| File Name: | AI Development with Grok, Qwen2.5, Deepseek & ChatGPT |
| Content Source: | https://www.udemy.com/course/build-ai-apps-with-qwen-25-deepseek-ollama/ |
| Genre / Category: | Other Tutorials |
| File Size : | 3.3 GB |
| Publisher: | udemy |
| Updated and Published: | June 09, 2025 |
What you’ll learn
- Understand what are large language models (LLMS) and how it works
- Build AI-powered applications using Deepseek, Qwen2.5 and Ollama
- Setting Up and Running Qwen 2.5 and DeepSeek Locally Using Ollama
- Create UI Application that Interacts with Large Language Model such as Qwen and Deepseek
- Use Ollama CLI with Qwen2.5 and Deepseek
- Basic command-line proficiency (executing scripts, installing packages)
- Master fine tuning with Qwen2.5
- Learn AI Development With Grok
- Learn AI Development with ChatGPT
Break Free from the Cloud—Build AI on Your Terms
For years, cloud-based AI has been the go-to solution for developers. The convenience of API-driven models made it easy to integrate AI into applications without worrying about infrastructure. However, this convenience comes with trade-offs—high costs, data privacy concerns, and reliance on third-party providers.
As AI adoption grows, more developers are rethinking their approach and turning to self-hosted AI models that run entirely on their local machines. This shift isn’t just about reducing cloud expenses—it’s about full control, performance, and independence.
Why Developers Are Moving to Local AI
Performance Without Latency
Cloud AI introduces delays. Each request must travel across the internet, interact with remote servers, and return results. Running AI locally eliminates network lag, making AI-driven applications significantly faster and more responsive.
Privacy and Data Security
Many industries—especially healthcare, finance, and legal sectors—require strict data security. Sending sensitive information to cloud providers raises privacy risks. By running AI models locally, developers keep their data in-house, ensuring compliance with security regulations.
Cost Efficiency
Cloud-based AI pricing often scales unpredictably. API calls, storage, and processing costs can quickly add up, making long-term AI development expensive. Local AI eliminates recurring fees, allowing developers to work with AI at no extra cost beyond initial hardware investment.
Customization and Optimization
Cloud AI models come as pre-trained black boxes with limited flexibility. Developers who want fine-tuned AI for specific use cases often hit restrictions. Self-hosted models allow for deeper customization, training, and optimization.
Key Tools Powering Local AI Development
To build AI applications without cloud dependencies, developers are turning to three powerful tools:
Grok – Is a cutting-edge large language model developed by xAI, designed for advanced reasoning, problem-solving, and knowledge generation, providing developers access to powerful AI capabilities via API integration rather than local deployment.
ChatGPT — a conversational AI developed by OpenAI, capable of generating human-like text responses across a wide range of topics. With models available through OpenAI’s API and increasingly through local and private deployments via open-source alternatives and fine-tuned versions, ChatGPT empowers developers to build chatbots, virtual assistants, and intelligent content-generation systems without relying entirely on cloud services.
Qwen 2.5 – A robust language model designed for text generation, automation, and reasoning. Unlike cloud-based AI, it runs entirely on local hardware, giving developers full control over processing and execution.
Deepseek – An efficient AI model that applies distillation techniques to reduce computational costs while maintaining high performance. This makes it ideal for developers who need lightweight, high-speed AI without requiring powerful GPUs.
Ollama – A streamlined model management tool that simplifies loading, running, and fine-tuning AI models locally, ensuring smooth deployment and integration into projects.
Building AI on Your Own Terms
Whether you’re working on intelligent automation, AI-driven assistants, or advanced text generation, local AI offers unparalleled control and flexibility.
Developers who make the shift gain:
Full AI Independence – No reliance on cloud APIs or external services.
Privacy & Control – All processing happens on local machines, ensuring data security.
Hands-on AI Development – Direct interaction with models instead of relying on third-party platforms.
Optimization Capabilities – The ability to fine-tune AI models for performance and efficiency.
Scalability Without Costs – AI usage no longer depends on pay-per-use pricing models.
As the AI landscape evolves, local AI isn’t just an alternative—it’s the future. By understanding how to deploy, optimize, and build with self-hosted models, developers can break free from cloud restrictions and unlock AI’s full potential.
Ready to Take AI Into Your Own Hands? Let’s Begin!
Who this course is for:
- Software engineers looking to develop applications using local LLMs like Qwen and DeepSeek
- Full-stack developers looking to integrate LLM models into web applications
- Students and researchers exploring the execution of local AI models
- Python programmers seeking to integrate AI into their projects
- AI/ML beginners keen to gain practical experience in AI development
DOWNLOAD LINK: AI Development with Grok, Qwen2.5, Deepseek & ChatGPT
AI_Development_with_Grok__Qwen2.5__Deepseek___ChatGPT.part1.rar – 1.5 GB
AI_Development_with_Grok__Qwen2.5__Deepseek___ChatGPT.part2.rar – 1.5 GB
AI_Development_with_Grok__Qwen2.5__Deepseek___ChatGPT.part3.rar – 350.1 MB
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