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Update README.rst (#6461)

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* Update README.rst

Fixes typos and makes the related technologies stand out from their description.

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Co-authored-by: Jorge Orpinel <[email protected]>
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Himanshu
2021-08-25 22:14:27 +03:00
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co-authored by Jorge Orpinel
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@@ -20,8 +20,9 @@ learning projects. Key features:
#. Simple **command line** Git-like experience. Does not require installing and maintaining
any databases. Does not depend on any proprietary online services.
#. Management and versioning of **datasets** and **machine learning models**. Data is saved in
S3, Google cloud, Azure, Alibaba cloud, SSH server, HDFS, or even local HDD RAID.
#. Management and versioning of **datasets** and **machine learning
models**. Data can be saved in S3, Google cloud, Azure, Alibaba
cloud, SSH server, HDFS, or even local HDD RAID.
#. Makes projects **reproducible** and **shareable**; helping to answer questions about how
a model was built.
@@ -182,30 +183,37 @@ Fedora / CentOS (rpm)
Comparison to related technologies
==================================
#. *Data Engineering* tools such as `AirFlow <https://airflow.apache.org/>`_, `Luigi <https://github.com/spotify/luigi>`_ and
others - in DVC data, model and ML pipelines represent a single ML project and focuses on data scientist' experience while
data engineering pipeline orchestrates multiple data projects and focuses on efficient execution. DVC project can be used
from data pipeline as a single execution step. `AirFlow DVC <https://github.com/covid-genomics/airflow-dvc>`_ is an example
of such integration.
#. Data Engineering tools such as `AirFlow <https://airflow.apache.org/>`,
`Luigi <https://github.com/spotify/luigi>`, and others - in DVC data,
model and ML pipelines represent a single ML project focused on data
scientists' experience. Data engineering tools orchestrate multiple data
projects and focus on efficient execution. A DVC project can be used from
existing data pipelines as a single execution step.
#. `Git-annex <https://git-annex.branchable.com/>`_ - DVC uses the idea of storing the content of large files (which should
not be in a Git repository) in a local key-value store, and uses file hardlinks/symlinks instead of
copying/duplicating files.
#. `Git-LFS <https://git-lfs.github.com/>`_ - DVC is compatible with any remote storage (S3, Google Cloud, Azure, SSH,
etc). DVC also uses reflinks or hardlinks to avoid copy operations on checkouts; thus handling large data files
much more efficiently.
#. `Git-LFS <https://git-lfs.github.com/>`_ - DVC is compatible with many
remote storage services (S3, Google Cloud, Azure, SSH, etc). DVC also
uses reflinks or hardlinks to avoid copy operations on checkouts; thus
handling large data files much more efficiently.
#. *Makefile* (and analogues including ad-hoc scripts) - DVC tracks dependencies (in a directed acyclic graph).
#. Makefile (and analogues including ad-hoc scripts) - DVC tracks
dependencies (in a directed acyclic graph).
#. `Workflow Management Systems <https://en.wikipedia.org/wiki/Workflow_management_system>`_ - DVC is a workflow
management system designed specifically to manage machine learning experiments. DVC is built on top of Git.
#. `DAGsHub <https://dagshub.com/>`_ - online service of Git+DVC repositories with pipeline and metrics visualization and
DVC-specific cloud storage.
#. `DAGsHub <https://dagshub.com/>`_ - online service to host DVC
projects. It provides a useful UI around DVC repositories and integrates
other tools.
#. `DVC Studio <https://studio.iterative.ai/>`_ - online service of DVC repositories visualisation from DVC team. Also,
integrated with `CML (CI/CD for ML) <https://cml.dev/>`_ for training models in clouds and Kubernetes.
#. `DVC Studio <https://studio.iterative.ai/>`_ - official online
platform for DVC projects. It can be used to manage data and models, run
and track experiments, and visualize and share results. Also, it
integrates with `CML (CI/CD for ML) <https://cml.dev/>` for training
models in the cloud or Kubernetes.
Contributing