Paul Battisson is the Chief Operating Officer for Cloud Galacticos. In this episode, we are talking about the convergence of two of his favorite things: “super hardcore nerdy math” (as Paul puts it) and Apex.

 

Paul has performed some unique experiments that combined his love for math with his love for Apex. In our conversation, we talk about those experiments at length. We go into some detail about what factors can affect Apex performance and weirdly, talk a lot about pigeons.

 

Show Highlights:

 

Paul’s inspiration for learning how to code. What Machine Learning is and how it is architected Why Paul wanted to get Machine Learning (ML) in Apex. The difference between Machine Learning and Artificial Intelligence (AI). Where matrices fall into the toolset of creating Machine Learning. How linear regressions are utilized in Machine Learning. All about Paul’s Virtual Reality (VR) experiment in Apex. The pitfalls of enterprise VR and where Paul sees it going in the future. Specifics on how to increase performance in iterators What heap size is and how it affects performance What Paul’s two books are about.

 

Links:

1. Paul's Blog Posts on Machine Learning in Apex:

 

Part 1: https://paulbattisson.com/blog/2018/ai-for-salesforce-developers-part-1/ Part 2: https://paulbattisson.com/blog/2018/ai-for-salesforce-developers-part-2/ Part 3: https://paulbattisson.com/blog/2019/ai-for-salesforce-developers-part-3/

2. Better Performance in Loops:

 

Part 1: https://paulbattisson.com/blog/2014/force-com-cast-episode-6-faster-loops/  Part 2: https://paulbattisson.com/blog/2014/force-com-casts-episode-7-faster-loops-part-2/

 

3. Follow up blog on performance: https://paulbattisson.com/blog/2020/Salesforce-Performance-Some-Thoughts/

4. Dreamforce deck of doing VR in Visualforce: https://www.slideshare.net/pbattisson/salesforce1-vr-immersive-data-visualization

5. Learning Salesforce Development With Apex: https://www.amazon.com/Learning-Salesforce-Development-Apex-English/dp/9389898188/

 

6. Mastering Apex Programming: https://www.amazon.com/gp/product/1800200927

 

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