rename misc to pandas

Author: robzor92

docs/concepts/dev/inside.md

@@ -10,15 +10,15 @@ Hopsworks provides a Jupyter notebook development environment for programs writt
 
 Hopsworks provides source code control support using Git (GitHub, GitLab or BitBucket). You can securely checkout code into your project and commit and push updates to your code to your source code repository. 
 
-### Bundled FTI Pipeline Environments
+### FTI Pipeline Environments
 
 Hopsworks assumes that an ML system consists of three independently developed and operated ML pipelines.
 
 * Feature pipeline: takes as input raw data that it transforms into features (and labels)
 * Training pipeline: takes as input features (and labels) and outputs a trained model
 * Inference pipeline: takes new feature data and a trained model and makes predictions
 
-In order to facilitate the development of these pipelines Hopsworks bundles several python environments containing necessary dependencies. Each environment may then also be customized further by installing additional dependencies from PyPi, Conda, Wheel files, GitHub repos or a custom Dockerfile. Internal compute such as Jobs and Jupyter is run in one of these environments and changes are applied transparently when you install new libraries using our APIs. That is, there is no need to write a Dockerfile, users install libraries directly in one or more of the environments. You can setup custom development and production environments by creating separate projects.
+In order to facilitate the development of these pipelines Hopsworks bundles several python environments containing necessary dependencies. Each of these environments may then also be customized further by cloning it and installing additional dependencies from PyPi, Conda channels, Wheel files, GitHub repos or a custom Dockerfile. Internal compute such as Jobs and Jupyter is run in one of these environments and changes are applied transparently when you install new libraries using our APIs. That is, there is no need to write a Dockerfile, users install libraries directly in one or more of the environments. You can setup custom development and production environments by creating separate projects or creating multiple clones of an environment within the same project.
 
 ### Jobs
 

docs/user_guides/projects/python/python_env_overview.md

@@ -39,15 +39,15 @@ The `MODEL TRAINING` environments can be used in [Jupyter notebooks](../jupyter/
 
 * `tensorflow-training-pipeline` to train TensorFlow models
 * `torch-training-pipeline` to train PyTorch models
-* `misc-training-pipeline` to train XGBoost, Catboost and SkLearn models
+* `pandas-training-pipeline` to train XGBoost, Catboost and SkLearn models
 
 ### Model inference
 
 The `MODEL INFERENCE` environments can be used in a deployment using a custom predictor script.
 
 * `tensorflow-inference-pipeline` to train TensorFlow models
 * `torch-inference-pipeline` to train PyTorch models
-* `misc-inference-pipeline` to train XGBoost, Catboost and SkLearn models
+* `pandas-inference-pipeline` to train XGBoost, Catboost and SkLearn models
 * `minimal-inference-pipeline` to install your own custom framework
 
 ## Next steps