Tag: MLOps
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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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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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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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Data Science Platform Architecture, a Reference Design With Trade Offs Named (Data Science Series, Part 27)
A finance director asked why a four day model took eleven weeks to reach a customer, and almost every answer turned out to be architectural. Here is the four plane reference design I would build today, with the cost and operations trade offs named in real numbers.
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CI/CD for Machine Learning Pipelines: Tests, Gates and Continuous Training (Data Science Series, Part 26)
A machine learning pipeline needs two things ordinary CI does not: data validation and a defensible opinion on model quality. Here is the fast lane, slow lane and quality gate I would build for a churn model, with the GitHub Actions workflow and the failures you will hit.
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Monitoring Machine Learning Models in Production: Drift, Decay and Silent Failure (Data Science Series, Part 25)
A model that breaks loudly gets fixed on Tuesday. A model that decays quietly gets fixed after a quarter of wasted budget. Here is how I monitor drift, estimate performance before labels arrive, and set thresholds nobody mutes.
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MLflow Experiment Tracking, Model Registry and Versioning (Data Science Series, Part 24)
A model you cannot reproduce is a rumour. Here is how I set up MLflow tracking, a model registry and aliases on the churn project, including the errors you will hit on a local file store.
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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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