Azure Databricks vs. Azure Machine Learning

Centralised notebooks are out directly into production. This can lead to poorly engineered code. It is very good for fast queries and our data team are always able to provide what we ask for. It is a big cost to our business so it is important it runs efficiently and returns on our investment.

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I would highly recommend Azure machine learning design for those with less access to high-end computing infrastructure, as using Azure saves a lot of time, money, and effort by providing a hustle-free platform that is easy to use and train your employees on. On the other hand, if you are looking for complete control of the machine learning model you create and would like to add detailed functionalities and try different algorithms, then Azure is less suitable here as it’s very high level.

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• Data Processing and Transformations based on Spark
• Delta Lakehouse when clubbed with an external cloud storage
• Governance using Unity Catalog to unify IAM
• Delta Live Tables is a product, which although relatively newer, has a great potential with the visuals of a pipeline.

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• Easy to create the experiment.
• Easy to adopt the best algorithm.
• Efficient way to deploy the model as a web service.
• Centralized platform for the life cycle of machine learning goal.

• Intuitive interface
• Ease of use
• Providing FAQ or QRGs

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• Few models: Even though it has a lot of Machine Learning models, it is quite limited when compared to R. Most Data Scientists still use and prefer R, so the newest models tend to release as R libraries. With Azure ML, we need to wait for Microsoft to evaluate and decide if including a new model is a good idea or not
• Tableau interface: last time I checked there was no easy way to connect with Tableau.
• Cloud based: You always need a good internet connection to use it.

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The developers are able to switch between Python and SQL in the Notebook which allows the collaboration of SQL analyst and Data scientist. The integration of Mosaic AI allows users to write complex codes in natural languages. Unity catalog has centralized the security and governance features and simplified the process of maintaining it

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Good UX/UI and overall good usability, but it takes a while to get used to the product & platform. The whole design seems fragmented with little in terms of integration with project management tools such as JIRA, or wireframing. Overall it feels like an unfinished product that's meant for teaching more than for production.

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I'm satisfied with the Azure Machine Learning Studio- it fulfilled my goal in a single channel. Even haven't worr[ied] about the maintenance or any fault tolerance. This provide[s] the user interactive UI to grab the features easily. [Their] support teams also very help[ful], they stand with us at any time.

Not sure

I have found Azure Databricks to be much better than Snowflake for handling bigger, diverse data types. Snowflake is much simpler and better for smaller warehousing. The real time processing is much better in Azure Databricks and we have much more language options. Snowflake is more expensive but simpler to use. Both are great for different needs.

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It is easier to learn, it has a very cost effective license for use, it has native build and created for Azure cloud services, and that makes it perfect when compared against the alternatives. As a Microsoft tool, it has been built to contain many visual features and improved usability even for non-specialist users.

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• The support team is amazing, they help you at every stage of the projects, from sales to delivery.
• On a framework level, it has had an amazing impact and has reduced the clients overall data platform costs by a staggering 65%
• There has been a 40% Manual work requirement on average for the clients when they move to Azure Databricks Data Platform

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• Reduce energy consumption caused by GPUs.
• Saves on recycling and transporting costs and maintenance caused by buying high-end equipment.
• Improve productivity as building products using Azure is easier than building everything up from scratch (e.g., machine learning and AI applications).

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