Tag: Machine Learning
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Data Science Interviews and Specialisation for Infra Engineers (Infra to Data Science Series, Part 26)
A data science loop runs four to six rounds, and the one that screens out switchers is the case and statistics round, not coding. Prepare for the interview that exists and specialise toward MLOps, where your operations background is an edge.
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Incident Prediction and AIOps, Honestly Assessed (Infra to Data Science Series, Part 24)
Incident prediction on your own telemetry usually fails on arithmetic, not modelling. A base rate check, an honest look at AIOps, and the narrow cases where prediction actually pays.
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Log Analysis and Clustering at Scale for Infra Telemetry (Infra to Data Science Series, Part 23)
Clustering raw log lines fails because ids and timestamps make every message unique. Mine templates with Drain3 first, then cluster the structure, and a day of logs collapses to a triage table you can read in a minute.
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Experiment Tracking and Model Registry for Infra Data (Infra to Data Science Series, Part 20)
Log every training run, register the good ones as immutable versions, and let a single champion alias decide what serves, so promotion and rollback each become one line. Built on the incident classifier, with the real failures that bite.
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Pipelines and CI/CD for Machine Learning on Infra Data (Infra to Data Science Series, Part 19)
A pipeline and a CI gate that refuse to promote a model unless it clears a metric on a time aware split, built on the incident classifier from earlier parts, with the real failures that break each stage.
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Monitoring Models in Production for Drift and Decay (Infra to Data Science Series, Part 18)
A served model decays quietly. Here is how to catch it with input and prediction drift checks, a KS test, and a PSI threshold you can page on, all on your own telemetry.
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Serving a Model Batch and Real Time for Infrastructure Engineers (Infra to Data Science Series, Part 17)
Your trained model becomes two deployables, a batch scoring job on a schedule and a real time endpoint. Here is how an infrastructure engineer serves both, and which one most infra work actually needs.
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Feature Engineering on Operational Data for Infra Telemetry (Infra to Data Science Series, Part 15)
One lag feature took a fair 0.879 to 0.934 on a month of infra telemetry, while a rate of change added nothing. How to build past only lag, rolling, standard deviation and EWMA features that lift a score without leaking.
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Model Evaluation Without Fooling Yourself (Infra to Data Science Series, Part 14)
A single new feature lifted this model AUC from 0.889 to 0.975 on infra telemetry, and none of it was real. How to catch leakage in features, preprocessing and folds, and read a score you can defend.
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Your First Model, From Metric Threshold to Trained Classifier (Infra to Data Science Series, Part 13)
Your production alert rule is a one feature classifier, and it probably catches almost nothing. Here is how to measure it honestly and beat it with a trained model whose operating point you actually choose.
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Machine Learning Fundamentals for Infra Engineers (Infra to Data Science Series, Part 12)
Machine learning fundamentals for infra engineers, using your own incident labels: why accuracy lies on rare events, and how a baseline, recall and precision decide a real model.
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Data Science Architect Career Path, and What to Learn Next (Data Science Series, Part 30)
Stack Overflow folded data scientist into an AI/ML engineer group in 2025, and the reported pay gap was about 44,500 dollars. Here is the architect path out of senior data scientist: four ladders, the real skills gap, and a twelve month plan.
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About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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