The Panintelligence Way: Deploy Machine Learning Without Python and at Scale
When I first started working with machine learning models, I had a very specific picture in my head of what “deploying a model” meant. You train something in Python, then you wrap it in an API, put it behind a service, deploy it somewhere, and then connect your application to it. That was the mental model I […]
One of the biggest misconceptions about predictive analytics is that the hard part is building the model. By the time you open any machine learning platform, most of the important work should already be done. A good model starts with good data, and that means spending time understanding the business problem before thinking about algorithms. Whether you’re trying to […]
From Model to Matchday: Building and Embedding Our World Cup Dashboards
By Persis Duaik, Presales Consultant at Panintelligence and the Brazilian half of our World Cup prediction team In the previous blog, I explained how we prepared more than 49,000 historical international football results for analysis, turning raw facts into model-ready variables: form difference, attack and defence ratings, and tournament importance. We even built the Panintelligence Team Strength Index (PI-TSI) to give us a clean, […]
Can You Really Predict Football? How We Prepared the World Cup Data
By Persis Duaik, Presales Consultant at Panintelligence and the Brazilian half of our World Cup prediction team In our first blog, Reece introduced our slightly ambitious plan to use PiPredict and Panintelligence to analyse the 2026 World Cup. Reece brings the finance brain, Yorkshire realism and the emotional resilience that comes from supporting Tottenham Hotspur. I bring the technical build, a presales […]