Tag: Infra to Data Science Series
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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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Data Science Portfolio and Resume for Infra Engineers (Infra to Data Science Series, Part 25)
Your infrastructure projects are already a data science portfolio. This part translates an operations resume into outcomes a data team reads and turns your telemetry work into two or three projects that get the interview.
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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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Capacity Forecasting From Infrastructure Telemetry (Infra to Data Science Series, Part 22)
A straight line on a seasonal metric tells you when the average crosses your limit, not when the weekly peak does. Forecast with Holt-Winters, read the date off the upper prediction band, and plan against the risk you can afford.
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Anomaly Detection on Metrics and Time Series for Infra Telemetry (Infra to Data Science Series, Part 21)
Static thresholds on seasonal infrastructure metrics measure the time of day, not trouble. Deseasonalise with STL, score the residual with a median and MAD based z, and reach for an isolation forest when several metrics move together.
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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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MLOps for Infrastructure Engineers, the Operations You Already Run (Infra to Data Science Series, Part 16)
Most of MLOps is the operational discipline infrastructure engineers already have. This part maps your runbooks, CI and monitoring onto a model, and names the two or three things that are genuinely new.
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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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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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