Category: Infra to Data Science
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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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Probability and Distributions for Infra Telemetry (Infra to Data Science Series, Part 11)
Fit named distributions to your own latency and arrival data in Python, and see exactly where a clean fitted curve understates the tail that breaches your SLO.
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Statistics for Infra Engineers, Percentiles Over Averages (Infra to Data Science Series, Part 10)
The percentiles you already trust from SLOs, made the core of statistics for infra data. Why the mean sat at the 68th percentile, why mean plus two standard deviations gave an impossible negative latency, and how a change significant at p 5.57e-05 moved the median by 0.13 ms.
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NumPy and pandas for Infra Engineers Who Know awk and jq (Infra to Data Science Series, Part 9)
Translate the awk and jq habits you already have into vectorised NumPy and pandas. Why iterrows ran 8,700 times slower than a column expression, how to dodge the copy trap, and the dtype choices that shrank a frame from 32.8 MB to 7.3 MB.
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Getting Data Into Python From SQL, APIs and Monitoring (Infra to Data Science Series, Part 8)
Turn last part’s hand export into a repeatable pull. How to read infrastructure data straight into pandas from SQL, an HTTP API and JSON monitoring, parameterised and correctly typed.
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Infrastructure Telemetry as a Dataset for Data Science (Infra to Data Science Series, Part 7)
Your monitoring data already is a dataset. This part reframes infrastructure telemetry as rows and columns, and shows why a counter has to become a rate before it can be a feature.
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Reproducible Data Work for Infra Engineers, Venvs, Git and Notebooks (Infra to Data Science Series, Part 6)
You already enforce reproducibility in ops with pinned images and config in Git. Here is how to apply the same three habits, one pinned environment, clean notebook version control and fixed seeds, to your metrics analysis so it survives a fresh clone.
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Python for Data Work, Past Automation Scripting (Infra to Data Science Series, Part 5)
Your automation scripts already move data. This part turns that habit into data code: load a month of cluster metrics with pandas, catch the dtype trap that silently breaks a metric column, and see why iterrows is slower than the plain loop you already write.
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Data Science Transition Roadmap and Timeline for Infra Engineers (Infra to Data Science Series, Part 4)
How long the move from infrastructure to data science really takes, broken into four phases you can hold against a calendar, with the sideways move that beats quitting for a bootcamp.
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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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