100 Days of Machine Learning on Databricks Day 7: The Machine Learning Lifecycle

What Is the Machine Learning Lifecycle?

Machine Learning is not just about training a model. It’s a comprehensive lifecycle that starts with raw data and ends with an intelligent system making reliable, real-time decisions.

In enterprise environments — especially those using SAP systems and Customer 360 platforms — managing this lifecycle efficiently, securely, and at scale is what separates ML pilots from production-ready AI systems.

The 7 Stages of the ML Lifecycle

Let’s break down each stage and see how Databricks streamlines the process with tools like Delta Lake, MLflow, Unity Catalog, and Model Serving.

1. Problem Framing and Business Alignment

Before writing a line of code, align on:

Business Objective: What decision do we want to automate?

Success Metric: What does “good” look like?

Domain Understanding: What data is relevant?

Predict customer churn using SAP CRM + Customer 360 engagement data to drive proactive retention.

2. Data Collection and Ingestion

Pull in data from multiple enterprise sources: