7 Ratings
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Score 7.5 out of 101
12 Ratings
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Score 8.6 out of 101

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Likelihood to Recommend

Amazon SageMaker

SageMaker is great for serving Jupyter notebooks, particularly if you already use other AWS products, such as S3. SageMaker's model retraining function is useful if you write a few Lambda functions to invoke jobs. Its model serving function is useful if your team has limited resources and is willing to submit to SageMaker's opinions.
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Databricks Unified Analytics Platform

Databricks has helped my teams write PySpark and Spark SQL jobs and test them out before formally integrating them in Spark jobs. Through Databricks we can create parquet and JSON output files. Datamodelers and scientists who are not very good with coding can get good insight into the data using the notebooks that can be developed by the engineers.
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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

  • 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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  • Extremely Flexible in Data Scenarios
  • Fantastic Performance
  • DB is always updating the system so we can have latest features.
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Cons

  • 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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  • Visualization - Great area of improvement
  • Integration with Git
  • COST
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Usability

No score
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Databricks Unified Analytics Platform9.0
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

We have not invested in another machine learning software at this time and so far this has proved very successful with our machine learning teams. As mentioned, I am training these individuals simply on the fundamentals of the software and using it/customizing it for their needs. It has been very easy to do this and has gotten great reviews across the organization so far.
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When we started using it, only the notebook experience was mature. However, DB was very helpful giving us direct support to get onto their platform. Really there was little in the way to compare to them at the time. AWS has services but not the same low-cost angle
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Return on Investment

  • 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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  • 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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Pricing Details

Amazon SageMaker

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

Databricks Unified Analytics Platform

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