Managing Model Ensembles With MLflow
In machine learning, an ensemble is a collection of diverse models that provide more predictive power together than any single model would on its own. The outputs of multiple learning algorithms are combined through a process of averaging or voting, resulting in potentially a better prediction for a given set of inputs. However, there are...
Need for Data-centric ML Platforms
This blog is the first in a series on MLOps and Model Governance. The next blog will be by Joseph Bradley and will discuss how to choose the right technologies for data science and machine learning based on his experience working with customers. Introduction Recently, I learned that the failure rate for machine learning projects...
Optimizing User Defined Functions with Apache Spark™ and R in the Real World: Scaling Pitch Scenario Analysis with the Minnesota Twins Part 2
Introduction In part 1 we talked about how Baseball Operations for the Minnesota Twins wanted to run up to 20k simulations on 15 million historical pitches - 300 billion total simulations - to more accurately evaluate player performance. The idea is simple: if 15 million historical pitches sketched an image of player performance, 300 billion...
How the Minnesota Twins Scaled Pitch Scenario Analysis to Measure Player Performance – Part 1
Statistical Analysis in the Game of Baseball A single pitch in Major League Baseball (MLB) generates tens of megabytes of data, from pitch movement to ball rotation to hitter behavior to the movement of each individual baseball player in response to a hit. How do you derive actionable insights from all this data over the...
