Blog
# Generative AI
# Machine Learning
# MLOps
# Artificial Intelligence
# Data Science
Vibe Youtubing with NotebookLM: The MLOps Coding Course Gets a Video Upgrade in Under 48 Hours
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.
Médéric Hurier · Sep 23rd, 2025
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
Médéric Hurier · Aug 26th, 2025
In this article, Médéric Hurier tests three versions of Google's Gemini 2.5 models—Flash, Pro, and Deep Think—by challenging them to create a complex, multi-scene interactive birthday experience for his daughter. The experiment revealed an exponential gap in capability, with the advanced Gemini Deep Think model delivering a delightful, polished, and fully functional result that surpassed the other models and captivated his daughter.
# Machine Learning
# MLOps
# AI
# Gemini Deep Think
Vishakha Gupta · Aug 19th, 2025
From enterprise search to agentic workflows, the ability to reason across text, images, video, audio, and structured data is no longer a futuristic ideal: It’s the new baseline. AI solutions have come a long way in that journey, but until we embrace the need for rethinking how we deal with data, let go of patchwork solutions, and give it a holistic approach, we will keep slowing down our own progress.
# AI Agents
# Multimodal/Generative AI
# Knowledge graph and graph databases
# RAG
# Vector / Similarity / Semantic Search
Médéric Hurier · Aug 12th, 2025
A new paradigm called the "Agentic Cloud," driven by Generative AI, is set to disrupt the dominance of cloud hyperscalers. While today's giants like AWS and Azure rely on vast but complex service catalogs, this emerging model uses intelligent agents to translate high-level user intentions into fully provisioned and autonomously managed infrastructure. This approach threatens to commoditize the underlying cloud platforms, shifting the primary value from the provider's software ecosystem to the intelligence of the agent itself. Despite significant technical challenges in reliability and security, the Agentic Cloud promises to democratize access to elite infrastructure, enabling a new wave of innovation by shifting the industry from a catalog-driven to an intelligence-driven model.
# Agentic Cloud
# AI
# AI Agents
# Cloud Computing
# Data Science
# Generative AI Tools
Ayesha Imran · Aug 5th, 2025
In Part 2 of this hands-on tutorial, we continue building an automated knowledge graph pipeline using Google's Gemini 2.5 Flash and ApertureDB. While Part 1 focused on entity extraction and storage, this post dives into relationship extraction, graph creation, and interactive visualization. Using tools like LangChain, Pydantic, and PyVis, we define connections between entities, insert them into ApertureDB, and generate a visual representation of the graph. Optimized with batch processing and threading, this pipeline supports scalable, multimodal data applications. The tutorial concludes by showcasing real-world use cases—from semantic search and customer support to educational content and RAG pipelines—and lays the groundwork for future integrations with LLMs.
# Gemini 2.5
# ApertureDB
# LLMs
Ayesha Imran · Jul 30th, 2025
Learn how to combine Gemini 2.5 and ApertureDB to extract, deduplicate, and store structured entities—laying the foundation for automated knowledge graph creation.
# Knowledge graph and graph databases
# RAG
# Gemini
# ApertureData
Sonam Gupta · Jul 22nd, 2025
AI-assisted coding is on the rise, with tools like Copilot, Cursor, and Windsurf enabling a more intuitive, fast-paced approach known as “vibe coding.” Instead of carefully planning each line, developers now prompt, accept, and tweak code in real time—often feeling more like they’re jamming than programming. While this method can accelerate prototyping and spark creativity, it comes with risks: without foundational coding skills, it's easy to introduce silent errors. Sonam shares personal wins and frustrations with vibe coding and reminds us that while AI is a powerful partner, human judgment is still essential.
# AI-assisted Coding
# Vibe Coding
# Human Judgment
Soham Chatterjee · Jul 15th, 2025
Learn why stuffing prompts with excessive context actually hurts AI performance. This guide shows how irrelevant information confuses LLMs, the 'lost in the middle' effect, and proven techniques for cleaning and optimizing prompts to improve accuracy, reduce costs, and boost reliability in production AI systems.
# LLMs
# Prompt Bloat
# Machine Learning