DVC and Git For Data Science

Name: DVC and Git For Data Science
Rating: 4.1 (34 reviews)

Master the Basics of Git and Data Version Control (DVC) for Beginners

Created byJesse E. Agbe

Last updated 7/2022

English

What you'll learn

Learn Version Control and Why We Need it?
Understand the Need for Data Version Control
Git and Github For Data Science Project
Master DVC For Data Science Project
Explore DAGsHub
Build Your Own Custom Version Control Tool (Git) From Scratch

Course content

7 sections • 54 lectures • 9h 20m total length

Introduction5:03
Course Guide1:13
Setting Up and Installing Packages & Course Materials6:05
What is Version Control?4:03
Importance of Version Control1:39
Understand how version control clarifies code, data, and models by tracking changes and enabling time travel for historical review, speeding development and enabling team collaboration.
Data Version Control - The What and Why?4:46
Version Control Tools1:08
Project Structuring Using Cookiecutter12:52

What is Git?3:02
Git Workflow - Theory3:32
Configuring Git3:07
Github Platform6:58
Explore using GitHub as a remote code repository, including signing up, creating repositories, cloning and pushing code, and managing issues and pull requests.
Configuring SSH For GitHub6:10
generate an ssh key pair, optionally set a passphrase, and add the public key to GitHub settings to enable push access without passwords.
Git Essentials - Creating a Repo6:22
Git Workflow - Practical13:53
Git Essentials - Commit & Best Practices13:39
Git Essentials - Undoing Uncommitted Changes4:57
Git Essentials - Exploring Git Commit on Github2:50
Git Essentials - Git Logs9:59
Git Essentials - Branching For ML Model and Data Science7:37
Explore branch types: main branch, master branch, and develop branch, plus feature, release, and hot phase rate branches; learn to create, switch, rename, and delete branches locally and remotely.
Git - Tricks & Tips5:16
Learn practical git basics for data science workflows, including tracking files, identifying ignored vs untracked files, and essential commands such as init, clone, pull, push, and fetch to manage changes.
GitHub - Advanced Search15:38
GitHub-Dorks36:07

Intro & Setting up Workspace15:52
Data Versioning and Pushing Data to Dagshub11:59
Data Preparation & Model Building22:16
Git Branching for ML Models7:37
Learn practical git branching for machine learning workflows: create and manage branches (main, develop, feature, release, hotfix), switch between them, rename, view, and delete branches for model versions.
Model Storage on DagsHub with Git & DVC10:07
Saving New ML Models to a New Branch11:05
ML Experiment Tracking with DagsHub18:57
Learn to track machine learning experiments with DagsHub, logging metrics via yaml configurations, recording model parameters, comparing base and improved models, and pushing results through git workflows.

Introduction & Manually Running ML Pipelines7:10
DVC Pipelines - DVC Run Interactive Experiment9:24
DVC Pipelines - DVC Run12:09
DVC Pipelines - DVC Metrics3:59
DVC Pipelines - DVC Repro12:55
DVC Pipelines - Pushing Data and Code to DagsHub13:07
Push your DVC pipelines from your workspace to DagsHub by configuring a remote and private repository, then push data and code with git and DVC.
DVC Pipelines - Fixing Error with Push and Pull1:56

Requirements

Basic Understanding of Python and Machine Learning
Determination and Willingness to Learn

Description

Our modern world runs on software and data, with Git - a version control tool we track and manage the different changes and versions of our software. Git is very useful in every programmer's work. It is a must-have tool for working in any software-related field, that includes data science to machine learning.

What about the data and the ML models we build? How do we track and manage them?

How do data scientist, machine learning engineers and AI developers track and manage the data and models they spend hours and days building?

In this course we will explore Git and DVC - two essential version control tools that every data scientist, ML engineer and AI developer needs when working on their data science project.

This is a very new field hence there are not a lot of materials on using git and dvc for data science projects. The goal of this exciting and unscripted course is to introduce you to Git and DVC for data science.

We will also explore Data Version control, how to track your models and your datasets using DVC and Git.

By the end of the course you will have a comprehensive overview of the fundamentals of Git and DVC and how to use these tools in managing and tracking your ML models and dataset for the entire machine learning project life cycle.

This course is unscripted,fun and exciting but at the same time we will dive deep into DVC and Git For Data Science.

Specifically you will learn

Git Essentials
How Git works
Git Branching for Data Science Project
Build our own custom Version Control Tools from scratch
Data Version Control - The What,Why and How
DVC Essentials
How to track and version your ML Models
DVC pipelines
How to use DAGsHub and GitHub
Label Studio
Best practices in using Git and DVC
Machine Learning Experiment Tracking
etc

Who this course is for:

Anyone interested in Learning Git and DVC
Data Scientist curious about Data Version Control
Students

DVC and Git For Data Science

What you'll learn

Explore related topics

Course content

Module 01 - Introduction8 lectures • 37min

Module 02 - Git Essentials For Data Science15 lectures • 2hr 19min

Module 03 - Building A CLI for Version Control From Scratch4 lectures • 2hr 22min

Module 03 - DVC Essentials8 lectures • 44min

Module 04 - DAGsHub5 lectures • 40min

Module 04 - End to End Data Science Project with DVC and Git7 lectures • 1hr 38min

DVC Pipelines - Makefiles for Data Science Project7 lectures • 1hr 1min

Requirements

Description

Who this course is for: