Learn to package, distribute, and manage Machine Learning applications using Docker. This course covers creating reproducible ML environments, containerizing training workflows, and preparing models for deployment.
Prerequisites Python and Command Line Basics
Level:
Reproducible Environments
Create consistent and shareable development and execution environments for ML projects using Dockerfiles.
Dependency Management
Manage complex Python dependencies (like TensorFlow, PyTorch, scikit-learn) within containers.
Containerizing Workflows
Package ML training scripts and inference applications into Docker containers.
Data Handling in Containers
Implement strategies for managing datasets and model artifacts with Docker volumes and bind mounts.
Optimized ML Images
Build efficient Docker images for ML applications using techniques like multi-stage builds.
GPU Acceleration
Configure Docker containers to utilize NVIDIA GPUs for accelerated ML tasks.