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.
Helping leaders succeed and organizations thrive.
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.
How Data Science Started at Continental Tires
This paper discusses the journey and lessons learned in managing the life cycle of machine learning (ML) and artificial intelligence (AI) models as Continental Tires industrialized its data science initiatives.
The paper concludes by highlighting the success of Continental Tires in developing AI-based products for internal process optimization and the strong foundation they have established for quickly evolving new use cases and rolling them out as productive software.
Key points covered include:
1. The importance of a bottom-up approach and establishing a common infrastructure for all data science teams. 2. The need for collaboration between data scientists and IT teams to develop processes and bridge gaps between business and IT departments. 3. The process of industrializing models and creating data products that optimize business processes or extend existing products. 4. The essential elements of model life-cycle management, including data and feature versioning, model versioning, model warm-up, model deployment, and model retraining. 5. The significance of the model warm-up phase in testing the model’s performance in real-world scenarios and the need for a protected environment that allows data scientists to “fail fast, learn fast.” 6. The deployment of models and the distribution of responsibilities among different teams to maintain and improve the software. 7. The importance of enabling the data science community by providing a provisioning environment and promoting data-driven work across the company.
Bridging gaps between business and IT for successful AI model development and deployment.
Creating data products that optimize processes and extend existing offerings.
Comprehensive approach to manage models from versioning to deployment and retraining.
Empowering data scientists with provisioning environments and promoting data-driven work.
Dubravko Dolic, Head of Applied Analytics & AI for Continental Tires, has been programming with data since 1996. He is focused on solving data driven problems and generating insights by squeezing data sources. Dolic loves IT and tools in the field of data analytics and data science.
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