U AI & ML with Docker

Why Containers Matter More Than

Ever in the Age of AI
by Gourav Shah
The AI/ML Revolution is Here
AI is already ML models are growing
transforming every more complex and
industry harder to deploy

Need for scalable, portable, reproducible

environments
To scale AI/ML, we need tools that simplify and standardize workflows.
Docker vs VMs in ML
Feature Docker Virtual Machines

Startup Time § Fast ÷ Slow

Resource Use Efficient Heavy

Portability High Medium

Isolation High Very High

Dev Workflow Smooth Clunky

Why Docker in AI/ML?
Removes "works on my machine" issues

Simplifies setup and dependencies

Enables repeatable experimentation

Empowers seamless deployment across environments

Docker is the glue between models and production.

Why Should You Care?
If you are a:

i ML Engineer
reproducible experiments + scalable infra

v DevOps Engineer
deploy models with confidence

¼ Data Scientist
avoid environment hell

_ AI Hobbyist
run state-of-the-art models on your laptop

Containers make you 10x more productive in ML.

Where Docker Fits into the ML Workflow ?

Data Collection & Exploration.

Model Development & Training

Experiment Tracking

Model Packaging

Model Deployment

Monitoring & Updates

Common Tools + Docker in ML
Serving APIs
TensorFlow Serving FastAPI for
Tracking / TorchServe inference APIs Orchestration
MLflow, DVC, Docker Compose + Deployment
Weights & Biases Kubernetes
Hugging Face
Development /Kubernetes +
Jupyter, VS Code Docker = ready-to-
(dev environments) deploy models

6
Popular Use Cases for Docker in ML

Containerized Jupyter ML model training on MLflow + Docker for REST APIs for model
notebooks for research GPU clusters experiment tracking serving (Flask/FastAPI)

Deploying models to Portable inference on

Hugging Face Spaces edge devices
Who's Using Docker for AI/ML?
Netflix Uber Amazon NASA
Personalize Michelange SageMaker Containeriz
d content lo platform uses ed ML for
with ML built on Docker space
pipeline Dockerized under the simulations
containers workflows hood

Big names rely on containers to make AI reliable.

1. Uber: Streamlining ML Pipelines with Docker
Uber employs Docker to containerize its machine learning workflows, facilitating consistent
environments for development and deployment. This approach enhances the scalability and
reproducibility of their ML models, which are integral to services like ETA predictions and
dynamic pricing.

Source : https://www.uber.com/en-IN/blog/from-predictive-to-generative-ai/?
utm_source=chatgpt.com
2. Netflix: Orchestrating ML Workflows Using
Docker
Netflix utilizes Docker containers to manage and scale its machine learning workflows
efficiently. By containerizing their ML tasks, Netflix ensures consistent environments across
development and production, aiding in tasks like content recommendation and streaming
optimization.

Source : https://netflixtechblog.com/maestro-netflixs-workflow-orchestrator-ee13a06f9c78
3. Walmart: Scaling AI Solutions with Docker
Walmart leverages Docker to containerize its AI applications, facilitating scalable and efficient
deployment across its vast retail infrastructure. This strategy supports various use cases,
including inventory management and customer experience enhancements.

Source : https://medium.com/walmartglobaltech/machine-learning-platform-at-walmart-
b06819825ef7
4. Ingka Group (IKEA): Scalable MLOps with Docker
and Kubernetes
Ingka Group, the parent company of IKEA, adopted Docker and Kubernetes to build a robust
MLOps platform. This setup allows for dynamic scaling of AI/ML applications, improved
collaboration through uniform development environments, and enhanced security. The
containerized approach accelerates prototyping and deployment of new models, aligning with
IKEA's commitment to innovation.

Source : https://www.docker.com/customer-stories/ingka/?utm_source=chatgpt.com
5. NASA: Accelerating Data Analysis with Docker
NASA employs Docker containers to standardize and expedite its machine learning workflows,
particularly in processing vast amounts of satellite data. Containerization aids in maintaining
consistent environments, crucial for the reproducibility of scientific analyses.

Source : https://aws.amazon.com/solutions/case-studies/nasa-jpl-spot-case-study/?
utm_source=chatgpt.com
6. ZEISS Microscopy: Cross-Platform AI Model
Deployment
ZEISS, a leader in optics and optoelectronics, utilizes Docker to deploy AI models across various
platforms, including cloud and local Windows-based clients. By containerizing their AI
solutions, ZEISS ensures consistent performance and simplifies the distribution of complex
models, enhancing their microscopy software's capabilities.

Source : https://www.docker.com/customer-stories/zeiss/?utm_source=chatgpt.com
Docker in the world of LLMs / Agentic AI
Running Models with Docker Model Runner

Framework agnostic (TF, Torch,

Launch a ready-to-serve REST XGBoost)
Dockerized abstraction of any API in seconds Works with all major ML frameworks
trained model No need to write custom API code
Package your model with all
dependencies

Example: docker run -p 8080:8080 ghcr.io/mlc-ai/model-runner:latest

Docker + MCP Tooling
What's MCP? Model Context Protocol lets AI models access real-world tools

Docker + MCP =
Self-hosted MCP toolkits (Terraform, Kubernetes, CLI
agents)
Tool-aware autonomous agents

Example:

docker run -p 3000:3000 realops/kubernetes-mcp-

server:latest
Deploying NVIDIA NIM with Docker

GPU-accelerated, secure, scalable

REST interface for easy integration

Containerized inference microservices

(Mixtral, LLaMA2, etc.)

Example: docker run --gpus all -p 8000:8000 nvcr.io/nim/mixtral:latest

Agentic AI + Docker
Agentic AI = LLMs + Tools + Memory + Goals

Containerized toolchains

Isolated execution environments

Easy orchestration
(Docker Compose / K8s)

_ Build and deploy autonomous AI agents with ease.

Sample Agentic DevOps Setup

Agentic AI Framework Tool Containers

(LangGraph / BeeAI) (CLI tools, MCP tools)

Docker Networking or Model Containers

Kubernetes (LLMs, Embedders)

Reproducible. Scalable. Composable.

What You'll Learn in This Course
Build & run ML projects in Docker

Package Models with Dockerfiles

Track experiments and logs

Deploy ML models

Use MCP and agentic patterns for automation

What You'll Walk Away With

1
Hands-on Docker experience

2
A toolkit for AI/ML deployment

3
Understanding of real-world use cases

4
Readiness to tackle MLOps & AI automation
Let's Dockerize Your AI
Journey!
Start with your first Learn, build, deploy 4
containerized ML the DevOps way
project

Ready to become future-proof?

À Join the mission. Build real. Learn Docker for AI/ML.

Get Started with your AI/ML Journey

www.schoolofdevops.com
]www.schoolofai.dev