Machine Learning in Practice: From Data to Production

Building Machine Learning models in a Jupyter notebook is one thing. Getting them into production and keeping them there is a completely different story. This guide takes you through the complete process.

The ML Pipeline

A production-ready ML system consists of much more than just the model:

Data is King

The difference between a prototype and production-ready model often lies in data quality:

80%

Time on Data

Of ML projects

10x

Data Quality

More important than model

200%

ROI Improvement

With better data

Data Quality Checklist

Model Training Best Practices

Do's

Start simple, add complexity as needed
Use cross-validation
Track all experiments (MLflow, Weights & Biases)
Version control for data and code
Automate the training process

Don'ts

Jump straight to the most complex models
Focus only on accuracy
Forget to measure computational cost
Ignore model interpretability
Train without reproducibility

Deployment Strategies

There are various ways to deploy ML models, each with pros and cons:

Monitoring & Maintenance

Model performance degrades over time (concept drift)
Data distributions can change
New edge cases appear
Business requirements evolve

That's why continuous monitoring is essential:

Key Metrics to Monitor

Model accuracy and other performance metrics
Prediction latency and throughput
Input data distributions (data drift)
Error rates and types
Resource usage (CPU, memory, GPU)