Introduction to Azure Databricks ML Workflows

Azure Databricks provides a powerful, collaborative, and unified platform for building and deploying end-to-end machine learning workflows on Azure. By integrating with Azure Machine Learning and leveraging Databricks' robust data processing and ML capabilities, you can streamline your ML lifecycle from data preparation to model deployment and monitoring.

This documentation guides you through the process of architecting, implementing, and managing your machine learning projects using Azure Databricks.

Key Concepts

  • Databricks Workflows: A native Databricks feature for orchestrating and scheduling complex data pipelines, including ML tasks.
  • MLflow: An open-source platform for managing the ML lifecycle, including experimentation, reproducibility, and deployment. Databricks has first-class integration with MLflow.
  • Delta Lake: A storage layer that brings reliability to data lakes, enabling ACID transactions and schema enforcement, crucial for reliable ML data.
  • Managed MLflow: Databricks-managed MLflow instance for simplified setup and integration.
  • Model Registry: MLflow's feature for centrally managing the lifecycle of MLflow models.
  • Feature Store: Centralized repository for curated and reusable features for ML models.

Setting Up Your Environment

To begin building ML workflows with Azure Databricks, ensure you have:

  • An Azure subscription.
  • An Azure Databricks workspace.
  • Appropriate permissions to create resources within your Azure subscription.

Consider setting up:

  • Databricks Clusters: Configure clusters optimized for ML workloads, including GPU acceleration if needed.
  • Access to Data: Mount your Azure Data Lake Storage Gen2 or connect to other data sources.
  • MLflow Tracking Server: Utilize the managed MLflow or configure your own.

Building ML Workflows

A typical ML workflow involves several stages:

  1. Data Ingestion & Preparation: Loading data, cleaning, transformation, and feature engineering using Spark and Delta Lake.
  2. Model Training: Experimenting with different algorithms and hyperparameters. Use MLflow to log parameters, metrics, and artifacts.
  3. Model Evaluation: Assessing model performance using various metrics and validating against test datasets.
  4. Model Registration: Storing trained models in the MLflow Model Registry for versioning and lifecycle management.
  5. Model Deployment: Deploying registered models as real-time endpoints or batch inference services.

Example: Feature Engineering with Spark

Use PySpark or Scala within a Databricks notebook to perform complex feature transformations:


from pyspark.sql import SparkSession
from pyspark.sql.functions import col

spark = SparkSession.builder.appName("FeatureEngineering").getOrCreate()

df = spark.read.format("delta").load("/mnt/datalake/raw_data")

# Example: Creating a new feature
processed_df = df.withColumn("new_feature", col("column_a") * col("column_b"))

processed_df.write.format("delta").mode("overwrite").save("/mnt/datalake/features")

Orchestration with Databricks Workflows

Databricks Workflows allows you to define, schedule, and monitor your ML pipelines as a series of tasks. This can include notebooks, Python scripts, JARs, and Delta Live Tables pipelines.

Key Workflow Components:

  • Tasks: Individual units of work (e.g., a notebook run, a script execution).
  • Dependencies: Defining the order in which tasks should run.
  • Triggers: Scheduling workflows based on time intervals or external events.
  • Parameters: Passing dynamic values to tasks.

You can create workflows through the Databricks UI, the Databricks CLI, or programmatically using the Databricks Jobs API.

Monitoring and Observability

Effective monitoring is critical for maintaining ML models in production:

  • MLflow UI: Track experiments, compare runs, and analyze model performance.
  • Databricks Jobs UI: Monitor workflow execution, view logs, and identify failures.
  • Model Performance Monitoring: Set up alerts for data drift, concept drift, and performance degradation.
  • Logging: Implement robust logging within your tasks to capture detailed information.

Best Practices

  • Version Control: Use Git for notebooks and code.
  • Reproducibility: Leverage MLflow to log all aspects of an experiment.
  • Data Quality: Use Delta Lake and enforce schema validation.
  • Modular Design: Break down complex workflows into smaller, reusable tasks.
  • Security: Manage credentials and access securely using Databricks secrets.
  • Cost Optimization: Tune cluster configurations and leverage auto-scaling.

Getting Started Tutorials

Explore these practical guides to build your first ML workflow: