How to Integrate Machine Learning Model Development with Databricks Workflows?

[tarunnagar]

Hey everyone 

I’m currently exploring machine learning model development and I’m interested in understanding how to effectively integrate ML workflows within Databricks.

Specifically, I’d like to hear from the community about:

• How do you structure ML pipelines in Databricks, from data preprocessing to model training and deployment?

• Which Databricks tools or features (like MLflow, Delta Lake, or Databricks Jobs) do you find most useful for end-to-end ML workflows?

• How do you automate model retraining, versioning, and monitoring within Databricks?

• What are the common pitfalls or challenges when combining Databricks workflows with ML development, and how do you overcome them?

• Are there best practices for collaboration among data engineers, data scientists, and ML engineers using Databricks?

I’m looking for practical tips, workflow examples, or even small code snippets if you’re willing to share.

Basically, I want to understand how to seamlessly integrate the entire ML lifecycle — from data ingestion to model deployment — inside Databricks.

Thanks in advance for your insights!

[jameswood32]

You can integrate machine learning model development into Databricks Workflows pretty smoothly using the platform’s native tools. The main idea is to treat your ML lifecycle (data prep → training → evaluation → deployment) as a series of tasks within a Databricks Workflow (formerly Jobs).

Start by creating notebooks or Python scripts for each stage of your pipeline — e.g., one for data ingestion/cleaning, one for model training, and another for evaluation. Then, use Workflows to chain these together as sequential or parallel tasks. You can add task dependencies, retry policies, and schedule the whole pipeline to run automatically.

For tracking experiments, MLflow (integrated with Databricks) is essential. It handles model versioning, hyperparameter logging, and performance metrics. You can even register your best model in the MLflow Model Registry and deploy it directly via Databricks Model Serving or external endpoints.

If you’re using feature engineering pipelines, consider Feature Store to keep features consistent between training and inference.

Finally, automate retraining by triggering the workflow with Delta Live Tables or data freshness events. This way, your ML model development becomes part of a repeatable, production-grade pipeline in Databricks.