Tag: Data Science
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How to Become a Data Scientist From an Infra, SRE or VMware Background
If you run infrastructure, carry an SRE pager or administer VMware, you already own half of a data science career. Here is the order to build the other half, with a resource mapped to every step.
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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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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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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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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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