The notorious "it works on my machine" problem has evolved into a critical bottleneck for AI teams as they move from simple experimentation to complex agentic workflows. This bootcamp, led by industry authority Gourav Shah, is engineered to transform you into a technical lead capable of orchestrating full-stack AI ecosystems. You will master the logic of high-performance containerization, from optimizing Dockerfiles for GPU acceleration to leveraging the brand-new Docker Model Runner for local LLM inference. More importantly, you will stay ahead of the curve by implementing Model Context Protocol (MCP) toolkits, enabling your AI agents to interact securely with external data sources and tools through standardized, containerized gateways. From standardizing research environments to deploying production-grade API stacks with Docker Compose, this curriculum provides the operational backbone required for modern AI leadership.

This Course Offers

• Docker Model Runner (DMR) Mastery: You will be able to pull and run OCI-compliant Large Language Models directly from Docker Hub, exposing OpenAI-compatible APIs for local development without the need for external cloud tokens.
• Agentic AI with MCP: Master the Model Context Protocol Toolkit to connect your LLM agents to secure, containerized tools like Google Calendar, Notion, and local file systems through a unified gateway.
• Streamlined MLOps Pipelines: Learn to containerize specialized AI interfaces like Streamlit dashboards and Gradio demos, ensuring your stakeholders see exactly what you see.
• Multi-Service AI Architecture: Gain the skills to use Docker Compose to manage complex stacks involving vector databases, inference engines, and frontend applications in a single, reproducible file.

Why We Love This Course

1. AI-Specific Optimization: It is clear that this course goes beyond "Docker 101," focusing specifically on the challenges of large model weights, CUDA drivers, and high-memory environments.
2. Next-Generation Tooling: You can tell the curriculum is forward-thinking, prioritizing the Docker MCP Toolkit which is currently redefining how AI agents access local and remote tools.
3. Hugging Face Integration: The approach feels incredibly practical for researchers, providing a direct roadmap for publishing containerized projects to Hugging Face Spaces.
4. Operational Reproducibility: What sets it apart is the focus on DevContainers, allowing your entire team to spin up identical, pre-configured AI development environments with a single click.

The difference between a data scientist and an AI engineer is the ability to package a vision into a portable, scalable reality. The question is no longer just how well your model performs, but how easily it can be integrated into a larger system. This bootcamp provides the technical authority and containerized framework to own the entire AI lifecycle. Start building your portable AI future today.

• Data Scientists and ML Engineers who want to productionize their workflows
• AI/ML Practitioners looking to containerize and deploy models easily
• DevOps Engineers supporting AI teams and looking to build ML-ready pipelines
• AI Hobbyists and Learners who want to run LLMs or dashboards locally using containers
• Anyone tired of “it works on my machine” issues in ML environments

• Basic understanding of Python — you don’t need to be an expert, but you should be comfortable running scripts or working in notebooks.
• Familiarity with Machine Learning concepts — knowing what a model is, and having used libraries like scikit-learn, pandas, or TensorFlow will help.
• Laptop with Docker/Rancher installed — we’ll walk you through setting up Docker Desktop for Windows, macOS, or Linux.
• A GitHub account (recommended) — for accessing project code and pushing your own.
• Curiosity to build real-world AI/ML projects with Docker — no prior Docker experience is required!