Duration 3 days – 21 hrs
Overview
This advanced course equips technical professionals and AI strategists with the skills to evaluate, deploy, and integrate open-source large language models (LLMs) like DeepSeek, Mistral, LLaMA, and Claude (Anthropic) into enterprise environments. The course focuses on comparing open vs. proprietary models, exploring private AI deployment strategies, and understanding how to reduce costs while maintaining performance, control, and data privacy.
Objectives
- Compare the architecture, capabilities, and performance of top open-source LLMs vs. proprietary solutions like OpenAI and Gemini.
- Assess business and technical trade-offs: cost, latency, privacy, and flexibility.
- Deploy open-source models like DeepSeek, Mistral, and LLaMA in controlled environments.
- Integrate open-source AI into existing systems using APIs, containers, or on-prem tools.
- Plan for hybrid or fully private AI setups to meet organizational goals and compliance standards.
Audience
- AI engineers, machine learning developers, and data scientists
- IT architects, DevOps professionals, and technical project managers
- CIOs, AI strategists, and R&D leaders exploring open-source AI alternatives
- Teams planning to build or migrate to private/local LLM infrastructure
Prerequisites
- Strong foundation in Python and APIs
- Familiarity with AI/ML model concepts and cloud platforms
- Prior experience with deploying machine learning models or AI applications
Course Content
Day 1: Open-Source vs. Proprietary LLMs – Landscape & Comparison
- Understanding LLM evolution: open vs. closed
- Key model comparisons:
- DeepSeek: Open LLM with tool-use and code capabilities
- Mistral: Lightweight yet performant open-source transformer
- LLaMA 2/3: Meta’s fine-tunable base model
- Claude: Privacy-focused model from Anthropic
- Benchmarking performance (accuracy, latency, scalability)
- Licensing considerations: Open vs. commercial restrictions
Day 2: Deployment Models and Private AI Infrastructure
- Use cases for private AI deployment (finance, healthcare, government, etc.)
- Hosting options: On-premises, edge, VPC cloud, or hybrid
- Containerized deployment (Docker, Kubernetes, Hugging Face Transformers)
- Introduction to inference servers: vLLM, TGI, Ollama, and LM Studio
- Practical lab: Deploy a lightweight Mistral model in a secure container
Day 3: Integration Strategies and Cost-Saving Considerations
- Using open models via API vs. running locally
- Integrating with enterprise apps, RAG pipelines, and workflows
- Prompt optimization and fine-tuning basics
- Security, compliance, and audit trails in open AI use
- TCO (Total Cost of Ownership) comparison: proprietary vs. open
- Final project: Build a basic private chatbot using DeepSeek or LLaMA model
Final Hands-On Project:
Deploy and integrate an open-source LLM (e.g., DeepSeek or Mistral) in a simulated business environment. Compare performance, cost, and output with a proprietary tool like GPT-4 or Claude.
