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# DVC Basic Concepts and Features | ||
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Learn basic concepts and features of DVC with interactive lessons: | ||
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1. [Data Management](https://katacoda.com/dvc/courses/basics/data) <br/> The | ||
core function of DVC is data tracking and management. Let's see how to do it. | ||
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2. [Getting the Best Performance](https://katacoda.com/dvc/courses/basics/performance) | ||
<br/> It is important to optimize the DVC setup for having the best | ||
performance with handling big data files. | ||
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3. [Tracking Data Versions](https://katacoda.com/dvc/courses/basics/versioning) | ||
<br/> DVC takes advantage of GIT's versioning features to keep track of the | ||
data versions. | ||
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4. [Sharing Data](https://katacoda.com/dvc/courses/basics/sharing) <br/> DVC | ||
facilitates sharing of data between different people that work on the same | ||
project. | ||
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5. [Stages And Pipelines](https://katacoda.com/dvc/courses/basics/pipelines) | ||
<br/> DVC has a built-in way to connect ML steps into a DAG and run the full | ||
pipeline end-to-end. |
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# DVC Basic Concepts and Features | ||
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Learn basic concepts and features of DVC with interactive lessons: | ||
|
||
1. [Data Management](https://katacoda.com/dvc/courses/basics/data) <br/> The | ||
core function of DVC is data tracking and management. Let's see how to do it. | ||
|
||
2. [Getting the Best Performance](https://katacoda.com/dvc/courses/basics/performance) | ||
<br/> It is important to optimize the DVC setup for having the best | ||
performance with handling big data files. | ||
|
||
3. [Tracking Data Versions](https://katacoda.com/dvc/courses/basics/versioning) | ||
<br/> DVC takes advantage of GIT's versioning features to keep track of the | ||
data versions. | ||
|
||
4. [Sharing Data](https://katacoda.com/dvc/courses/basics/sharing) <br/> DVC | ||
facilitates sharing of data between different people that work on the same | ||
project. | ||
|
||
5. [Stages And Pipelines](https://katacoda.com/dvc/courses/basics/pipelines) | ||
<br/> DVC has a built-in way to connect ML steps into a DAG and run the full | ||
pipeline end-to-end. | ||
|
||
# Interactive Tutorials | ||
|
||
1. [Data Versioning](https://katacoda.com/dvc/courses/tutorials/versioning) | ||
<br/> Using DVC commands to work with multiple versions of datasets and ML | ||
models. | ||
|
||
2. [Pipelines](https://katacoda.com/dvc/courses/tutorials/pipelines) <br/> Using | ||
DVC commands to build a simple ML pipeline. | ||
|
||
3. [dvc fetch](https://katacoda.com/dvc/courses/examples) <br/> We will use an | ||
example project with some data, code, ML models, pipeline stages, as well as | ||
a few Git tags. Then we will see what happens with dvc fetch as we switch | ||
from tag to tag. | ||
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# Native Tutorials | ||
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1. [Versioning](/docs/tutorials/versioning) <br/> Using DVC commands to work | ||
with multiple versions of datasets and ML models. | ||
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2. [Pipelines](/docs/tutorials/pipelines) <br/> Using DVC commands to build a | ||
simple ML pipeline. | ||
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3. [Longer Tutorial](/docs/tutorials/tutorial) <br/> Introduces DVC | ||
step-by-step, while additionally explaining in great detail the motivation | ||
and what's happening internally. | ||
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# Community Blogs and Tutorials | ||
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- [Data Version Control Tutorial](https://blog.dataversioncontrol.com/data-version-control-tutorial-9146715eda46) | ||
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- [Creating an awesome project using DVC and DAGsHub](https://dagshub.com/docs/overview/) | ||
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- [Using DVC to create an efficient version control system for data projects](https://medium.com/qonto-engineering/using-dvc-to-create-an-efficient-version-control-system-for-data-projects-96efd94355fe) | ||
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- [Introduction to using DVC to manage machine learning project datasets](https://techsparx.com/software-development/ai/dvc/simple-example.html) | ||
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- [Managing versioned machine learning datasets in DVC, and easily share ML projects with colleagues](https://techsparx.com/software-development/ai/dvc/versioning-example.html) | ||
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- [A walkthrough of DVC](https://blog.codecentric.de/en/2019/03/walkthrough-dvc/) | ||
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- [DVC dependency management](https://blog.codecentric.de/en/2019/08/dvc-dependency-management/) | ||
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- [How to use data version control (dvc) in a machine learning project](https://towardsdatascience.com/how-to-use-data-version-control-dvc-in-a-machine-learning-project-a78245c0185) | ||
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- [My first try at DVC](https://stdiff.net/MB2019051301.html) | ||
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- [Effective Management of your Machine Learning Laboratory](https://www.linkedin.com/pulse/effective-management-your-machine-learning-laboratory-ulaganathan/) |
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# Interactive Tutorials | ||
|
||
1. [Data Versioning](https://katacoda.com/dvc/courses/tutorials/versioning) | ||
<br/> Using DVC commands to work with multiple versions of datasets and ML | ||
models. | ||
|
||
2. [Pipelines](https://katacoda.com/dvc/courses/tutorials/pipelines) <br/> Using | ||
DVC commands to build a simple ML pipeline. | ||
|
||
3. [dvc fetch](https://katacoda.com/dvc/courses/examples) <br/> We will use an | ||
example project with some data, code, ML models, pipeline stages, as well as | ||
a few Git tags. Then we will see what happens with dvc fetch as we switch | ||
from tag to tag. |
2 changes: 1 addition & 1 deletion
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static/docs/get-started/example-pipeline.md → static/docs/tutorials/pipelines.md
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