ML Development & Deployment Workflow
Take your AI/ML models from experimentation to production with confidence. Learn how to train, evaluate, deploy, monitor, and continuously improve models using modern MLOps practices and tools.
The ML Production Gap: Most ML projects never make it to production. Models that perform well in notebooks often fail in real-world conditions due to data drift, infrastructure issues, or lack of monitoring. This workflow bridges that gap.
This workflow provides a complete path from model experimentation to production deployment and ongoing evaluation:
🎯 The MLOps Lifecycle
This workflow creates a continuous cycle: Experiment → Evaluate → Deploy → Monitor → Learn → Experiment. Each iteration improves model performance based on real production data.
Train models, track experiments, and iterate toward the best performing version
Set up your ML development environment with experiment tracking, version your datasets, train models with different hyperparameters, and systematically compare results to find the best approach.
Version Your Data
Use DVC or Delta Lake to version training datasets. Never train on unversioned data.
Set Up Experiment Tracking
Initialize MLflow or W&B at the start of your training script to log metrics, parameters, and artifacts.
Run Hyperparameter Sweeps
Use Optuna, Ray Tune, or W&B Sweeps to systematically explore hyperparameter space.
Compare & Select Best Model
Use experiment dashboards to compare runs and select the best performing model for evaluation.
import mlflow
import mlflow.pytorch # or mlflow.sklearn, mlflow.tensorflow
# Start experiment tracking
mlflow.set_experiment("my-model-experiment")
with mlflow.start_run():
# Log parameters
mlflow.log_params({
"learning_rate": 0.001,
"batch_size": 32,
"epochs": 100,
"model_architecture": "transformer",
"dataset_version": "v2.1"
})
# Train your model
model = train_model(config)
# Log metrics
mlflow.log_metrics({
"accuracy": 0.95,
"f1_score": 0.93,
"loss": 0.05,
"inference_time_ms": 12.5
})
# Log the model artifact
mlflow.pytorch.log_model(model, "model")
# Log additional artifacts
mlflow.log_artifact("confusion_matrix.png")
mlflow.log_artifact("feature_importance.json")💡 Pro Tip: Reproducibility First
Log everything: random seeds, library versions, data preprocessing steps, and environment details. Use pip freeze > requirements.txt and log it as an artifact.
Rigorously test models with offline benchmarks, bias checks, and production-like conditions
Before deployment, validate your model against held-out test sets, check for bias and fairness issues, stress-test with edge cases, and benchmark inference performance under production-like load.
💡 Pro Tip: Shadow Mode Testing
Before full deployment, run your new model in “shadow mode” - it receives real production traffic but its predictions aren't used. Compare its outputs against your current model to catch issues before they impact users.
Package, containerize, and deploy models with production-grade infrastructure
Best for: Real-time predictions, web applications
FastAPI, Flask, TensorFlow Serving
Best for: Large-scale inference, scheduled predictions
Apache Spark, AWS Batch, Airflow
Best for: Real-time data, event-driven predictions
Kafka, Flink, Kinesis
Package Model
Export model with dependencies (ONNX, TorchScript, or framework-native format)
Create Inference Service
Build FastAPI/Flask app with prediction endpoints, health checks, and input validation
Containerize
Build Docker image with model, dependencies, and serving code
Deploy to Infrastructure
Push to Kubernetes, cloud ML platform, or serverless environment
Progressive Rollout
Use canary deployments to gradually shift traffic (1% → 10% → 50% → 100%)
FROM python:3.11-slim
WORKDIR /app
# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Copy model and serving code
COPY model/ ./model/
COPY app.py .
# Health check endpoint
HEALTHCHECK --interval=30s --timeout=10s --retries=3 \
CMD curl -f http://localhost:8000/health || exit 1
# Run with Gunicorn for production
CMD ["gunicorn", "app:app", \
"--bind", "0.0.0.0:8000", \
"--workers", "4", \
"--timeout", "120"]💡 Pro Tip: Model Registry
Use MLflow Model Registry or similar to manage model versions. Tag models as “staging” or “production” and enable one-click rollbacks if issues are detected.
Track model performance, detect drift, and catch issues before they impact users
💡 Pro Tip: Ground Truth Logging
Log all predictions with unique IDs so you can join them with ground truth labels later. This enables you to calculate real accuracy metrics and identify when the model is struggling.
Close the feedback loop with A/B testing, retraining pipelines, and iterative improvement
Use production data and feedback to continuously improve your models. Set up A/B tests to validate improvements, automate retraining when drift is detected, and build a culture of experimentation.
Run controlled experiments comparing model versions. Measure business metrics (conversion, engagement) not just ML metrics.
Set up pipelines that automatically retrain when data drift exceeds thresholds or on a regular schedule.
Collect explicit user feedback (thumbs up/down) and implicit signals (clicks, conversions) to improve training data.
Regularly review model errors, categorize failure modes, and prioritize improvements based on impact.
Trigger Detection
Monitor for retraining signals: data drift, accuracy drop, scheduled interval, or manual trigger
Data Refresh
Pull latest production data, apply quality filters, and create new training/validation splits
Automated Training
Run training pipeline with same hyperparameters or trigger new sweep
Automated Evaluation
Run evaluation suite and compare against current production model
Promotion Decision
Auto-promote if metrics improve, or alert humans for review if uncertain
💡 Pro Tip: Champion/Challenger Pattern
Always have a “challenger” model training in the background. When it beats the current “champion” on evaluation metrics, automatically promote it to shadow testing, then to production via canary deployment.
Develop
Train & experiment
Evaluate
Test & validate
Deploy
Package & serve
Improve
Retrain & iterate
Monitor
Track & alert
Join the community to discuss MLOps best practices and share your workflow variations.