Vision-Language Models - MultimodalAI - From Foundations to a Fully Local SmolVLM Demo

A deep, researcher-level exploration of Vision-Language Models in 2026, covering core architectures, training paradigms, and multimodal fusion mechanisms. The tutorial connects theory to practice through a fully local, real-time live demo system running entirely on-device, demonstrating how modern models unify visual perception and language reasoning without relying on cloud infrastructure. It also includes a SmolVLM-based local captioning demo, where a lightweight Vision-Language Model generates natural language descriptions from live camera input in real time.

E-Book - From Research to Production - Industrializing NLP and Large Language Models

E-book release : From Research to Production - Industrializing NLP and Large Language Model

Deploying a Scalable Machine Learning Service on Kubernetes

In today’s rapidly evolving tech landscape, deploying machine learning models efficiently and reliably in production environments is a critical skill. This tutorial series provides a hands-on, end-to-end guide for deploying a machine learning service on Kubernetes, designed specifically for beginners eager to master MLOps practices.

Building a Scalable data Processing Pipeline with Kafka and Docker Compose

Building a scalable data processing pipeline is crucial for applications that require efficient handling of large volumes of data in real-time.

Join the Community in AI, ML, Data Science, ,Computer Vision, Data Science, GenAI, NLP, MLOps, LLMOps – Let’s Learn Together

Join me on an exciting journey exploring the world of Artificial Intelligence, Machine Learning, and Data Science. Whether you’re just starting or have some experience, let’s break down complex topics and learn together. My mission is to make AI and ML approachable and practical for everyone.

Automate ML and LLM Workflow with GitHub Actions & CML

SAM 2 Advanced Object Segmentation for Images and Videos

SAM 2 (Segment Anything Model 2) is an advanced machine learning model designed for comprehensive object segmentation in both static images and dynamic videos. Developed by Meta AI Research, SAM 2 represents a significant leap forward in computer vision capabilities, offering real-time performance and zero-shot generalization. This tutorial will explore the key features of SAM 2, its architecture, and how to get started with using this powerful tool.

Multi-Modal Retrieval - Bridging Text and Images with BGE and CLIP

In today’s data-rich world, being able to retrieve relevant information across different modalities (text, images, audio) has become increasingly important. This post will guide you through creating a multi-modal retrieval system that combines text embeddings from BGE (Bidirectional Generative Encoder) and image embeddings from CLIP (Contrastive Language-Instrumental Pre-training) to index and query Wikipedia articles.

Running Ollama in Google Colab (Free Tier)

Ollama empowers you to leverage powerful large language models (LLMs) like Llama2,Llama3,Phi3 etc. without needing a powerful local machine. Google Colab’s free tier provides a cloud environment perfectly suited for running these resource-intensive models. This tutorial details setting up and running Ollama on the free version of Google Colab, allowing you to explore the capabilities of LLMs without significant upfront costs.

2-Introduction to LLM — [AIMagazine]

This chapter will be your comprehensive guide to navigating the fascinating world of LLMs. We’ll delve into their core concepts, exploring different types like autoregressive models and encoder-decoder models. You’ll discover the magic behind self-attention, a mechanism that allows LLMs to focus on relevant information, and delve into the pre-training strategies that give them their vast knowledge. Finally, we’ll showcase the real-world applications of LLMs, from powering chatbots and generating realistic dialogue to creating marketing copy and summarizing complex topics.