Data Version Control
open-source version control system for ML projects
DVC is a new type of experiment management software that has been built on top of the existing engineering toolset particularly on a source code version control system (currently Git). DVC reduces the gap between existing tools and data science needs, allowing users to take advantage of experiment management software while reusing existing skills and intuition.
Key features:
[0] simple command line Git-like experience. It does not require installing and maintaining any databases. It does not depend on any proprietary online services
[1] management and versioning of datasets and ML models. Data is saved in S3, Google Cloud, Azure, Alibaba cloud, SSH server, HDFS, or even local HDD RAID
[2] makes projects reproducible and shareable; helping to answer questions about how a model was built
[3] helps manage experiments with Git tags/branches and metrics tracking
The main commands :feelsgoodmeme:
webpage: https://dvc.org
docs: https://dvc.org/doc
github: https://github.com/iterative/dvc
:ods: channel: #tool_dvc
#dvc #version #control #ml #projects #system #git
open-source version control system for ML projects
DVC is a new type of experiment management software that has been built on top of the existing engineering toolset particularly on a source code version control system (currently Git). DVC reduces the gap between existing tools and data science needs, allowing users to take advantage of experiment management software while reusing existing skills and intuition.
Key features:
[0] simple command line Git-like experience. It does not require installing and maintaining any databases. It does not depend on any proprietary online services
[1] management and versioning of datasets and ML models. Data is saved in S3, Google Cloud, Azure, Alibaba cloud, SSH server, HDFS, or even local HDD RAID
[2] makes projects reproducible and shareable; helping to answer questions about how a model was built
[3] helps manage experiments with Git tags/branches and metrics tracking
The main commands :feelsgoodmeme:
$ dvc add <name_file>$ dvc run <name_file>$ dvc [push/pull]webpage: https://dvc.org
docs: https://dvc.org/doc
github: https://github.com/iterative/dvc
:ods: channel: #tool_dvc
#dvc #version #control #ml #projects #system #git
Hi, our friends @mike0sv and @agusch1n just open-sourced MLEM - a tool that helps you deploy your ML models as part of the DVC ecosystem
It’s a Python library + Command line tool.
TLDR:
📦 MLEM can package an ML model into a Docker image or a Python package, and deploy it to Heroku (we made them promise to add SageMaker, K8s and Seldon-core soon :parrot:).
⚙️ MLEM saves all model metadata to a human-readable text file: Python environment, model methods, model input & output data schema and more.
💅 MLEM helps you turn your Git repository into a Model Registry with features like ML model lifecycle management.
Read more in release blogpost: https://dvc.org/blog/MLEM-release
Also, check out the project: https://github.com/iterative/mlem
And the website: https://mlem.ai
Guys are happy to hear your feedback, discuss how this could be helpful for you, how MLEM compares to MLflow, etc.
Ask in the comments!
#mlops #opensource #deployment #dvc
It’s a Python library + Command line tool.
TLDR:
📦 MLEM can package an ML model into a Docker image or a Python package, and deploy it to Heroku (we made them promise to add SageMaker, K8s and Seldon-core soon :parrot:).
⚙️ MLEM saves all model metadata to a human-readable text file: Python environment, model methods, model input & output data schema and more.
💅 MLEM helps you turn your Git repository into a Model Registry with features like ML model lifecycle management.
Read more in release blogpost: https://dvc.org/blog/MLEM-release
Also, check out the project: https://github.com/iterative/mlem
And the website: https://mlem.ai
Guys are happy to hear your feedback, discuss how this could be helpful for you, how MLEM compares to MLflow, etc.
Ask in the comments!
#mlops #opensource #deployment #dvc