Welcome to the Machine Learning Hub, your one-stop destination for all things related to machine learning!
Get ready to embark on an exciting journey into the realm of AI and discover how machines can learn and make intelligent decisions. Our blog articles are crafted with simplicity and clarity in mind, making complex machine learning concepts easy to understand for everyone. Whether you’re a beginner or an experienced practitioner, we’ve got you covered with informative and insightful content. Explore the fascinating world of algorithms, models, and data as we delve into supervised and unsupervised learning, reinforcement learning, and more. Discover practical applications in various domains like healthcare, finance, and autonomous vehicles. From introductory guides to advanced techniques, we’re here to help you demystify machine learning and unlock its potential. Join us on this journey as we unravel the secrets of machine learning and empower you to build intelligent systems that can analyze data, make predictions, and drive innovation.
Let’s shape the future together with the power of machine learning!
There are many ways to get more from the models and GPU infrastructure you already have. Quantization, optimized kernels, and better inference engines can all help, but speculative decoding is especially useful because it can increase generation speed without simply adding more GPUs. There are now several approaches to speculative decoding. Traditional methods use a …
Speed Up LLM Inference with DSpark Speculative Decoding Read More »
Today, we’re introducing Gemini Omni 1.1 Flash, a new suite of creative controls and generative video capabilities to support developers. Gemini Omni brought real-world reasoning to generative creation, and today’s updates make Omni 1.1 production-ready for professional use via the Gemini API in Google AI Studio. Whether you’re building generative video workflows, creative tools, or …
A support request lands in the queue, and something has to work out what it is about before anyone can answer it. The hand goes to the prompt. That usually works, which is exactly why it became the reflex. It is also the slowest way to do it, the most expensive, and the one that …
RAG Is Not the Whole Toolkit: The NLP Techniques Real Problems Still Need Read More »
Introduction Running a small language model locally is straightforward. Running one productively — inside a real development workflow, with proper context, tool access, and iteration speed — is a different problem entirely. The gap between “I got a model responding in my terminal” and “I have a local AI setup that actually improves how I …
Today, we’re introducing Gemini 3.5 Transcribe, our most precise speech-to-text model yet, designed for intelligent voice interactions. Unlike conventional speech recognition models that struggle with background noise, complex jargon, and disfluency cleanup, Gemini 3.5 Transcribe converts raw audio directly into accurate, polished, formatted text. Across our products like the Gemini app and on Android, we’ve …
When RAG retrieval disappoints, the advice AI engineers hear today is almost always “add a reranker”. Ask why a reranker works, and the answer usually stays at the architecture level: it is a cross-encoder, it applies attention over the query and the passage together, it is fine-tuned on relevance labels. All of that is true, …
Introduction Most developers see Python dataclasses as a shortcut for avoiding repetitive dunder methods like __init__ and __repr__. So at first look, it seems like a simple way to write less code and move faster. In practice, dataclasses are designed to reduce boilerplate in data-focused classes while keeping behavior clear and under your control. Instead …
addresses the fundamental challenge of slow, sequential token generation in language model inference. DFlash speculative decoding support for CPUs was recently enabled in vLLM v0.25.0. In our testing with Qwen3.5-9B on an r8i AWS instance, powered by Intel® Xeon® 6 processors with Performance-cores, DFlash increased average token generation throughput to 3.92x that of the autoregressive …
Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash Read More »
Muse Glimmer is gaining attention in the local AI community and is being compared with Qwen’s 27B-class models. In many cases, it is performing better, especially for local coding and agentic workflows. Meta looks strong in the open-model space, and with a few more iterations, models like this could start competing closely with proprietary systems. …
Run Muse Glimmer for Local Vibe Coding with llama.cpp, DFlash, and Pi Read More »
This post was co-authored with Max Silfverberg (Data Scientist, AI Solutions Lead), Antti Hallavo (Lead AI Software Engineer), and Pontus Huotari (Lead Data Scientist). We work at Alma Media, a Finnish digital services, marketplaces and media company. One of our focus areas is developing AI/ML solutions for real estate listing services, where understanding image content plays an important role. services handle hundreds of thousands of listings a year. Most of those come with …
Why We Fine-Tuned SigLip (And Why That’s Not Always the Right Call) Read More »
