Master Docker for real-world AI & ML workflows β Dockerfiles, Compose, Docker Model Runner, Model Context Protocol (MCP)
What you will learn
Run and manage Docker containers tailored for AI/ML workflows
Containerize Jupyter notebooks, Streamlit dashboards, and ML development environments
Package and deploy Machine Learning models with Dockerfile
Publish your ML Projects to Hugging Face Spaces
Push and pull images from DockerHub and manage Docker image lifecycle
Apply Docker best practices for reproducible ML research and collaborative projects
LLM Inference with Docker Model Runner
Setup Agentic AI Workflows with Docker Model Context Protocol (MCP) Toolkit
Build and Deploy Containerised ML Apps with Docker Compose
Add-On Information:
Get Instant Notification of New Courses on our Telegram channel.
Noteβ Make sure your ππππ¦π² cart has only this course you're going to enroll it now, Remove all other courses from the ππππ¦π² cart before Enrolling!
- Conquer Environment Inconsistencies: Master Docker to create isolated, consistent environments for all your ML and GenAI projects, ensuring models execute identically across development, testing, and production stages.
- Ensure Unwavering Reproducibility: Learn to package entire AI project dependencies and codebases into immutable images, fostering transparent, verifiable, and easily shareable research and development workflows.
- Architect Agile MLOps Pipelines: Gain the skills to integrate Docker seamlessly into your MLOps strategy, accelerating the build, test, and deployment cycles for complex machine learning models and applications.
- Optimize Generative AI Workloads: Discover strategies for efficiently containerizing and serving resource-intensive LLMs and other generative AI models, enhancing scalability and performance for inference at scale.
- Implement Advanced Agentic AI Paradigms: Dive into the Docker Model Context Protocol (MCP) Toolkit, enabling you to build sophisticated, context-aware agentic AI systems that interact dynamically and intelligently.
- Maximize Hardware Resource Utilization: Apply Docker best practices for effectively leveraging underlying hardware, including GPUs, ensuring your deep learning and high-performance AI tasks run with optimal efficiency.
- Bridge the Dev-to-Prod Gap for AI: Develop robust, portable containerized solutions that allow for seamless transition of AI models and applications from local development to any cloud or on-premise production environment.
- Orchestrate Complex AI Applications: Learn to compose multi-service AI architectures, combining model servers, data pipelines, and frontends into cohesive, scalable systems using Docker’s powerful orchestration capabilities.
- Foster Collaborative AI Development: Implement containerization strategies that standardize development environments across teams, drastically reducing integration issues and accelerating shared project progress.
- Fortify AI System Security: Understand crucial container security principles, from building lean images to managing runtime permissions, protecting your AI intellectual property and sensitive data throughout the deployment lifecycle.
- PROS:
- Highly Practical and Project-Oriented: Delivers immediately applicable skills for real-world AI and ML project implementation.
- Cutting-Edge Agentic AI Integration: Features modern tools like MCP, preparing learners for advanced, intelligent system development.
- Comprehensive AI/ML Lifecycle Coverage: Addresses the entire journey from development to deployment with specific AI/ML tooling.
- Strong Emphasis on Reproducibility and Scalability: Teaches foundational practices for building robust, shareable, and scalable AI solutions.
- CONS:
- Assumes Basic AI/ML Understanding: Learners will benefit more if they possess foundational knowledge of AI/ML concepts to fully contextualize the Docker applications.
English
language