Predictive maintenance in industrial IoT: data pipelines
Data architecture for Predictive Maintenance: how to standardize data flow via OPC UA and distribute computation between Edge and Cloud without replacing SCADA.
INSIGHTS & PRACTICE
Practical guides to business-process automation: from the first assessment to measurable results.
479 publications
Data architecture for Predictive Maintenance: how to standardize data flow via OPC UA and distribute computation between Edge and Cloud without replacing SCADA.
Shifting from reactive invoice analysis to proactive cost modeling during architectural design using unit economics and AI assistants.
Why cloud bills rise after migration and how to embed financial efficiency (FinOps) into infrastructure design during the application planning stage.
Analyzing cloud architectural errors: from over-provisioning to uncontrolled scaling, and exploring practical mitigation methods through FinOps and proper governance.
Effective cloud governance requires shifting from reactive cost-cutting to automated design-phase control and shared financial responsibility between teams.
A practical guide to cost allocation, rightsizing, and reserved capacity for CTOs and CFOs. Learn how to turn the cloud into a predictable and efficient business asset.
Cloud budget control is a continuous architectural process. We analyze budget leaks, FinOps implementation, and practical infrastructure optimization tools.
Analyzing a pragmatic approach to multi-cloud and hybrid architectures: how to avoid operational complexity traps, implement FinOps, and secure the organizational perimeter.
Choosing a cloud migration strategy is a financial and architectural decision. We analyze how to prevent budget growth using cost modeling and FinOps methodology.
An overview of implementing a FinOps culture, the Inform-Optimize-Operate cycle, and unit economics principles to effectively manage cloud spending in the enterprise sector.
By 2027, successful AI adoption in development will depend not on code generation speed, but on integrating AI tools into controlled testing, security, and architectural oversight processes.
Transitioning from experimental ML models to robust enterprise systems using AWS Well-Architected Framework, Google SRE, and 12-Factor App principles.