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Platform and Approach Comparison

Saurabh707344
New Contributor III

Do anyone have structure and crisp comparison between benefits of performing MLOps using below ways and what are the strong areas of each platform:

a) Standalone Databricks where all pipelines and orchestration done on Databricks and external third party tools.

b) Databricks on AWS where Databricks solely used for Data engineering activities and AWS Sagamaker/other AWS services used for all MLOps activities.

c) Databricks on Azure where Databricks solely for Data engineering activities and Azure ML/other Azure services used for all MLOps activities.

d) Databricks on AWS where Databricks used for Data engineering and ML pipeline activities, and AWS services used for quick deployment etc.

e) Databricks on Azure where Databricks used for Data engineering and ML pipeline activities, and Azure services used for quick deployment etc.

2 REPLIES 2

Anonymous
Not applicable

Hi @Saurabh Singh​ 

Great to meet you, and thanks for your question!

Let's see if your peers in the community have an answer to your question. Thanks.

Kaniz
Community Manager
Community Manager

Hi @Saurabh Singh​, Here is a structured and crisp comparison of the benefits and strong areas of each platform for performing MLOps:

a) Standalone Databricks:

  • Benefits: Unified platform: Databricks provides a unified environment for data engineering, data science, and machine learning.
    • Integrated pipelines: Pipelines can be built and orchestrated entirely within Databricks, simplifying the workflow.
    • Flexibility: External third-party tools can be easily integrated for specific MLOps requirements.
    • Strong areas: Streamlined data engineering: Databricks excels in data engineering tasks and provides powerful data manipulation capabilities.
    • Collaboration: Databricks offers collaborative features, allowing teams to work together efficiently.

b) Databricks on AWS:

  • Benefits:Integration with AWS ecosystem: Seamless integration with AWS services such as SageMaker allows leveraging specialized MLOps capabilities.
    • Scalability: AWS provides robust infrastructure and scalability options for handling large-scale ML workloads.
    • Strong areas:Data engineering: Databricks on AWS is well-suited for data engineering activities, benefiting from AWS's storage and processing services.
    • End-to-end data processing: AWS services complement Databricks, enabling comprehensive data processing and analytics pipelines.

c) Databricks on Azure:

  • Benefits:Native Azure integration: Databricks integrates seamlessly with Azure services, including Azure Machine Learning, providing a cohesive MLOps ecosystem.
    • Azure-specific capabilities: Leveraging Azure services for MLOps activities offers specialized features and services tailored to Azure's ML offerings.
    • Strong areas:Data engineering: Databricks on Azure shines in data engineering tasks, taking advantage of Azure's data storage and processing services.
    • Azure ML integration: The tight integration between Databricks and Azure ML simplifies ML pipeline development and deployment.

d) Databricks on AWS:

  • Benefits:Single platform for data engineering and ML pipeline: Databricks allows end-to-end development and deployment of both data engineering and ML pipelines.
    • Quick deployment with AWS: Utilizing AWS services for deployment enables rapid and scalable model deployment.
    • Strong areas:Combined data engineering and ML pipeline activities: Databricks on AWS provides a unified environment for both data engineering and ML pipeline tasks, simplifying the development process.
    • Seamless integration with AWS services: The integration with AWS services offers extensive options for data processing, storage, and deployment.

e) Databricks on Azure:

  • Benefits:Combined data engineering and ML pipeline activities: Databricks on Azure provides a unified environment for data engineering and ML pipeline tasks.
    • Quick deployment with Azure services: Azure services facilitate efficient and scalable model deployment.
    • Strong areas:Azure-specific capabilities: Leveraging Azure services for quick deployment and other MLOps activities provides access to Azure-specific features and functionalities.
    • Azure ML integration: Databricks on Azure seamlessly integrates with Azure ML, offering a comprehensive platform for developing and operationalizing ML models.

It's important to note that the suitability of each platform may vary based on specific requirements, existing infrastructure, team expertise, and other factors. Evaluating these factors in relation to your organization's needs will help determine the optimal choice for MLOps activities.

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