More than a hundred years ago, Andrei Markov invented algorithms to predict a future action based on the past. His algorithms became the base of many features we would call machine learning today, from mail spam filter to speech recognition Markov chains and hidden Markov models are used everywhere. How can we apply his work to large scale web application? How can we improve the user’s perceived performance of our app by predicting what chunk of code to load next? And how can we make decisions in a microservice architecture based on Markov’s work? Let’s find that out together and dive into the world of prediction and perceived performance.
What are the key takeaways from this talk?
Use machine learning and analytics to optimize your performance.
