13 Ratings
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Score 7.8 out of 101
16 Ratings
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Score 8.8 out of 101

Likelihood to Recommend

Amazon SageMaker

Well suited scenarios:
  • For quick POC of ML and DL.
  • To train a model on a large dataset using multiple servers.
  • To host a model to be used by multiple applications.
Less appropriate scenarios:
  • For data analysis tasks.
  • For a data scientist who has less of a programming background.
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Databricks Unified Analytics Platform

  • DB generally fits 95% of what you need to do
  • Primarily the ability to transform data and or do ad-hoc DS work
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Feature Rating Comparison

Platform Connectivity

Amazon SageMaker
Databricks Unified Analytics Platform
8.3
Connect to Multiple Data Sources
Amazon SageMaker
Databricks Unified Analytics Platform
9.0
Extend Existing Data Sources
Amazon SageMaker
Databricks Unified Analytics Platform
9.0
Automatic Data Format Detection
Amazon SageMaker
Databricks Unified Analytics Platform
7.0

Data Exploration

Amazon SageMaker
Databricks Unified Analytics Platform
6.0
Visualization
Amazon SageMaker
Databricks Unified Analytics Platform
6.0
Interactive Data Analysis
Amazon SageMaker
Databricks Unified Analytics Platform
6.0

Data Preparation

Amazon SageMaker
Databricks Unified Analytics Platform
8.0
Interactive Data Cleaning and Enrichment
Amazon SageMaker
Databricks Unified Analytics Platform
8.0
Data Transformations
Amazon SageMaker
Databricks Unified Analytics Platform
9.0
Data Encryption
Amazon SageMaker
Databricks Unified Analytics Platform
7.0
Built-in Processors
Amazon SageMaker
Databricks Unified Analytics Platform
8.0

Platform Data Modeling

Amazon SageMaker
Databricks Unified Analytics Platform
8.3
Multiple Model Development Languages and Tools
Amazon SageMaker
Databricks Unified Analytics Platform
9.0
Automated Machine Learning
Amazon SageMaker
Databricks Unified Analytics Platform
8.0
Single platform for multiple model development
Amazon SageMaker
Databricks Unified Analytics Platform
9.0
Self-Service Model Delivery
Amazon SageMaker
Databricks Unified Analytics Platform
7.0

Model Deployment

Amazon SageMaker
Databricks Unified Analytics Platform
7.5
Flexible Model Publishing Options
Amazon SageMaker
Databricks Unified Analytics Platform
7.0
Security, Governance, and Cost Controls
Amazon SageMaker
Databricks Unified Analytics Platform
8.0

Pros

Amazon SageMaker

  • SageMaker is useful as a managed Jupyter notebook server. Using the notebook instances' IAM roles to grant access to private S3 buckets and other AWS resources is great. Using SageMaker's lifecycle scripts and AWS Secrets Manager to inject connection strings and other secrets is great.
  • SageMaker is good at serving models. The interface it provides is often clunky, but a managed, auto-scaling model server is powerful.
  • SageMaker is opinionated about versioning machine learning models and useful if you agree with its opinions.
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Databricks Unified Analytics Platform

  • Extremely Flexible in Data Scenarios
  • Fantastic Performance
  • DB is always updating the system so we can have latest features.
No photo available

Cons

Amazon SageMaker

  • SageMaker does not allow you to schedule training jobs.
  • SageMaker does not provide a mechanism for easily tracking metrics logged during training.
  • We often fit feature extraction and model pipelines. We can inject the model artifacts into AWS-provided containers, but we cannot inject the feature extractors. We could provide our own container to SageMaker instead, but this is tantamount to serving the model ourselves.
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Databricks Unified Analytics Platform

  • The navigation through which one would create a workspace is a bit confusing at first. It takes a couple minutes to figure out how to create a folder and upload files since it is not the same as traditional file systems such as box.com
  • Also, when you create a table, if you forgot to copy the link where the table is stored, it is hard to relocate it. Most of the time I would have to delete the table and re-created.
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Usability

Amazon SageMaker

No score
No answers yet
No answers on this topic

Databricks Unified Analytics Platform

Databricks Unified Analytics Platform 9.0
Based on 1 answer
This has been very useful in my organization for shared notebooks, integrated data pipeline automation and data sources integrations. Integration with AWS is seamless. Non tech users can easily learn how to use Databricks. You can have your company LDAP connect to it for login based access controls to some extent
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Alternatives Considered

Amazon SageMaker

Amazon SageMaker comes with other supportive services like S3, SQS, and a vast variety of servers on EC2. It's very comfortable to manage the process and also support the end application by one click hosting option. Also, it charges on the base of what you use and how long you use it, so it becomes less costly compared to others.
No photo available

Databricks Unified Analytics Platform

I also use Microsoft Azure Machine Learning in parallel with Databricks. They use different file formats which teach me to be flexible and able to write different programs. They are equally useful to me and I would like to master both platforms for any future usage. I do prefer Databricks because it could be free if I decided to go with the Databricks Community Edition only.
Ann Le profile photo

Return on Investment

Amazon SageMaker

  • We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
  • We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
  • For our use-cases, serving models is less expensive with SageMaker than bespoke servers.
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Databricks Unified Analytics Platform

  • Quick adoption of cloud services by end users
  • Cost is high
No photo available

Pricing Details

Amazon SageMaker

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

Databricks Unified Analytics Platform

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

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