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Pachyderm has become a go-to solution for data teams seeking efficient data processing. Its compatibility with a majority of coding …
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What is Pachyderm?
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…
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Pachyderm Enterprise Edition
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On Premise
Pachyderm Community Edition
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On Premise
Pachyderm Enterprise Edition
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What is Pachyderm?
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 Edition
Pachyderm Enterprise Edition is a commercial offering designed for the largest projects in highly secure environments. Along with support, users get access to a range of premium features including Pachyderm Console, authentication and access controls (RBAC), no scaling limits, JupyterHub integration, and centralized multiple cluster management.
Pachyderm Community Edition
Pachyderm Community Edition is the open source version of Pachyderm. With Pachyderm Community Edition, the user gets the core Data Versioning and Pipeline features of Pachyderm that can be deployed locally or in the cloud of choice. For help, there’s a community of experts ready to offer their assistance.
Pachyderm Features
- Supported: Automated Data Versioning - Pachyderm’s Data Versioning gives teams an automated and performant way to keep track of all data changes
- Supported: Data-Driven Pipelines - Pachyderm’s Containerized Pipelines speed data processing while lowering compute costs
- Supported: Immutable Data Lineage - Pachyderm’s Data Lineage provides an immutable record for all activities and assets in the ML lifecycle
- Supported: Console - The Pachyderm Console provides an intuitive visualization of DAG (directed acyclic graph) and aids in reproducibility
- Supported: Notebooks - Pachyderm’s JupyterLab Mount Extension provides a point-and-click interface to Pachyderm versioned data
- Supported: Enterprise Administration - Pachyderm provides robust tools for deploying and administering Pachyderm at scale across different teams in the organization
Pachyderm Screenshots
Pachyderm Videos
Intro to Pachyderm | Data Versioning and Pipelines for MLOps
How To Scale Breast Cancer Detection with Pachyderm
Pachyderm Competitors
Pachyderm Technical Details
| Deployment Types | On-premise, Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Linux, Mac |
| Mobile Application | No |
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Community Insights
TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
- Business Problems Solved
- Pros
- Cons
Pachyderm has become a go-to solution for data teams seeking efficient data processing. Its compatibility with a majority of coding languages, including Python, allows teams to focus on their projects without worrying about language limitations. Users have expressed satisfaction with Pachyderm's data versioning and storage patterns, which they find unrivaled in the field.
One of the common business problems solved by Pachyderm is navigating browser compatibility issues. The software provides an automated, fully data-driven environment for various data transformations with lineage and versioning. This has resulted in increased efficiency in development, shorter time to debug, and ultimately a reduction in cost for organizations. Automating data handling with Pachyderm also reduces errors generated due to human intervention. Machine learning teams have found Pachyderm helpful in operationalizing data tasks during their ML lifecycle, automating preprocessing, and data versioning. Its ability to version data also allows users to recreate models with ease, making it ideal for conferencing needs.
Efficient Testing: Several reviewers highlighted that the ability to keep branches of data sets while testing new transformation pipelines is one of the strongest features of Pachyderm. This allows for more efficient testing and development of data pipelines without disrupting the main data set.
Python Support: Reviewers appreciated that Pachyderm offers native python support, which makes it a unique and powerful feature. This allows for more flexibility in programming and a wider range of possibilities for data processing.
Automated Data Pipelines: Many users found that Pachyderm provides a meaningful platform to automate data pipelines, making the process more efficient and allowing for more time to be spent on analysis and interpretation of results. This is especially important in industries where data is constantly changing and needs to be updated frequently.
Steep Learning Curve: Many reviewers have stated that the software has a steep learning curve and is not beginner-friendly. Users need to possess a certain skill set to properly use it.
Missing Features: Some users have reported missing features, especially with more exotic languages as the product is still in its early phase of development.
Slow Processing: Several customers have experienced network issues which result in slow processing times for Pachyderm.