Learn how AI, observability, predictive maintenance, and resilience are helping service organizations move beyond reactive operations and improve uptime.
R&D succeeds when teams build just enough infrastructure to validate the highest-risk technical assumptions without over-engineering or over-researching the problem.
AI infrastructure isn’t about GPUs. Most issues come from storage, networking, data pipelines. If GPU utilization is low, check the infrastructure first, not the model.
Unbounded retries and autoscaling can turn minor latency into cascading outages. API reliability must be bounded and load-aware to prevent retry storms.
CV data issues keep recurring. I built cv-quality — a toolkit to audit datasets, catch annotation errors, find mislabeled samples, and streamline labeling.
A practical approach to enhancing DAG failure detection using AI to improve pipeline reliability and enable proactive intervention in large-scale data environments.
We analyzed 1,000 data pipeline incidents across 500+ environments and found that code-related failures still account for ~10% of all data quality issues.
Distributed AI systems fail faster than humans can respond, making traditional response insufficient. Self-healing systems use telemetry and automation to recover early.
Agentic AI transforms DevOps from reacting to incidents to systems that understand, decide, and act on their own, reducing toil and enabling autonomous infrastructure.
Autoscaling isn’t real elasticity — it’s slow, reactive, and can mislead. Use demand metrics, keep warm capacity, and pair with circuit breakers & observability.
CI/CD-driven modernization of data platforms, improving release speed, observability, and reliability through automation, parallelization, and job-level telemetry.
Enterprise GIS platforms blend IT & OT, offering vital operational insight. To protect critical systems, secure the boundary with zero-trust principles and segmentation.
