Category: AI/ML
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Errors, Retries, Rate Limits and Timeouts in LLM Applications (AI Engineering Series, Part 6)
Stock SDK retry settings send three requests in 1.5 seconds when the server asked you to wait two minutes. Here is what the defaults actually do, measured, and the retry policy I ship instead.
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Token Budgets, Context Limits and Cost per LLM Request (AI Engineering Series, Part 5)
One tool schema adds 389 tokens to every request. A newer tokenizer can add 30 percent to your bill with no price change. Here is how to measure token cost before a request leaves your process, and what our documentation assistant actually costs per 1,000 calls.
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Structured Output From an LLM: JSON Mode, Schemas and Validation That Does Not Fail Silently (AI Engineering Series, Part 4)
Constrained decoding guarantees the shape of a model response, not its truth. Here is how JSON mode, strict JSON schemas and a second validation layer fit together, with the errors you will actually hit.
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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.
Architect’s Toolkit
PJ’s Tools
VMware Cloud Foundation
- VCF Documentation
- VCF 9 Planning & Preparation Workbook
- VCF Bill of Materials (BoM)
- VMware Compatibility Guide
- VMware Interoperability Matrix
- VMware Configuration Maximums
- VMware Ports & Protocols
- VMware Hands-on Labs
- RVTools Download
Nutanix
AI & Cloud-Native Platform
- NVIDIA Build (Model Catalog)
- NVIDIA AI Enterprise Reference Architecture
- NVIDIA NIM Performance Benchmarking
- NVIDIA NGC Catalog
- NeMo Microservices Helm Chart
- Helm Charts Repository
- Hugging Face Models
Architecture & Design
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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