• 2025 Through the Lens of IoT World: A Year in Review

    I opened the analytics dashboard for iotworld.co and just stared at the bars rising and falling month by month. “Is this for real?” I thought to myself. The story those numbers were starting to tell wasn’t just about traffic. It was about reach, resonance, and connection across countries and communities… Read More ⇢

    2025 Through the Lens of IoT World: A Year in Review

  • Why Favoriot’s ML Infrastructure Reduces Costs

    When people see the phrase “No external pipelines. No separate ML infrastructure.”, it can sound technical or abstract. In practice, it describes a tangible benefit that directly affects how quickly, reliably, and cost-effectively an IoT solution can be built and maintained. This is precisely what the Favoriot Machine Learning feature… Read More ⇢

  • Why Favoriot’s Built-in Machine Learning Matters for AI Researchers and IoT Developers

    The moment IoT projects grow beyond simple dashboards, the same question always appears. “How do we make this system smarter without turning it into a research project?” For years, AI researchers and IoT developers have lived between two worlds. On one side, raw sensor data is streaming in nonstop. On… Read More ⇢

  • What Is Favoriot Edge Gateway and How Does It Work?

    Favoriot Edge Gateway is built for one convenient reason. Real IoT systems rarely talk straight to the cloud. They speak to gateways first. Factories, buildings, farms, campuses, and cities already have gateways in place. Some speak LoRa. Some speak Modbus. Some collect data from dozens or hundreds of devices sitting… Read More ⇢

    What Is Favoriot Edge Gateway and How Does It Work?

  • Traditional Machine Learning vs Large Language Models (LLMs)

    Both approaches learn from data, yet they serve different purposes and behave very differently in practice. Here is a clear, practical comparison. 1. Core purpose Traditional machine learning Large Language Models (LLMs) 2. Type of data Traditional ML LLMs 3. Model scope Traditional ML LLMs 4. Training approach Traditional ML… Read More ⇢

    Traditional Machine Learning vs Large Language Models (LLMs)

  • Machine Learning vs Deep Learning in IoT

    Most IoT use cases do not need Deep Learning.Traditional Machine Learning is usually enough. Now let me explain why, in a very grounded way. Start from the nature of IoT data Most IoT systems deal with: This type of data is structured and repetitive. For this, classic ML works very… Read More ⇢

    Machine Learning vs Deep Learning in IoT

  • Why Favoriot Chooses Machine Learning Over LLMs

    Favoriot started to implement AI into its IoT platform by using traditional Machine Learning techniques, and many wondered when Favoriot would use Large Language Models (LLMs). Below are the reasons Favoriot started with Machine Learning before moving to Agentic AI. 1. How is this ML different from LLMs The ML… Read More ⇢

    Why Favoriot Chooses Machine Learning Over LLMs

  • Official Announcement -Favoriot Evolves into an AIoT Platform with Built-in Machine Learning

    We are pleased to announce a significant upgrade to the Favoriot Platform. Favoriot is no longer positioned solely as an IoT platform. With the introduction of native Machine Learning capabilities, Favoriot now operates as a complete AIoT Platform, enabling predictive, adaptive, and data-driven intelligence directly on top of IoT data.… Read More ⇢

    Official Announcement -Favoriot Evolves into an AIoT Platform with Built-in Machine Learning

  • Zero-Trust Security for IoT: What It Really Means Beyond the Buzzword

    Zero-trust security has become a familiar phrase in technology discussions. It appears in whitepapers, conference slides, and vendor pitches. In enterprise IT, the idea is relatively well understood. Never assume trust. Always verify. Limit access by default. When applied to Internet of Things systems, the concept becomes more complicated. IoT… Read More ⇢

    Zero-Trust Security for IoT: What It Really Means Beyond the Buzzword