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:
01
Data Collection
Collect and label data
02
Data Preprocessing
Clean and transform data
03
Feature Engineering
Create relevant features
04
Model Training
Train and validate models
05
Model Evaluation
Test performance metrics
06
Deployment
Put model in production
07
Monitoring
Track performance over time
Data is King
The difference between a prototype and production-ready model often lies in data quality:
Time on Data
80%
Of ML projects
Data Quality
10x
More important than model
ROI Improvement
200%
With better data
Data Quality Checklist
+Data is representative of production situations
+No data leakage between train and test sets
+Labels are consistent and accurate
+Missing values are correctly handled
+Outliers are identified and treated
+Feature distributions are stable over time
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:
Method
Latency
Scalability
Complexity
REST API
Medium
Good
Low
Batch Inference
N/A
Excellent
Low
Streaming
Low
Good
High
Edge Deployment
Very Low
Limited
Medium
Monitoring & Maintenance
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)
Fintech Startup
99.9%
Uptime achieved
50ms
P95 latency
€150K
Annual savings
By setting up a robust MLOps pipeline, this startup could continuously update and monitor their fraud detection model, resulting in better performance and lower operational costs.