Category: Tech Notes
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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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Operations Mindset vs Data Science Mindset for Infra Engineers (Infra to Data Science Series, Part 3)
An operations mindset chases zero failure; a data science mindset budgets error and ships something useful before it is perfect. Here is how an infrastructure engineer switches between the two without losing the discipline that makes them worth hiring.
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Transferable Skills for Infra Engineers Moving to Data Science (Infra to Data Science Series, Part 2)
An operator moving into data science already holds more of the job than the posting admits. How to audit your skills against a real data science job description, which transfer, which reshape, and the two gaps to close first.
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What a Data Scientist Actually Does, Compared to Infra, SRE and VMware Work (Infra to Data Science Series, Part 1)
If you run infrastructure, keep an SRE rotation or administer VMware, you are not starting data science from zero. What a data scientist actually does, where your skills already transfer, and where to start.
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Red Hat AI vs the Managed Clouds, the Verdict and What to Learn Next (Red Hat Gen AI Series, Part 30)
Capstone of the series: where Red Hat AI beats AWS, Google, Azure and IBM watsonx, where it loses on cost, and an honest verdict on self hosting Granite versus renting a frontier API.
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Air Gapped and Disconnected Red Hat AI Deployments (Red Hat Gen AI Series, Part 29)
Stand up a self hosted Granite assistant in a disconnected data center: mirror images with oc-mirror v2, carry model weights across the gap, trust your registry, and lock down egress.
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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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