Inspire, develop, and guide a winning organization.
Create visible workflows to achieve well-architected software.
Understand and use meaningful data to measure success.
Integrate and automate quality, security, and compliance into daily work.
Understand the unique values and behaviors of a successful organization.
LLMs and Generative AI in the enterprise.
An on-demand learning experience from the people who brought you The Phoenix Project, Team Topologies, Accelerate, and more.
Learn how making work visible, value stream management, and flow metrics can affect change in your organization.
Clarify team interactions for fast flow using simple sense-making approaches and tools.
Multiple award-winning CTO, researcher, and bestselling author Gene Kim hosts enterprise technology and business leaders.
In the first part of this two-part episode of The Idealcast, Gene Kim speaks with Dr. Ron Westrum, Emeritus Professor of Sociology at Eastern Michigan University.
In the first episode of Season 2 of The Idealcast, Gene Kim speaks with Admiral John Richardson, who served as Chief of Naval Operations for four years.
New half-day virtual events with live watch parties worldwide!
DevOps best practices, case studies, organizational change, ways of working, and the latest thinking affecting business and technology leadership.
Is slowify a real word?
Could right fit help talent discover more meaning and satisfaction at work and help companies find lost productivity?
The values and philosophies that frame the processes, procedures, and practices of DevOps.
This post presents the four key metrics to measure software delivery performance.
This paper, published in the Fall 2024 Enterprise Technology Leadership Journal, addresses the challenges of managing risks associated with artificial intelligence (AI) and machine learning (ML) systems. The authors aim to educate auditors, internal risk directors, and software leaders on the differences between existing risk management controls and those needed for new AI/ML systems, including generative AI. The paper provides an overview of the control environment for AI/ML systems, focusing on six unique assets and suggesting controls for managing associated risks throughout the AI/ML development life cycle.
AI/ML systems introduce new types of software assets, such as model weights and datasets, which require specific controls and management practices.
Effective risk management for AI/ML systems requires controls at each stage of the development lifecycle, from data preparation to continuous monitoring.
Traditional software development controls are insufficient for AI/ML systems; new controls are needed to address unique risks such as model drift, data quality issues, and hardware dependencies.
Ongoing surveillance of AI/ML systems in production is crucial for detecting issues like model drift, data anomalies, or outputs beyond expected thresholds.
The authors discuss the various stages of AI/ML system development, including data preparation, data management, model development, model evaluation, model deployment, and continuous monitoring. For each stage, they provide sample controls to mitigate specific risks. The paper emphasizes the importance of understanding the new types of software assets introduced by AI/ML systems and the need for organizations to update their practices to accommodate these new components. The authors also highlight real-world examples of problems that can occur when AI/ML assets are not properly managed, underscoring the importance of implementing effective controls.
Cofounder and Chief Methodologist at Scaled Agile, Inc.
Chief Technology and Innovation Officer, Co-Founder at Excella
John Rzeszotarski assists organizations with strategic planning and leadership in the solution and infrastructure focus areas; moreover, John provides thought leadership to large enterprises that need to focus on reliability, scalability, regulatory, and other business considerations. His expertise spans many verticals with a focus on digital, payments, security, development, and his primary passion is solving business and IT problems thru technology, process, and culture transformations.
Michael Edenzon is a senior IT leader and engineer that modernizes and disrupts the technical landscape for highly-regulated organizations. Michael provides technical design, decisioning, and solutioning across complex verticals and leverages continuous learning practices to drive organizational change. He is a fervent advocate for the developer experience and believes that enablement-focused automation is the key to building compliant software at scale.
Peter Ajemba is an expert in machine learning and artificial intelligence systems. He works with organizations to apply AI/ML to optimize business processes and systems and to develop data and insights products. A former chief technology officer for a drug discovery venture that pioneered AI/ML use in bio-signal amplification, Peter provides technology leadership in product design, process improvement, regulatory strategy, and team development across consumer products, medical devices, and drug discovery verticals.
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