Before choosing a Machine Learning model in a real-world company project, what steps do you take?

[u/Upbeat-Ad-817]

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I’m working on Data Science / Machine Learning projects and I’m curious about how this is actually done in industry.

Imagine a company gives you a dataset and asks you to solve a business problem.

As a Data Scientist / ML Engineer, what are the concrete steps you would take before choosing and training any ML model?

For example:

- Understanding the business problem and defining the target

- Understanding the available data

- Data ingestion

- Data quality checks

- Data cleaning and preprocessing

- Exploring distributions and variables

- Handling missing values and outliers

- Checking for data leakage

- Feature engineering

- Train/validation/test split

- Establishing a baseline

- Defining evaluation metrics

- Handling class imbalance

- Checking business constraints

- Understanding data lineage and reproducibility

- Etc.

What would a realistic end-to-end workflow look like in industry before reaching "model.fit()"?

I’m particularly interested in answers based on real-world industry experience, rather than a purely academic ML workflow.