AWS to Azure Migration (ML Models)

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Choose Azure ML. It is simpler, cheaper, and requires fewer code changes than Azure + Databricks ML while remaining fully scalable. If your priorities are cost control, low complexity, minimal code changes, and scalability, the better option is Azure ML.

Azure ML gives you a managed ML environment that is closer to how SageMaker works. Your existing Python, Pandas, and ML libraries will run with almost no changes. You get built-in compute, managed endpoints, environment/version control, and MLOps without needing to stitch multiple services together. It is simpler and usually cheaper for small to mid-scale workloads.

Databricks ML is powerful but adds extra cost and operational complexity. It shines when you already have large-scale Spark workloads or need collaborative notebooks across big engineering teams. For straightforward Python ML migration, it is overkill.

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Hi wfhDataEngineer

Welcome to Microsoft Q&A and Thanks for sharing the details.

For a straightforward migration with minimal changes, Azure Machine Learning might be the better choice .

1. Azure Machine Learning (Azure ML)

• Benefits:
  • It's designed for easy integration and offers a suite of tools tailored for machine learning workflows.
  • You can leverage Azure's automated machine learning features, which can handle many tasks with minimal manual coding adjustments.
  • Azure ML provides scalability options and better manageability with models at various stages through its Model Registry.
• Considerations: While Azure ML has many features and a user-friendly interface, depending on your current workloads and specific use cases, the initial learning curve might require some time investment.

2. Azure + Databricks ML

• Benefits:
  • Databricks offers a collaborative platform built around Apache Spark, suitable for big data processing and machine learning, which could be beneficial if your models are data-intensive.
  • If you're familiar with Spark and want to harness its distributed processing capabilities, this might be the best option.
  • Depending on your current setup and the amount of data processed, Databricks could provide a cost-effective solution due to its pay-per-use pricing model.
• Considerations: If your team is not familiar with Databricks or Spark, it might involve a steeper learning curve. Additionally, migrating specific ML objects and ensuring compatibility with MLFlow can be complex.

Conclusion

For a straightforward migration with minimal changes, Azure Machine Learning might be the better choice as it aligns more closely with your needs for simplicity and reduced code changes. If you're looking for scalability and your workloads involve significant data, Databricks may also be a strong candidate.

Recommendations:

Review your current workload requirements against both platforms. Consider a small test migration with both options to measure performance and integration ease before fully committing.

I hope this helps you make a decision! Feel free to reach out if you have more questions or need further clarifications.

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