Tag: generative-ai
-
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.
-
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.
-
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.
-
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.
-
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.
-
Building Enterprise AI with NVIDIA NeMo Microservices: From Data to Guardrails
The GenAI wave is no longer about just calling an LLM API. It’s about building reliable, scalable, secure, and continuously improving AI systems. While many teams are still experimenting with prompts, enterprises are moving toward something bigger: 👉 AI factories powered by microservices And that’s exactly where NVIDIA NeMo comes in. The Big Picture: Enterprise…
-
What is NVIDIA NeMo — and Why It Matters for Agentic AI
When people talk about AI systems, they often focus on models or APIs. But once you move beyond simple use cases, a bigger challenge appears: How do you control, guide, and manage AI behavior in real-world systems? This is where NVIDIA NeMo becomes critical. If NIM is the layer that runs AI models, then NeMo…
-
What is NVIDIA NIM — and Why It Matters for Modern AI Systems
When most people start learning AI, they focus on models—LLMs, vision models, embeddings, and so on. But in real-world systems, models alone are not enough. The real challenge is how to run these models reliably, at scale, and in a way that applications can actually use them. This is exactly where NVIDIA NIM comes into…
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.
You May Have Missed
DrJha