Dynamic programming (DP) problems are interesting and at the same time challenging to solve. It requires a combination of rigorous analysis and a methodical approach to come up with solution for this type of problem. This session is designed to aid interviewees with a better understanding of the characteristics of DP problems, to explain the different approaches that one can apply and more importantly to provide a recipe that outlines a sequence of steps to transition from a brute force solution to an optimized solution. With sufficient amount of practicing in applying this recipe, interviewees can easily handle and have fun at solving DP problems. The insights and intuitions gain from mastering DP problem solving technique can easily be translated to solving other types of problems such as backtracking.
Machine Learning is more approachable than ever before and the number of companies applying Machine Learning to build AI powered applications and products has dramatically increased in recent years. On this journey of adopting Machine Learning, many companies learn successful Machine Learning projects require good software infrastructure to enable quick experiment iteration, ease of model development and deployment. Some of these large companies have sufficient resources to invest in building the necessary software infrastructure for their needs and the rest of the companies are looking for open source solutions to help them.
MLflow, an open source platform for the Machine Learning development lifecycle, was created in 2018 and it was designed to be extensible and pluggable from day one to simplify and speed up the the development of AI powered applications.
This session is designed to share the common needs in the Machine Learning development lifecycle, an overview of the features in the MLflow platform and it will end with a demo.