100 Days of Machine Learning on Databricks Day 9: Challenges in Machine Learning Projects

Most machine learning courses and tutorials focus on model training and accuracy. But real-world ML projects face challenges far beyond fitting a good model.

In enterprise settings — where you deal with SAP systems, Customer 360 data, governance, and evolving business needs — the path to ML success is full of hidden roadblocks.

Today, we’ll uncover the most common challenges across the ML lifecycle and offer practical solutions using Databricks as the enterprise-grade platform.

The 10 Biggest Challenges in ML Projects — And How to Solve Them

1. Unclear Business Objective

Projects start with “We want to do AI/ML” without defining the real business impact.

An ML team is asked to “predict customer behavior” without defining which behavior matters — conversion? churn? upsell?

Solution:

• Collaborate with domain experts and product owners from Day 0
• Use SAP and Customer 360 data to map objectives to metrics
• Define KPIs like: “Reduce churn by 10% among high-value segments”

Databricks Tip:

Use Unity Catalog + Notebooks to align everyone on the same data and context in a collaborative workspace.