In this episode, my guest is Daniel Mccaffery. Daniel is a technology thought leader driving Data and Analytics at Climate Corporation (a division of Bayer). 

Daniel shares his insights on using ML to provide personalized recommendations for helping farmers grow crops with higher yield, profitable and sustainability. This involves deciding the right seed, right crop protection, density levels across different parts of the farm, etc. This is a fascinating example of AI and physical sciences coming together to build an innovative product offering. Daniel and I had a blast covering several topics: the building of models, model deployment and re-training, explainability for farmers to understand the recommendations, managing bias, experimentation A/B testing, monitoring drifts, data labeling, and perspectives on key bottlenecks in going from idea to ROI.