Machine Learning
Blog Posts
The Myth of Machine Learning Non-Reproducibility and Randomness for Acquisitions and Testing, Evaluation, Verification, and Validation
A reproducibility challenge faces machine learning (ML) systems today. This post explores configurations that increase reproducibility and provides recommendations for these challenges.
Read More•By Andrew O. Mellinger, Daniel Justice, Marissa Connor, Shannon Gallagher, Tyler Brooks
In Artificial Intelligence Engineering


The Top 10 Blog Posts of 2024
This post presents the top 10 most-visited posts of 2024, highlighting our work in software acquisition, artificial intelligence, large language models, secure coding, and more.
Read More•By Bill Scherlis
In Software Engineering Research and Development

Introduction to MLOps: Bridging Machine Learning and Operations
Machine learning operations (MLOps) has emerged as a critical discipline in artificial intelligence and data science. This post introduces MLOps and its applications.
Read More•By Daniel DeCapria
In Artificial Intelligence Engineering

Measuring AI Accuracy with the AI Robustness (AIR) Tool
Understanding your artificial intelligence (AI) system’s predictions can be challenging. In this post, SEI researchers discuss a new tool to help improve AI classifier performance.
Read More•By Michael D. Konrad, Nicholas Testa, Linda Parker Gates, Crisanne Nolan, David James Shepard, Julie B. Cohen, Andrew O. Mellinger, Suzanne Miller, Melissa Ludwick
In Artificial Intelligence Engineering


The Challenge of Adversarial Machine Learning

This SEI Blog post examines how machine learning systems can be subverted through adversarial machine learning, the motivations of adversaries, and what researchers are doing to mitigate their attacks.
Read More•By Matt Churilla, Nathan M. VanHoudnos, Robert W. Beveridge
In Artificial Intelligence Engineering


Tackling Collaboration Challenges in the Development of ML-Enabled Systems

This SEI blog post highlights research examining the collaboration challenges inherent in the development of machine-learning-enabled systems compared to traditional software development projects.
Read More•By Grace Lewis
In Artificial Intelligence Engineering

Improving Automated Retraining of Machine-Learning Models
This post describes how to improve representative MLOps pipelines by automating exploratory data-analysis tasks.
Read More•By Rachel Brower-Sinning
In Artificial Intelligence Engineering

Six Dimensions of Trust in Autonomous Systems
This post chronicles the adoption and growth of autonomous systems and provides six considerations for establishing trust.
Read More•By Paul Nielsen
In Software Engineering Research and Development

Release of SCAIFE System Version 2.0.0 Provides Support for Continuous-Integration (CI) Systems
Key features in new release of SCAIFE System Version 2.0.0 including support for continuous-integration (CI) systems, and status of evolving SEI SCAIFE work
Read More•By Lori Flynn
In Secure Development

Systems Engineering and Software Engineering: Collaborating for the Smart Systems of the Future
Convergence between systems engineering and software engineering is forging new practices for engineering the smart systems of the future.
Read More•By Paul Nielsen
In Cyber-Physical Systems
