AI/ML Resources

Building an AI/ML Data Lake With Apache Iceberg

Learn how Apache Iceberg powers AI/ML data lakes with time travel and schema flexibility, shown via a full Python-based feature store implementation.

May 20, 2025

by Anant Kumar

· 2,258 Views · 1 Like

Secrets Sprawl and AI: Why Your Non-Human Identities Need Attention Before You Deploy That LLM

Your RAG implementation can expose secrets in some unexpected ways. Secure your LLM deployments and scrub knowledge bases to prevent your secrets from leaking.

May 20, 2025

by Dwayne McDaniel

· 2,918 Views · 1 Like

Operational Principles, Architecture, Benefits, and Limitations of Artificial Intelligence Large Language Models

Large Language Models (LLMs) are advanced AI systems that generate human-like text by learning from extensive datasets and employing deep learning neural networks.

May 19, 2025

by Venkata Suresh Babu Chilluri

· 2,211 Views · 1 Like

Navigating the LLM Landscape: A Comparative Analysis of Leading Large Language Models

Here is an in-depth analysis comparing top LLMs, including OpenAI and its exciting contenders like LangChain, Anthropic, Cohere, Google, and more.

Updated May 19, 2025

by Jorge Torres

· 3,501 Views · 2 Likes

AI-Driven Test Automation Techniques for Multimodal Systems

Learn how AI-powered test automation improves reliability and efficiency in multimodal AI systems by addressing complex testing challenges effectively.

May 19, 2025

by Gaurav Sharma

· 2,037 Views · 4 Likes

The Smart Way to Talk to Your Database: Why Hybrid API + NL2SQL Wins

Discover why the future of database interaction — it's a smart hybrid of solid backend architecture and AI-powered NL2SQL assistance.

May 19, 2025

by Dmitry Narizhnykh

CORE

· 6,791 Views · 3 Likes

Integrating Model Context Protocol (MCP) With Microsoft Copilot Studio AI Agents

Model Context Protocol (MCP) integrates seamlessly with Copilot Studio, enabling AI agents to access real-time data and interoperate.

May 19, 2025

by Aravind Nuthalapati

CORE

· 22,529 Views · 1 Like

The Full-Stack Developer's Blind Spot: Why Data Cleansing Shouldn't Be an Afterthought

Full-stack developers often focus on clean code but neglect clean data, leading to performance issues, security vulnerabilities, and frustrated users.

May 16, 2025

by Farah Kim

· 3,282 Views · 2 Likes

How Can Developers Drive Innovation by Combining IoT and AI?

Discover how developers can drive innovation by combining IoT and AI to create transformative solutions and unlock new opportunities across industries.

May 16, 2025

by Harris Anderson

· 3,455 Views · 1 Like

AI-Driven Root Cause Analysis in SRE: Enhancing Incident Resolution

This article examines how AI is transforming root cause analysis (RCA) in Site Reliability Engineering by automating incident resolution and improving system reliability.

May 16, 2025

by Nikhil Kapoor

· 3,981 Views · 5 Likes

Developers Beware: Slopsquatting and Vibe Coding Can Increase Risk of AI-Powered Attacks

Slopsquatting and vibe coding are fueling a new wave of AI-driven cyberattacks, exposing developers to hidden risks through fake, hallucinated packages.

May 14, 2025

by Aminu Abdullahi

· 4,172 Views · 2 Likes

Unlocking AI Coding Assistants: Generate Unit Tests

This blog post instructs on creating qualitative unit tests for a Spring Boot application using an AI coding assistant and its capabilities and limitations.

May 14, 2025

by Gunter Rotsaert

CORE

· 3,569 Views · 2 Likes

Beyond Code Coverage: A Risk-Driven Revolution in Software Testing With Machine Learning

Machine learning-driven, risk-based testing targets critical risks, automates test selection, and improves software reliability.

May 14, 2025

by Gopinath Kathiresan

· 3,111 Views · 1 Like

Intro to RAG: Foundations of Retrieval Augmented Generation, Part 1

Retrieval-augmented generation (RAG) retrieves relevant information from external sources to improve the accuracy and reliability of responses, making it a powerful tool.

May 14, 2025

by Jennifer Reif

CORE

· 3,041 Views · 3 Likes

Ethical AI in Agile

Learn all about ethical AI integration for Agile teams with four key guardrails: Data Privacy, Human Value, Output Validation, and Transparent Attribution.

May 14, 2025

by Stefan Wolpers

CORE

· 2,306 Views · 2 Likes

Agentic AI for Automated Application Security and Vulnerability Management

Explore how agentic AI enables several agents to jointly detect, fix, and verify security vulnerabilities in your codebase.

May 13, 2025

by Vamsi Kavuri

CORE

· 11,748 Views

Fraud Detection Using Artificial Intelligence and Machine Learning

AI/ML is revolutionizing fraud detection with automated, adaptive, real-time anomaly detection, despite challenges like interpretability.

May 13, 2025

by Nikhil Kapoor

· 2,317 Views · 3 Likes

How AI Agents Are Transforming Enterprise Automation Architecture

AI agents enhance enterprise automation through multi-layered architectures, improving efficiency and handling complex tasks across various sectors.

May 13, 2025

by Nishant Bijani

· 2,313 Views

The Role of Retrieval Augmented Generation (RAG) in Development of AI-Infused Enterprise Applications

Explore how RAG enhances enterprise AI apps by improving contextual accuracy, relevance, and response quality across real-world use cases.

May 13, 2025

by Ram Ravishankar

· 2,103 Views

AI-Based Threat Detection in Cloud Security

Learn how AI enhances cloud security with advanced threat detection methods like supervised learning, LLMs, and self-healing systems, tackling modern challenges.

May 12, 2025

by Tanvir Kaur

· 3,738 Views

The Latest AI/ML Topics