// MLOps Guides 2026 ⏱ ~3 min read

Deploy a Machine Learning Model with Docker and MLflow: Complete Tutorial (2026)

Ayub Shah

· 📅 April 2026 · 👤 ML engineers & data scientists

87%of ML projects never reach production

10xfaster with proper MLOps

45minto complete this tutorial

Want to learn how to deploy a machine learning model with Docker and MLflow? This step-by-step tutorial takes you from a trained model to a live API endpoint — using MLflow for tracking, Flask for serving, and Docker for containerization.

This complete deploy a machine learning model with Docker and MLflow guide is designed for ML engineers who know how to train models but haven’t deployed one to production yet. No prior Docker or web framework experience required.

Table of Contents

01Why Deploy ML Models with Docker & MLflow?Essential
02PrerequisitesSetup
03Train & Log Model with MLflowStep 1
04Find MLflow Model PathStep 2
05Create Flask APIStep 3
06Write the DockerfileStep 4
07Build & Run ContainerStep 5
08Test Your APIStep 6
09TroubleshootingHelp
10What to Do NextNext

01 Why Deploy a Machine Learning Model with Docker and MLflow?

You trained a model. It hits 94% accuracy. You're excited. Then someone asks the question that stops every new ML engineer cold: "How do we actually use it?"

Training a model is step one. Deploying it — making it available to other systems, APIs, or end users — is an entirely different skill. Without a repeatable deployment process, your model lives forever in a Jupyter notebook that only runs on your laptop.

This tutorial shows you how to deploy a machine learning model with Docker and MLflow step by step. You'll use MLflow to track and package your model, Flask to serve predictions, and Docker to containerize everything so it runs identically on any machine.

⚡ What you'll build

Train model
→
Log with MLflow
→
Flask API
→
Docker
→
Live endpoint

⏱ Total time

About 45 minutes to complete this deploy a machine learning model with Docker and MLflow tutorial.

02 Prerequisites

🐍 Python 3.8+ — check with python --version
🐳 Docker Desktop — free at docker.com
🧠 Basic ML knowledge — you know what training a model means
📦 Python packages — run pip install mlflow scikit-learn flask gunicorn

⚠️

DOCKER MUST BE RUNNING

Make sure Docker Desktop is open and shows "Running". The most common mistake when learning how to deploy a machine learning model with Docker and MLflow is running Docker commands while the daemon is still sleeping.

03 Train and Log Model with MLflow

Create train.py. This is the first step in learning how to deploy a machine learning model with Docker and MLflow — you need a trained model to deploy.

Pythontrain.py
import mlflow
import mlflow.sklearn
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

mlflow.set_experiment("iris-deployment")

with mlflow.start_run():
    X, y = load_iris(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_train, y_train)

    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)

    mlflow.log_param("n_estimators", 100)
    mlflow.log_param("random_state", 42)
    mlflow.log_metric("accuracy", accuracy)
    mlflow.sklearn.log_model(model, "model")

    print(f"Model accuracy: {accuracy:.4f}")
    print(f"Run ID: {mlflow.active_run().info.run_id}")

				
					
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						import mlflow
import mlflow.sklearn
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
 
mlflow.set_experiment("iris-deployment")
 
with mlflow.start_run():
    X, y = load_iris(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_train, y_train)
 
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
 
    mlflow.log_param("n_estimators", 100)
    mlflow.log_param("random_state", 42)
    mlflow.log_metric("accuracy", accuracy)
    mlflow.sklearn.log_model(model, "model")
 
    print(f"Model accuracy: {accuracy:.4f}")
    print(f"Run ID: {mlflow.active_run().info.run_id}")

					

			

Run: python train.py

04 Find Your MLflow Model Path

Bash
find mlruns -name "MLmodel" -print

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						find mlruns -name "MLmodel" -print

Your MODEL_PATH looks like: mlruns/0/abc123def456/artifacts/model

05 Create a Flask API

Create app.py to serve your model:

Pythonapp.py
import numpy as np
from flask import Flask, request, jsonify
import mlflow.pyfunc

app = Flask(__name__)

MODEL_PATH = "mlruns/0/YOUR_RUN_ID_HERE/artifacts/model"
model = mlflow.pyfunc.load_model(MODEL_PATH)

@app.route("/health", methods=["GET"])
def health():
    return jsonify({"status": "healthy"})

@app.route("/predict", methods=["POST"])
def predict():
    data = request.get_json(force=True)
    features = np.array(data["features"]).reshape(1, -1)
    prediction = model.predict(features)
    return jsonify({"prediction": int(prediction[0])})

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000)

				
					
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						import numpy as np
from flask import Flask, request, jsonify
import mlflow.pyfunc
 
app = Flask(__name__)
 
MODEL_PATH = "mlruns/0/YOUR_RUN_ID_HERE/artifacts/model"
model = mlflow.pyfunc.load_model(MODEL_PATH)
 
@app.route("/health", methods=["GET"])
def health():
    return jsonify({"status": "healthy"})
 
@app.route("/predict", methods=["POST"])
def predict():
    data = request.get_json(force=True)
    features = np.array(data["features"]).reshape(1, -1)
    prediction = model.predict(features)
    return jsonify({"prediction": int(prediction[0])})
 
if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000)

					

			

Create requirements.txt:

TXTrequirements.txt
flask==3.0.0
mlflow==2.11.0
scikit-learn==1.4.0
numpy==1.26.4
gunicorn==21.2.0

				
					
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						flask==3.0.0
mlflow==2.11.0
scikit-learn==1.4.0
numpy==1.26.4
gunicorn==21.2.0

					

			

06 Write the Dockerfile

DOCKERDockerfile
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY app.py .
COPY mlruns/0/YOUR_RUN_ID_HERE/artifacts/model ./model
EXPOSE 5000
ENV MODEL_PATH=./model
CMD ["gunicorn", "--bind", "0.0.0.0:5000", "--workers", "2", "app:app"]

				
					
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						FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY app.py .
COPY mlruns/0/YOUR_RUN_ID_HERE/artifacts/model ./model
EXPOSE 5000
ENV MODEL_PATH=./model
CMD ["gunicorn", "--bind", "0.0.0.0:5000", "--workers", "2", "app:app"]

					

			

07 Build and Run the Container

Bash
docker build -t ml-model-api:v1 .
docker run -p 5000:5000 --name iris-api -d ml-model-api:v1
docker ps
docker logs iris-api

				
					
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						docker build -t ml-model-api:v1 .
docker run -p 5000:5000 --name iris-api -d ml-model-api:v1
docker ps
docker logs iris-api

					

			

08 Test Your API

Bash
curl http://localhost:5000/health
curl -X POST http://localhost:5000/predict -H "Content-Type: application/json" -d '{"features": [5.1, 3.5, 1.4, 0.2]}'

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						curl http://localhost:5000/health
curl -X POST http://localhost:5000/predict -H "Content-Type: application/json" -d '{"features": [5.1, 3.5, 1.4, 0.2]}'

Expected response: {"prediction": 0} — your model is now successfully deployed!

09 Troubleshooting Common Errors

⚠️ Port 5000 is already in use

Run docker run -p 5001:5000 ml-model-api:v1 and test at port 5001.

⚠️ Model not found

Run find mlruns -name "MLmodel" to get the correct path, then update Dockerfile and rebuild.

10 What to Do Next

📊

Add logging

Track predictions and monitor drift.

☁️

Deploy to cloud

Push to AWS ECS or Google Cloud Run.

🔄

CI/CD pipeline

Auto-retrain and redeploy with GitHub Actions.

📖 External resources: MLflow Docs • Docker Docs • Flask Docs

📚