Amazon SageMaker vs. Pachyderm

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Amazon SageMaker
Score 8.5 out of 10
N/A
Amazon SageMaker enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.N/A
Pachyderm
Score 0.0 out of 10
N/A
Pachyderm is for data science teams who want to operationalize the data tasks in their ML lifecycle to iterate on data more quickly and reliably. Pachyderm supports data versioning and pipelines for MLOps, and this data foundation allows data science teams to automate and scale their machine learning lifecycle while guaranteeing reproducibility. Pachyderm provides data-driven automation, petabyte scalability and end-to-end reproducibility. Pachyderm Enterprise…
$0
Pricing
Amazon SageMakerPachyderm
Editions & Modules
No answers on this topic
Pachyderm Enterprise Edition
$0
Pachyderm Community Edition
$0
Pachyderm Enterprise Edition
$0
Pachyderm Community Edition
$0
Offerings
Pricing Offerings
Amazon SageMakerPachyderm
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Amazon SageMakerPachyderm
Best Alternatives
Amazon SageMakerPachyderm
Small Businesses
InterSystems IRIS
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Score 7.8 out of 10
Google Cloud AI
Google Cloud AI
Score 8.2 out of 10
Medium-sized Companies
InterSystems IRIS
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Score 7.8 out of 10
Google Cloud AI
Google Cloud AI
Score 8.2 out of 10
Enterprises
Dataiku
Dataiku
Score 8.2 out of 10
Dataiku
Dataiku
Score 8.2 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Amazon SageMakerPachyderm
Likelihood to Recommend
9.0
(5 ratings)
-
(0 ratings)
User Testimonials
Amazon SageMakerPachyderm
Likelihood to Recommend
Amazon AWS
It allows for one-click processes and for things to be auto checked before they are moved through the process but through the system. It also makes training easy. I am able to train users on the basic fundamentals of the tool and how it is used very easily as it is fully managed on its own which is incredible.
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Pachyderm
No answers on this topic
Pros
Amazon AWS
  • Machine Learning at scale by deploying huge amount of training data
  • Accelerated data processing for faster outputs and learnings
  • Kubernetes integration for containerized deployments
  • Creating API endpoints for use by technical users
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Pachyderm
No answers on this topic
Cons
Amazon AWS
  • It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
  • Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.
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Pachyderm
No answers on this topic
Alternatives Considered
Amazon AWS
Amazon SageMaker took the heavy lifting out of building and creating models. It allowed for our organization to use our current system for integration and essentially added on a feature to help all levels of Data scientists and IT professionals in our department and company as a whole. The training was simple as well.
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Pachyderm
No answers on this topic
Return on Investment
Amazon AWS
  • 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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Pachyderm
No answers on this topic
ScreenShots

Pachyderm Screenshots

Screenshot of Automated Data Versioning - Pachyderm’s Data Versioning gives teams an automated and performant way to keep track of all data changesScreenshot of Data-Driven Pipelines - Pachyderm’s Containerized Pipelines speed data processing while lowering compute costsScreenshot of Immutable Data Lineage - Pachyderm’s Data Lineage provides an immutable record for all activities and assets in the ML lifecycleScreenshot of Console - The Pachyderm Console provides an intuitive visualization of your DAG (directed acyclic graph) and aids in reproducibilityScreenshot of Notebooks - Pachyderm’s JupyterLab Mount Extension provides a point-and-click interface to Pachyderm versioned dataScreenshot of Enterprise Administration - Pachyderm provides robust tools for deploying and administering Pachyderm at scale across different teams in your organization