Blog
# LLMs
# NeMo Guardrails
# PII
# HIPAA/FDA
Taming LLMs with NeMo Guardrails
LLMs can perform complex tasks like drafting contracts or answering medical questions, but without safeguards, they pose serious risks—like leaking PII, giving unauthorized advice, or enabling fraud. NVIDIA’s NeMo Guardrails provides a modular safety framework that enforces AI safety through configurable input and output guardrails, covering risks such as PII exposure, jailbreaks, legal liability, and regulatory violations. In high-stakes areas like healthcare, it blocks unauthorized diagnoses and ensures HIPAA/FDA compliance. Each blocked action includes explainable metadata for auditing and transparency, turning AI safety from a black-box filter into configurable, measurable infrastructure.
Kopal Garg · Nov 12th, 2025
Médéric Hurier · Nov 4th, 2025
Deploying AI agents in enterprises is complex, balancing security, scalability, and usability. This post compares deployment paths on Google Cloud—highlighting Cloud Run with IAP as the most secure and flexible option—and shows how teams can build powerful agents with ADK without losing the human touch.
# AI Agent
# Agentops
# Generative AI Tools
# Data Science
# Artificial Intelligence
Abby Morgan · Oct 28th, 2025
Modern LLMs are defined as much by how they’re trained as by what they learn. This post unpacks the often-overlooked foundations of that process: pretraining—the stage that shapes a model’s core reasoning and knowledge. Starting with ULMFiT’s breakthrough in transfer learning and InstructGPT’s formalized multi-stage pipeline, it explores how pretraining has evolved into a dynamic ecosystem of techniques, from instruction-augmented and multi-phase approaches to continual and reinforcement-based pretraining. Amid the growing complexity and shifting definitions, one truth remains: understanding pretraining is essential to understanding how language models think, reason, and behave.
# Language Models
# LLMs
Médéric Hurier · Oct 21st, 2025
When IT blocked every translation tool, Médéric Hurier decided not to wait. In just one lunch break, he built Slides-To-Translate — a fully automated Google Slides translator using Gemini 2.5 Flash, Colab, and Vertex AI — for only $0.04. His quick hack turned a bureaucratic bottleneck into a lightning-fast, secure, and reusable solution that proves anyone with a bit of code and curiosity can outpace corporate constraints.
# Generative AI Tools
# Data Sceince
# Programming
# Coding
# Hacking
Axel Mendoza · Oct 14th, 2025
Terraform becomes messy at scale—too much duplication, manual setup, and no orchestration. Terragrunt fixes this by automating state management, reducing repetition, and handling dependencies. In 2025, its new Stacks feature enables reusable infrastructure patterns, making it the better choice for multi-environment setups despite a small learning curve.
# DevOps
# IAC
# Terraform
# Terragrunt
# Tool Comparison
Médéric Hurier · Oct 7th, 2025
Inspired by the French show “C’est pas sorcier,” “It’s Not Artificial” uses Google’s Gemini multi-speaker text-to-speech to recreate its conversational learning style. The project automatically generates full audio episodes — complete with distinct voices, languages, and topics — from simple inputs. What began as a nostalgic experiment now hints at a future where AI-driven conversations make education and training more personal, engaging, and human-like.
# Artificial Intelligence
# Generative AI Tools
# Conversational AI
# Data Science
# Gemini
Vishakha Gupta · Sep 30th, 2025
Graphs are everywhere, but often misunderstood. This blog busts common myths about knowledge graphs, explains why they’re faster and more flexible than you think, and shows how AI can help build them.
# Knowledge graph and graph databases
# Dataset Preparation and Management
# Multimodal/Generative AI
Médéric Hurier · Sep 23rd, 2025
Using NotebookLM’s Video Overview, I turned my MLOps Coding Course from text into a full video series in just two days. What once felt like a month-long grind became a fast and creative process — demonstrating how AI can amplify expertise instead of replacing it.
# Generative AI
# Machine Learning
# MLOps
# Artificial Intelligence
# Data Science
Mats Eikeland Mollestad · Sep 16th, 2025
Machine learning pipelines are vulnerable to data and infrastructure errors that can disrupt production. By implementing smoke tests with both random and controlled synthetic data, teams can validate pipeline functionality and schema adherence before running full-scale jobs. This practice supports continuous integration and delivery, leading to fewer outages and more reliable deployments.
# ML Testing
# CI/CD
# Machine Learning
George Chouliaras, Antonio Castelli & Zeno Belligoli · Sep 9th, 2025
We share a pragmatic framework for evaluating LLM-powered applications in production. Anchored in high-quality human labels and a calibrated ‘LLM-as-judge’ approach, it turns subjective outputs into consistent, actionable metrics—enabling continuous monitoring, faster iteration, and safer launches at scale. We distill lessons from a year of building and operating this framework at Booking.com, with the aim to make evaluation a core practice in the GenAI development lifecycle.
# Gen AI
# Evaluation
# LLMs
# LLM Evaluation
Subham Kundu · Sep 2nd, 2025
As AI agents like Claude and Cursor integrate into enterprise workflows, organizations face critical security challenges around safe resource access. The Model Context Protocol (MCP) is establishing communication standards, while OAuth 2.1 and token exchange mechanisms provide authentication frameworks to balance AI capabilities with enterprise security requirements for sensitive corporate data.
# AI Agents
# MCP
# AI Security
# Machine Learning