Deploy Machine Learning Model Easily

Building a Machine Learning (ML) model is only half the journey — deploying it into a live product environment is where the real value emerges.

For Product Managers (PMs), understanding the deployment process is essential to ensure smooth collaboration with data scientists and engineers.

Deploying ML models involves more than just technical steps — it requires aligning the model with business objectives, planning for ongoing maintenance, and ensuring ethical, reliable performance.

In this article, we will learn about this

1. What Does Model Deployment Mean?
2. The Complete ML Model Deployment Lifecycle
3. Types of Model Deployment: Batch vs. Real-Time Predictions
4. Challenges in ML Model Deployment
5. Monitoring and Maintenance: Ensuring Continuous Performance

1. What Does Model Deployment Mean?

Model deployment refers to the process of integrating a trained machine learning model into a live environment where it can make predictions on real-world data.