Azure ML vs Azure Databricks (2024)

When comparing Databricks and Azure ML, it's important to keep in mind that they serve different purposes. While Databricks is ideal for analyzing large datasets using Spark, Azure ML is better suited for developing and managing end-to-end machine learning workflows.

There are advantages and disadvantages to using Databricks for ML workloads. The following information does not enumerate a full list and is subject to being extended or modified in the future.

Common features between Databricks and Azure ML

We added this section to define/correct some perceptions regarding Azure ML vs Databricks.

MLflow is the primary logging library for both platforms

Azure ML CLI v2 and SDK v2 use MLflow as a primary logging instrument. This makes it easy to reuse existing code from Databricks.

Tabular big data

Azure ML CLI and SDK v2 have native integration with Spark that allows the execution of workloads on big data using Spark code.

Both platforms have a UI for experimentation

When using Databricks, data scientists can continue to use interactive clusters and notebooks in the Databricks UI. In Azure ML, there are compute instances and the Azure ML Pipeline Designer. It's worth noting that all the features mentioned work well from initial experimentation only. Code should be migrated to build an enterprise ready end-to-end flow.

Both platforms support autoscaling

Azure ML provides built-in auto-scaling options for most of the compute options. Databricks clusters spin up and scale for processing massive amounts of data when needed and spin down when not in use. Also, Databricks has (Databricks) Pools that use a managed cache of virtual machines to quickly scale clusters up and down.

Both platforms have model registry

Azure ML provides a central model registry for the entire organization with full lineage for models. This lineage spans from data and Python dependencies, to the training run, all the way to deployments. Databricks has MLflow model registry for consistent, secure model deployment and management. Models are made available in a consistent, open format for deployment from the MLflow Model Registry to Kubernetes services, cloud or OSS inference services, or edge computing solutions.

Both platforms have automated machine learning enabled

Azure ML Provides more sophisticated ML model creation compared to Databricks. With Databricks, NLP, CV models are not supported currently. Also, direct deployment of models to AKS or containers is not available at the moment. More details here

Advantages of Azure ML over Databricks

Here are details about Azure ML advantages vs Databricks.

Complex MLOps processes

MLOps is simpler with Azure ML. One way it is simpler is that in Databricks we need to implement MLflow projects and deal with wheel files to start an experiment. The process is distinctly different from the usual experience in the Databricks UI on an Interactive cluster. It means that existing knowledge might not be enough to implement an enterprise training flow.

Inability to utilize several clusters for complex pipelines

An MLflow Project allows the definition of several steps in the flow, but does not easily support using different clusters on a per step basis. As a workaround, several MLflow projects may be executed in series, but makes the MLOps pipeline much more complex.

Azure ML has a better way to pre-process unstructured data

Thanks to the parallel job you can control how to split files into batches and configure parallelization using just a few lines of code. This capability is powerful to use for unstructured data.

Azure ML can deploy models

See Endpoints for inference in production to learn how you can utilize compute clusters to deploy models into production rather than just train them.

Productivity for all skill levels

Azure ML provides a better user interface to make it easier for a novice user to get started. It has productivity for all skill levels including code-first (notebooks/IDEs), low-code (AutoML), and no-code (Designer), whereas Databricks caters mostly to professionals.

Azure ML has plenty of out-of-the-box features

Azure ML provides full lifecycle tools, including Data Labeling, Data Drift detection, production monitoring (via Azure Monitor and Application Insights), managed endpoints, and more (for example, Integrated Responsible ML), as well as Hybrid ML capabilities (for compute utilization) with Azure Arc integration.

Advantages of Databricks over Azure ML

Reasons to use Databricks rather than Azure ML.

Various data sources are supported

Databricks allows you to get data from many different data sources. Databricks is deeply integrated with Delta Lake including built-in data versioning, time travel, and the option to use the Databricks Feature Store built on top of Delta Lake. Azure ML uses Azure Blob as the main data source and another service is often required to implement a data ingestion pipeline prior to starting training. Refer to this table to understand the data sources directly supported by AzureML and Databricks.

Integration with Apache Spark

Databricks is built on top of Apache Spark, a powerful distributed computing framework enabling fast, efficient processing of large datasets. Apache Spark provides a unified analytics engine for processing large-scale data processing, including machine learning algorithms. Databricks allows users to build and train machine learning models at scale using Spark, which is well suited for structured data.

Decision factors

Azure ML is cost-effective (lower TCO) with many out-of-the box ML features. It has more choice of deployment options in the Azure ecosystem and provides productivity for all skill levels.

Azure Databricks is chosen because of its data preparation capabilities and its batch stream processing. There is also a feature store provided. Databricks has ML as one component in a bigger data lake suite that includes streaming, data warehousing, and ETL.