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Introduction NVIDIA Nemotron-3 is a family of open models built for reasoning, coding, chat, and agentic workflows. The Nano variant packs strong language understanding into a 4-billion-parameter model that can be fine-tuned on a single 24GB GPU, making it practical for teams who want to adapt a capable base model to their own domain without renting a large training cluster. In this tutorial, we fine-tune Nemotron-3-Nano-4B on a customer support dataset. After training, the m

Introduction Most AI tutorials show you a text box. You type, the model replies, and the whole exchange stays on screen. That covers the mechanics of calling an LLM, but it leaves out what makes voice AI feel genuinely different: the question comes from a microphone, the answer comes back as speech, and the whole thing happens without touching a keyboard. This tutorial builds a working voice AI agent from scratch in Python. Press Enter to start recording, speak your question,

Introduction Most AI chatbot demos are stateless: every message you send is treated as the first. The model has no idea what you said three turns ago, cannot refer back to details you shared earlier, and cannot build a coherent conversation over time. This is the biggest gap between a demo and a real chatbot. In this tutorial, we fix that. We build an AI Chatbot with Memory that maintains the full conversation history across every turn, passes it to the model on each request,

You followed the tutorial. You copied the code. Your AI chatbot answers questions perfectly on your laptop. Then you try to ship it. The API times out under real load. The vector search returns garbage when the query doesn't match training examples exactly. There is no error handling, so one bad request crashes the whole service. You have no idea if it is even working correctly because there is no logging. The chunking strategy that worked on your sample PDF breaks on a scann

A production-ready full-stack inventory management system that eliminates spreadsheet chaos, provides real-time stock visibility, and automatically alerts you when inventory falls below reorder thresholds.

Course: RAG Evaluation Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your ability to build the two foundational components of any RAG evaluation workflow: a golden dataset and a retrieval quality report. Without a golden dataset, no evaluation metric has meaning. Without retrieval evaluation, you cannot tell whether failures come from the retrieval stage or the generation stage. By completing this assignment, you will have a

Course: Docker for AI Apps Level: Medium to Advanced Type: Individual Duration: 7 to 10 days Objective This assignment tests your ability to design and operate a multi-container Docker system for an AI application. You will configure container-to-container networking using a user-defined bridge network, orchestrate a multi-service stack with Docker Compose, build and containerize a FastAPI AI REST API with session management and health checks, apply Docker best practices incl

Course: Docker for AI Apps Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your ability to work with Docker's core building blocks: running and inspecting containers, writing a production-ready Dockerfile, containerizing a Python AI application, and persisting data across container restarts using named volumes. By completing this assignment, you will have built and deployed a fully containerized multi-turn AI chatbot that retai

Course: RAG from Scratch Level: Medium to Advanced Type: Individual Duration: 7 to 10 days Objective This assignment tests your ability to build the retrieval and generation stages of a RAG pipeline from scratch. You will implement cosine similarity without external vector search libraries, build a similarity search function, design a grounding-focused prompt template, and assemble a complete end-to-end RAG system that retrieves context and generates accurate, grounded answer

Course: RAG from Scratch Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your ability to build the foundational stages of a RAG pipeline: loading documents, extracting clean text, attaching metadata, enriching documents with LLM-generated keywords, and splitting them into retrievable chunks. By completing this assignment, you will have built a reusable knowledge base preparation pipeline that you can apply to any document colle

Knowledge workers waste 19% of their time searching for information. Enterprise RAG Systems change that—delivering instant, accurate answers from your documents with verifiable sources. Discover how hybrid search, multi-document intelligence, and hallucination prevention turn scattered institutional knowledge into your competitive advantage.

Executive Summary The Project Research Assistant is a comprehensive multi-agent system designed to streamline the entire research and...

The tech landscape is constantly evolving, and developers are always faced with the question: "What should I learn next?" For MERN stack...

Master the skills that Fortune 500 companies are desperately seeking – and get ahead of 90% of data scientists who only know theory Why...

Transform your cooking experience with AI that turns random ingredients into delicious meals! 73% of households now struggle with food...

Project Objective: Develop a web-based application that aids students and faculty in managing, analyzing, and extracting insights from...

Disclaimer : This assignment sample is designed as a demonstration of Codersarts' expertise and capabilities in delivering big data...

Struggling with your Exploratory Data Analysis and Predictive Modeling assignment? Our experts are here to help! Get guidance on...

What is Big Data? Big Data refers to datasets that are too large, complex, and dynamic to be effectively handled by traditional data...

Introduction In this blog, we’ll take a big step forward by creating your very first DAG in Apache Airflow. A Directed Acyclic Graph...