Category: Career Aspirants
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Prompt Patterns That Survive Contact With Real Users (AI Engineering Series, Part 3)
A production prompt is not a string, it is an ordered stack of blocks with different change frequencies. Here is the layout that survived real users, the cache mistake that cost us $612 a month, and why a longer prompt turned out to be 7.7 times cheaper.
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Calling a Model From Python: SDKs, Messages and Parameters That Matter (AI Engineering Series, Part 2)
Your first model call from Python, with the message format explained, the parameters that actually change behaviour on current reasoning models, and the two failure modes that corrupt output silently before anyone notices.
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What an AI Engineer Actually Does, vs ML Engineer, Data Scientist and Backend Developer (AI Engineering Series, Part 1)
An AI engineer builds applications on models somebody else trained. Here is what the job contains day to day, how it differs from ML engineering, data science and backend work, and the order I would learn it in.
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Data Science Architect Career Path, and What to Learn Next (Data Science Series, Part 30)
Stack Overflow folded data scientist into an AI/ML engineer group in 2025, and the reported pay gap was about 44,500 dollars. Here is the architect path out of senior data scientist: four ladders, the real skills gap, and a twelve month plan.
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Responsible AI, Model Risk and Governance That Survives an Audit (Data Science Series, Part 29)
Model governance is not a policy document. It is an inventory, an independent challenge function, and a monitoring trail that lets you answer who approved this model and on what evidence. Here is how I build one that holds up when an auditor asks.
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GPU Cost, Scale and Sizing Decisions for Machine Learning Workloads (Data Science Series, Part 28)
Nine percent average GPU utilisation on a cluster I inherited turned out to be the whole story. Here is how I size, buy and scale machine learning compute, with current instance rates and the arithmetic that decides each call.
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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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Feature Stores and Training Serving Skew in Machine Learning (Data Science Series, Part 23)
A model that scores well offline and badly in production is usually not a modelling failure. It is two pieces of code computing the same feature differently. Here is how skew happens, what a feature store fixes, and when a shared library is the better answer.
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Serving Machine Learning Models: Batch, Real Time and Streaming (Data Science Series, Part 22)
Batch, real time and streaming are three different answers to the same question, and picking the wrong one costs money for months before anyone notices. Here is how I choose, with runnable FastAPI and batch scoring code for the churn project.
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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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