Update README.md

Author: abhiojha8

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@@ -3,22 +3,20 @@ Working in a command line environment is recommended for ease of use with git an
 # Environment Set up
 * Download and install conda if you don’t have it already.
     * Use the supplied requirements file to create a new environment, or
-    * conda create -n [envname] "python=3.8" scikit-learn dvc pandas numpy pytest jupyter jupyterlab fastapi uvicorn -c conda-forge
+    * conda create -n [envname] "python=3.8" scikit-learn pandas numpy pytest jupyter jupyterlab fastapi uvicorn -c conda-forge
     * Install git either through conda (“conda install git”) or through your CLI, e.g. sudo apt-get git.
 
 ## Repositories
-* Create a directory for the project and initialize git and dvc.
-    * As you work on the code, continually commit changes. Generated models you want to keep must be committed to dvc.
+* Create a directory for the project and initialize git.
+    * As you work on the code, continually commit changes. Trained models you want to use in production must be committed to GitHub.
 * Connect your local git repo to GitHub.
 * Setup GitHub Actions on your repo. You can use one of the pre-made GitHub Actions if at a minimum it runs pytest and flake8 on push and requires both to pass without error.
     * Make sure you set up the GitHub Action to have the same version of Python as you used in development.
-* Set up a remote repository for dvc.
 
 # Data
 * Download census.csv and commit it to dvc.
 * This data is messy, try to open it in pandas and see what you get.
 * To clean it, use your favorite text editor to remove all spaces.
-* Commit this modified data to dvc (we often want to keep the raw data untouched but then can keep updating the cooked version).
 
 # Model
 * Using the starter code, write a machine learning model that trains on the clean data and saves the model. Complete any function that has been started.