Machine Learning
Blog Posts
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.
• 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.
• 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.
• 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.
• By Paul Nielsen
In Software Engineering Research and Development
Software Engineering for Machine Learning: Characterizing and Detecting Mismatch in Machine-Learning Systems
This post describes how we are creating and assessing empirically validated practices to guide the development of machine-learning-enabled systems.
• By Grace Lewis, Ipek Ozkaya
In Artificial Intelligence Engineering
Aligning DevSecOps and Machine Learning
Luiz Antunes explores the machine learning (ML) and DevSecOps domains and proposes ways to use them in collaboration for increased performance.
• By Luiz Antunes
In DevSecOps
Data-Driven Management of Technical Debt
Learn about the SEI's work on technical debt analysis techniques and practices to help software engineers manage its impact on projects in this SEI Blog post.
• By Ipek Ozkaya, Robert Nord
In Technical Debt
Machine Learning in Cybersecurity
Our technical report provides an overview of the relevant parts of an ML lifecycle--selecting the right problem, the right data, and the right math and summarizing the model output for …
• By Jonathan Spring
In CERT/CC Vulnerabilities
The Vectors of Code: On Machine Learning for Software
This blog post provides a light technical introduction on machine learning (ML) for problems of computer code, such as detecting malicious executables or vulnerabilities in source code....
• By Zachary Kurtz
In Artificial Intelligence Engineering
Deep Learning and Satellite Imagery: DIUx Xview Challenge
In 2017 and 2018, the United States witnessed a milestone year of climate and weather-related disasters from droughts and wildfires to cyclones and hurricanes....
• By Ritwik Gupta
In Artificial Intelligence Engineering
