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Description

Keepsake is a Python library that is open-source and specifically designed for managing version control in machine learning experiments and models. It allows users to automatically monitor various aspects such as code, hyperparameters, training datasets, model weights, performance metrics, and Python dependencies, ensuring comprehensive documentation and reproducibility of the entire machine learning process. By requiring only minimal code changes, Keepsake easily integrates into existing workflows, permitting users to maintain their usual training routines while it automatically archives code and model weights to storage solutions like Amazon S3 or Google Cloud Storage. This capability simplifies the process of retrieving code and weights from previous checkpoints, which is beneficial for re-training or deploying models. Furthermore, Keepsake is compatible with a range of machine learning frameworks, including TensorFlow, PyTorch, scikit-learn, and XGBoost, enabling efficient saving of files and dictionaries. In addition to these features, it provides tools for experiment comparison, allowing users to assess variations in parameters, metrics, and dependencies across different experiments, enhancing the overall analysis and optimization of machine learning projects. Overall, Keepsake streamlines the experimentation process, making it easier for practitioners to manage and evolve their machine learning workflows effectively.

Description

Pachyderm's Data Versioning offers teams an efficient and automated method for monitoring all changes to their data. With file-based versioning, users benefit from a comprehensive audit trail that encompasses all data and artifacts at each stage of the pipeline, including intermediate outputs. The data is stored as native objects rather than mere metadata pointers, ensuring that versioning is both automated and reliable. The system can automatically scale by utilizing parallel processing for data without the need for additional coding. Incremental processing optimizes resource usage by only addressing the differences in data and bypassing any duplicates. Additionally, Pachyderm’s Global IDs simplify the tracking of results back to their original inputs, capturing all relevant analysis, parameters, code, and intermediate outcomes. The intuitive Pachyderm Console further enhances user experience by providing clear visualizations of the directed acyclic graph (DAG) and supports reproducibility through Global IDs, making it a valuable tool for teams managing complex data workflows. This comprehensive approach ensures that teams can confidently navigate their data pipelines while maintaining accuracy and efficiency.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon S3 Yes 
Determined AI No 
Google Cloud Storage Yes 
JSON Yes 
Label Studio No 
PyTorch Yes 
Python Yes 
TensorFlow Yes 
scikit-learn Yes 

Integrations

Amazon S3 No 
Determined AI Yes 
Google Cloud Storage No 
JSON No 
Label Studio Yes 
PyTorch No 
Python No 
TensorFlow No 
scikit-learn No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Replicate

Country

United States

Website

keepsake.ai/

Vendor Details

Company Name

Pachyderm

Website

www.pachyderm.com

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Version Control

Branch Creation / Deletion No 
Centralized Version History No 
Code Review No 
Code Version Management No 
Collaboration Tools No 
Compare / Merge Branches No 
Digital Asset / Binary File Storage No 
Isolated Code Branches No 
Option to Revert to Previous No 
Pull Requests No 
Roles / Permissions No 

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

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