Paul Karsten 4/9/25 Paul Karsten 4/9/25

There is no Bad Data

Data's value depends on its intended use. Operational data collection often prioritizes transactions over analysis, resulting in data not optimized for later purposes. Technical data aggregation can introduce biases. Unclear business requests and data silos complicate analysis. To leverage data effectively, we need to be flexible on how we analyze the data we have at hand.

Paul Karsten 2/17/25 Paul Karsten 2/17/25

Ethics Is Not “One and Done”

Implementing ethics in Machine Learning is not a one-and-done effort. Organizations must build trustworthy systems that serve their customer’s best interests fairly. Ultimately, ethically designed machine learning models align with compliance and regulations while also avoiding harmful outcomes.

Paul Karsten 10/28/24 Paul Karsten 10/28/24

Practical Business Reasons to Resist the Allure of AI

There are many traps along the journey required to leverage AI/ML to generate value for your business. Success relies on aligning AI/ML initiatives with clear business objectives and understanding their true potential.

Paul Karsten 8/30/24 Paul Karsten 8/30/24

MLOps Automation

MLOps requires specialized knowledge that traditional DevOps teams lack. The challenges related to data quality, consistency, and accessibility demand a different set of skills and tools.

Paul Karsten 8/23/24 Paul Karsten 8/23/24

Model Release & Assessment Phase

This 3rd phase of our Data Science Process explores the release of ML models into production and the importance of ongoing monitoring and assessment.

Additionally, it provides a framework for defining "done" and achieving a high-quality model release.

Paul Karsten 7/10/24 Paul Karsten 7/10/24

Model Development

This blog post outlines the second phase of our Data Science Process: Model Development. Which involves building, training, and evaluating models based on data gathered during Question Formation. The process is iterative, experimenting with different algorithms, features, and parameters in a sandbox environment before scaling to larger datasets. Model performance is evaluated using metrics, validation for overfitting/underfitting, and checks for robustness and interpretability. Finally, models must be versioned, monitored for data drift, and continuously updated to ensure they remain effective and relevant over time.

Paul Karsten 5/13/24 Paul Karsten 5/13/24

Question Formation and Data Analysis in Data Science

This blog post focuses on the first phase of our Data Science Process: Question Formation and Data Analysis. In this phase, we iterate multiple times through question formation, data collection, and exploration. Initial questions are likely to be of low fidelity. Through the process of data exploration, the questions gain fidelity and drive toward business value.

Paul Karsten 5/13/24 Paul Karsten 5/13/24

Introducing a Data Science Process for AI/ML

This is an introduction to a series of blog posts describing the process of creating and operating data models in support of your AI/Machine Learning (ML) programs. It is structured to ensure that you can deliver actual business value.

Welcome to the Parallaxis Blog