Local AI Mastery: Build & Deploy AI Models with DeepScaleR & Ollama

What you will learn:

Set up DeepScaleR and Ollama for on-device AI model execution.
Run AI models locally without relying on cloud services.
Build a high-performing AI chatbot using DeepScaleR and FastAPI.
Develop an AI-powered math solver for complex equations.
Deploy DeepScaleR models using REST APIs.
Integrate DeepScaleR models with Gradio for user-friendly web interfaces.
Compare DeepScaleR's performance against leading cloud-based models.
Master fine-tuning techniques like LoRA and QLoRA.
Optimize AI inference speed for low-latency responses.
Build a robust AI-powered code assistant

Description

Unlock the power of on-device AI with our comprehensive course on mastering DeepScaleR and Ollama. Learn to build, fine-tune, and deploy sophisticated AI models locally, eliminating the need for expensive cloud APIs and enhancing data privacy. This hands-on training will guide you through the process of leveraging the DeepScaleR-1.5B model—a fine-tuned version of DeepSeek-R1-Distilled-Qwen—optimized for advanced tasks like math reasoning, code generation, and AI automation.

Starting from the fundamentals, you'll set up DeepScaleR and Ollama on your preferred operating system (Mac, Windows, or Linux) and learn to execute AI models locally. You'll then embark on practical projects, building a fully functional AI chatbot using DeepScaler and deploying it via FastAPI. You'll also create a high-performance AI math solver capable of tackling complex equations.

A key focus is on fine-tuning DeepScaleR using cutting-edge techniques like LoRA and QLoRA, allowing you to tailor the model to specific domains such as finance, healthcare, or legal analysis using custom datasets. You'll gain expertise in developing a robust AI-powered code assistant that efficiently generates, debugs, and explains code. We’ll also delve into optimizing for low-latency responses, comparing DeepScaleR's performance against OpenAI's o1-preview model, and utilizing Gradio to build intuitive, web-based interfaces for your AI applications.

This course caters to individuals of all skill levels, from AI beginners to experienced developers. Whether you're an AI developer, data scientist, software engineer, student, or tech enthusiast, this course will empower you to develop, fine-tune, and deploy AI models without external cloud dependencies. You'll gain control over your data, reduce costs, and achieve faster response times by running AI models directly on your device.

By the end, you'll possess multiple locally deployed AI applications—chatbots, math solvers, code assistants, and more—all fine-tuned for peak performance. Join us and become an expert in local AI deployment!

Curriculum

Introduction to DeepScaleR & Ollama

This introductory section lays the groundwork for your AI journey. You'll begin by understanding the capabilities of DeepScaleR, a fine-tuned version of DeepSeek-R1-Distilled-Qwen-1.5B, and learn how to utilize its strengths for various AI tasks. Next, you’ll explore Ollama's role in streamlining local model deployment. The section will cover setting up your environment on Mac, Windows (using WSL), or Linux, ensuring a smooth transition into the practical projects. A foundational module on Python basics will equip even beginners with the essential programming knowledge necessary to navigate the subsequent challenges.

Building AI Applications with DeepScaler

This section dives into hands-on application development. You'll tackle two key projects: building an AI-powered math solver that can solve complex mathematical problems and an AI chatbot utilizing DeepScaler, allowing you to interact with a locally running intelligent conversational model. These practical projects reinforce concepts learned in the introductory section and showcase the real-world application of DeepScaleR and Ollama.