Azure Machine Learning vs. Databricks Lakehouse Platform

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Azure Machine Learning
Score 7.9 out of 10
N/A
Microsoft's Azure Machine Learning is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.
$0
per month
Databricks Lakehouse Platform
Score 8.1 out of 10
N/A
Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization. Its Data Science Workspace is a collaborative environment for practitioners to run…
$0.07
Per DBU
Pricing
Azure Machine LearningDatabricks Lakehouse Platform
Editions & Modules
Studio Pricing - Free
$0.00
per month
Production Web API - Dev/Test
$0.00
per month
Studio Pricing - Standard
$9.99
per ML studio workspace/per month
Production Web API - Standard S1
$100.13
per month
Production Web API - Standard S2
$1000.06
per month
Production Web API - Standard S3
$9999.98
per month
Standard
$0.07
Per DBU
Premium
$0.10
Per DBU
Enterprise
$0.13
Per DBU
Offerings
Pricing Offerings
Azure Machine LearningDatabricks Lakehouse Platform
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Azure Machine LearningDatabricks Lakehouse Platform
Top Pros
Top Cons
Best Alternatives
Azure Machine LearningDatabricks Lakehouse Platform
Small Businesses
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10

No answers on this topic

Medium-sized Companies
Posit
Posit
Score 9.1 out of 10
Snowflake
Snowflake
Score 9.0 out of 10
Enterprises
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
Snowflake
Snowflake
Score 9.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Azure Machine LearningDatabricks Lakehouse Platform
Likelihood to Recommend
8.0
(4 ratings)
8.4
(17 ratings)
Likelihood to Renew
7.0
(1 ratings)
-
(0 ratings)
Usability
7.0
(2 ratings)
9.4
(3 ratings)
Support Rating
7.9
(2 ratings)
8.6
(2 ratings)
Implementation Rating
8.0
(1 ratings)
-
(0 ratings)
Contract Terms and Pricing Model
-
(0 ratings)
8.0
(1 ratings)
Professional Services
-
(0 ratings)
10.0
(1 ratings)
User Testimonials
Azure Machine LearningDatabricks Lakehouse Platform
Likelihood to Recommend
Microsoft
For [a] data scientist require[d] to build a machine learning model, so he/she didn't worry about infrastructure to maintain it.
All kind of feature[s] such as train, build, deploy and monitor the machine learning model available in a single suite.
If someone has [their] own environment for ML studio, so there [it would] not [be] useful for them.
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Databricks
If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.
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Pros
Microsoft
  • User friendliness: This is by far the most user friendly tool I've seen in analytics. You don't need to know how to code at all! Just create a few blocks, connect a few lines and you are capable of running a boosted decision tree with a very high R squared!
  • Speed: Azure ML is a cloud based tool, so processing is not made with your computer, making the reliability and speed top notch!
  • Cost: If you don't know how to code, this is by far the cheapest machine learning tool out there. I believe it costs less than $15/month. If you know how to code, then R is free.
  • Connectivity: It is super easy to embed R or Python codes on Azure ML. So if you want to do more advanced stuff, or use a model that is not yet available on Azure ML, you can simply paste the code on R or Python there!
  • Microsoft environment: Many many companies rely on the Microsoft suite. And Azure ML connects perfectly with Excel, CSV and Access files.
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Databricks
  • Process raw data in One Lake (S3) env to relational tables and views
  • Share notebooks with our business analysts so that they can use the queries and generate value out of the data
  • Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
  • Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers
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Cons
Microsoft
  • It would be great to have text tips that could ease new users to the platform, especially if an error shows up
  • Scenario-based documentation
  • Pre-processing of modules that had been previously run. Sometimes they need to be re-run for no apparent reason
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Databricks
  • Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
  • Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
  • Visualization in MLFLOW experiment can be enhanced
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Usability
Microsoft
Easy and fastest way to develop, test, deploy and monitor the machine learning model.
- Easy to load the data set
-Drag and drop the process of the Machine learning life cycle.
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Databricks
Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX
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Support Rating
Microsoft
Support is nonexistent. It's very frustrating to try and find someone to actually talk to. The robot chatbots are just not well trained.
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Databricks
One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.
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Implementation Rating
Microsoft
Not sure
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Databricks
No answers on this topic
Alternatives Considered
Microsoft
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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Databricks
Compared to Synapse & Snowflake, Databricks provides a much better development experience, and deeper configuration capabilities. It works out-of-the-box but still allows you intricate customisation of the environment. I find Databricks very flexible and resilient at the same time while Synapse and Snowflake feel more limited in terms of configuration and connectivity to external tools.
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Return on Investment
Microsoft
  • Productivity: Instead of coding and recoding, Azure ML helped my organization to get to meaningful results faster;
  • Cost: Azure ML can save hundreds (or even thousands) of dollars for an organization, since the license costs around $15/month per seat.
  • Focus on insights and not on statistics: Since running a model is so easy, analysts can focus more on recommendations and insights, rather than statistical details
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Databricks
  • The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
  • DB has the ability to terminate/time out instances which helps manage cost.
  • The ability to quickly access typical hard to build data scenarios easily is a strength.
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ScreenShots