Category: AI/ML
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How Neural Networks Learn, Without the Math (GenAI Series, Part 6)
Neurons, layers, and weights in plain English. How a neural network learns by guessing, measuring its error, and nudging its dials, repeated across millions of examples.
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What Generative AI Can and Cannot Do (GenAI Series, Part 5)
An honest look at generative AI: what it is genuinely good at, where it quietly fails, and why hallucination comes from the same machinery that writes its best answers.
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What a Model Really Is (GenAI Series, Part 4)
A model is not a database of answers. It is one large function that predicts the next token, built from billions of parameters. What model sizes and open vs closed weights really mean.
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How We Got from If-Statements to ChatGPT (GenAI Series, Part 3)
AI did not appear overnight. The road to ChatGPT runs through four eras, hand-written rules, machine learning, deep learning, and the 2017 transformer, explained in plain English.
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The GenAI Words Everyone Uses, and What They Actually Mean (GenAI Series, Part 2)
Model, tokens, parameters, inference, embeddings, hallucination: the words everyone uses about generative AI, sorted into build time and use time and explained in plain English.
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What Generative AI Actually Is, Explained Without the Hype (GenAI Series, Part 1)
Generative AI does not look answers up. It builds new text, images and code by predicting one piece at a time. Here is what that really means, in plain English, with GPT-4o and Llama as the examples.
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NVIDIA AI Enterprise Explained: What’s in the Suite, How It’s Licensed, and Whether It’s Worth It
A practitioner’s breakdown of what NVIDIA AI Enterprise actually bundles, how its per-GPU licensing lands on VMware vSphere, and when the subscription earns its keep versus when you can skip it.
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VMware Private AI vs Red Hat OpenShift AI vs Hyperscaler Managed AI: An Honest Verdict (Private AI Series, Part 30)
Three ways to run enterprise inference, three very different trade-offs. A straight comparison of VMware Private AI Foundation, Red Hat OpenShift AI and hyperscaler managed AI, ending in a clear verdict.
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Guardrails and Responsible AI on VMware Private AI: What NeMo Guardrails Actually Stops (Private AI Series, Part 28)
Private does not mean safe. Here is how NeMo Guardrails wraps your models on VMware Private AI, the five rail types, and an honest line on what guardrails catch and what they do not.
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Fine-Tuning Models on VMware Private AI with NeMo Customizer: LoRA, Full SFT and When to Bother (Private AI Series, Part 27)
RAG is not always the answer. Here is how NeMo Customizer fine-tunes models on VMware Private AI, the difference between LoRA and full SFT, and an honest take on when customization beats retrieval.
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NVIDIA NIM Operator on VMware Private AI: The Reference Architecture for Declarative Model Serving (Private AI Series, Part 25)
The NIM Operator is the Kubernetes-native control plane for model serving on VMware Private AI. Here is how its CRDs, caching and autoscaling actually fit together, and the vGPU constraint that bites multi-GPU models.
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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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