01 / Platform
Distributed ML platforms
Feature platforms, automated extraction, hyperparameter search and training pipelines over billions of events — Kafka, Flink, Spark, Druid, Kubernetes.
Constantine Gurnov — ML Platform & AI Infrastructure
Non-deterministicsystems, held toengineering contracts.
Seventeen years building the layer underneath machine learning — distributed streaming platforms, real-time scoring at internet scale, brain–computer interfaces, and now the observability and evaluation harnesses that let probabilistic models run in production without behaving like one.
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01Practice
The hard part was never the model. It was everything that had to stay true around it.
A model returns a guess. A platform has to return a guarantee — a schema that holds, a latency budget that is met, a failure that is detected and recovered from rather than shipped downstream.
My work sits on that seam. At Sift it meant anomaly detection over billions of events a day. At Neuralink it meant real-time, partially observable neural signal under hard safety constraints. Today it means wrapping LLMs and agent swarms in the same discipline: observe, detect, intervene, stabilise, measure.
02 / Reliability
LLM observability
Logs, metrics and traces across LLM pipelines. Schema drift, malformed output, latency variance and downstream failure, surfaced before users meet them.
03 / Evaluation
Agentic simulation
Production-derived failure distributions replayed as stochastic simulation — trajectory evaluation, release gates and automated rollback policy.
04 / Real time
Streaming & scoring
Near real-time scoring and anomaly detection at internet scale, including adversarial defence against traffic that is actively trying to evade you.
02Signal
Measured outcomes, not adjectives.
Each figure below is attached to a shipped system. The detail behind them lives in Work.
100×
Scale-up of BCI model training and evaluation, to billions of neural data points — Neuralink.
50K
Peak queries per second through real-time ML scoring and anomaly detection — Sift.
~100%
Structured-output success rate, up from ~71%, via prompt hardening, validation and bounded recovery.
40%
Share of traffic under adversarial attack that a bot-detection system was built to counter — Sift.
03Work
Mass Identity. Neuralink. Sift. Machine Zone. UBS.
Five environments with one thing in common: the cost of being wrong was measurable. Regulated derivatives infrastructure, safety-critical neural implants, adversarial fraud traffic, and now autonomous agents.
04Open source
Public repositories, written the way the production versions were.
Observability case studies, agent-swarm cores, control-loop notebooks and ML platform reference material — published under github.com/hyperscaleailabs.