Azure Databricks vs Azure Machine Learning: What are the differences?

Introduction

In this article, we will explore the key differences between Azure Databricks and Azure Machine Learning, two popular services provided by Microsoft for advanced data analytics and machine learning tasks.

1. Scalability and flexibility: Azure Databricks provides a cloud-based Apache Spark platform, designed for big data processing and analytics. It offers highly scalable and flexible infrastructure, allowing users to handle large volumes of data and execute distributed computing tasks efficiently. On the other hand, Azure Machine Learning is a managed service that focuses on the machine learning lifecycle. While it can handle large datasets, it may not provide the same level of scalability and fault-tolerance as Azure Databricks.

2. Collaboration and productivity: Azure Databricks offers collaborative features that enable teams to work together efficiently. It provides notebooks for code development, sharing, and collaboration. Additionally, it supports version control integration, allowing multiple users to work on the same codebase simultaneously. Azure Machine Learning also supports collaboration but may not provide the same level of productivity features as Azure Databricks. It primarily focuses on the machine learning workflow and providing a streamlined experience for building, training, and deploying machine learning models.

3. Machine learning workflow: Azure Machine Learning is specifically designed for the end-to-end machine learning workflow. It provides capabilities for data preparation, feature engineering, model training, and model deployment. It offers a graphical interface for building and managing machine learning pipelines. Azure Databricks, on the other hand, is a more general-purpose big data processing platform and may require additional setup and configuration to support the complete machine learning workflow.

4. Model management and deployment: Azure Machine Learning provides advanced features for managing and deploying machine learning models. It offers integration with Azure Kubernetes Service (AKS) for scalable and reliable model serving. It also supports model versioning and allows for seamless deployment of updated models. Azure Databricks, while it can be used to train machine learning models, may not provide the same level of model management and deployment capabilities as Azure Machine Learning.

5. Supported languages and frameworks: Azure Databricks supports multiple programming languages, including Python, R, Scala, and SQL. It also provides built-in support for popular machine learning libraries and frameworks, such as TensorFlow and PyTorch. Azure Machine Learning also supports multiple programming languages but is primarily focused on Python. It provides a wide range of libraries and frameworks for machine learning and data science tasks.

6. Pricing and cost: Azure Databricks and Azure Machine Learning have different pricing models. Azure Databricks pricing is based on the number and type of virtual machines used, as well as the storage and egress costs. Azure Machine Learning pricing is based on the number of training and inference units consumed. The cost of using these services may vary based on the specific usage patterns and requirements of the user.

In summary, Azure Databricks is a scalable and flexible big data processing platform, while Azure Machine Learning focuses on the end-to-end machine learning workflow. Azure Databricks offers collaborative features and supports multiple programming languages, making it suitable for data engineering and analytics tasks. Azure Machine Learning provides advanced model management and deployment capabilities, making it ideal for building and deploying machine learning models. The choice between Azure Databricks and Azure Machine Learning depends on the specific requirements and goals of the project.