Machine learning (ML) models can deliver powerful insights and predictions, but only if they are reliable, scalable, and maintainable in production environments. How do you ensure your ML models are production-ready? Here are some best practices to follow.
Before you train, test, or deploy your ML models, you need to make sure your data is accurate, consistent, and representative of the real-world scenarios you want to solve. You can use various techniques to validate your data, such as data profiling, data quality checks, data cleansing, data augmentation, and data splitting. You should also document your data sources, formats, schemas, and transformations for future reference and reproducibility.
Data validation through quality checks and EDA is a pivotal step that ensures thorough understanding of the data. With this knowledge, you can easily set up safeguards to catch data drift in production, thus future-proofing your ML pipeline.
Not all ML models are created equal. Depending on your problem domain, data characteristics, performance metrics, and resource constraints, you need to select the most suitable model for your use case. You can use various criteria to compare and evaluate different models, such as accuracy, precision, recall, F1-score, ROC curve, AUC, MSE, MAE, R2, and so on. You should also consider the complexity, interpretability, and generalizability of your models.
When choosing a model, it's not just important to have the one with the best accuracy. Establishing a baseline with heuristics or a model predicting the most common class can help to inform whether or not a model will actually be beneficial for business outcomes.
Once you have chosen your model, you need to test it thoroughly to ensure it meets your expectations and requirements. You can use various methods to test your model, such as cross-validation, hold-out validation, k-fold validation, bootstrapping, and so on. You should also test your model on new and unseen data, as well as on edge cases and outliers. You should also monitor your model's performance over time and update it as needed.
Model validation is a continuous process. Before deployment, * Define what success means to you, and set KPIs * Test your model on unseen and untrained data * If you have limited data, use or cross, hold-out or k-fold validation to ensure your model works as intended and is well tested * Tune your hyperparameters, opt for Grid search or random search for finding the best parameters for your model * Use multiple evaluation metrics like F-score, confusion matrix, R-squared etc. * Compare the performance of different models and consider benchmarking against existing systems. * Monitor your model's performance over time and if the model evaluation does not meet the desired criteria, go back to earlier steps and reiterate.
After you have tested your model, you need to deploy it to a production environment where it can serve predictions to your end-users or applications. You can use various tools and platforms to deploy your model, such as cloud services, containers, APIs, web frameworks, and so on. You should also ensure your model is secure, scalable, robust, and compliant with any regulations or standards.
Something to be considered before developing your model is the time required for inference. Suppose you have 2 models with similar performance, opt for the simpler model which will allow faster inference and decision-making in production. This will benefit end users and the business alike.
Deploying your model is not the end of the story. You need to maintain your model regularly to ensure it stays relevant, accurate, and reliable. You can use various techniques to maintain your model, such as logging, auditing, debugging, error handling, feedback collection, retraining, and so on. You should also communicate with your stakeholders and users to get their feedback and suggestions for improvement.
ML models are not built in isolation. You need to collaborate with your team members and other experts to ensure your models are aligned with your business goals, user needs, and ethical principles. You can use various tools and practices to collaborate with your team, such as version control, code review, documentation, testing, deployment pipelines, and so on. You should also share your learnings and challenges with your team and the wider ML community.