile in Machine Learning projects comes with its own set of challenges. We need a flexible and adaptive approach that considers the unique challenges of data-driven development. Blending Agile with data-focused methodologies, adopting frameworks like CPMAI or Microsoft's Team Data Science Process or Data Driven Scrum (DDS), and fostering increased collaboration between the datascience and softwaredevelopment teams are key factors for success. The key is to leverage the strengths of Agile while tailoring it to meet the specific demands of AI-ML projects.

Ever faced a situation where every choice feels like a losing one? Welcome to the world of the Kobayashi Maru! In this video, we dive into how 🚀 Star Trek’s no-win scenario reveals powerful lessons in leadership for handling real-world challenges. From staying calm under pressure to navigating impossible decisions, we'll break down key takeaways that can help you lead like a Starfleet captain.

Curious how to turn the toughest moments into opportunities for success? Hit play and find out! 🌟

In the fast-paced world of Agile project management, prioritising tasks effectively is paramount. This podcast explores various techniques to help you identify and focus on high-value tasks, ensuring your projects stay on track and deliver maximum impact.

🌟 Join us as we discuss 4 techniques (MoSCoW, RICE, Value Effort Matrix and WSJF) in detail, providing insights and practical examples to help you master the art of agile prioritisation and deliver exceptional products.

This Podcast is based on the article "Master 🥋 these 4 Agile Backlog Prioritization and Ordering techniques for Value Delivery⁠" by Debashish Chakrabarty. Please do subscribe to the newsletter for latest articles on Agile, Lean, Product Development & Leadership.