Mykhailo Vyhovsky

Unmonitored AI/ML model drift, driven by real-time data quality degradation, transforms a once-calibrated model into an operational risk. This leads to reduced performance and direct financial losses, necessitating comprehensive monitoring strategies and automated remediation.

Ukrainian companies face a critical challenge: how to utilize sensitive data for AI/ML while adhering to data protection laws. This article provides practical strategies and architectural approaches to resolve the conflict between innovation and compliance.

Managing data archiving and retention in multi-cloud and hybrid environments presents significant challenges for enterprises. This article explores how to develop and implement a consistent and compliant strategy that optimizes costs and ensures data availability.

Choosing the optimal data protection architecture in hybrid and multi-cloud environments demands a strategic approach that considers RTO/RPO, cyber threats, data sovereignty, and cost optimization. This choice is critical for business continuity and compliance amidst an increasingly complex IT landscape.

Implementing automated, end-to-end Data Lineage is critical for meeting escalating regulatory requirements and enabling effective internal audits. This solution demands significant investment but mitigates high risks and costs associated with non-compliance.

Increasing regulatory pressure and geopolitical factors make data residency and sovereignty critical for any enterprise handling sensitive information or operating in international markets. This article offers practical architectural solutions for CIOs/CTOs.

Selecting the right architectural approach for modernizing legacy systems is a critical decision for any CTO. This article compares the Strangler Pattern and the Modular Monolith, offering a practical framework for making an informed choice.