Merge branch 'cid/error-handling' into rishab/data-schema-refactor

Author: whoseoyster

.gitignore

@@ -18,7 +18,6 @@ examples/*
 !examples/tabular-classification
 !examples/text-classification
 
-
 # Documentation generated files #
 #################################
 docs/source/generated

docs/source/reference/upload.rst

@@ -13,6 +13,15 @@ Client constructor
 
    UnboxClient
 
+Project creation and loading
+----------------------------
+.. autosummary:: 
+   :toctree: api/
+   :template: class.rst
+   
+   UnboxClient.create_project
+   UnboxClient.load_project
+
 Upload functions
 ----------------
 .. autosummary:: 

examples/README.md

@@ -0,0 +1,52 @@
+<div align="left">
+  <img src="logo-blue-text.svg"><br>
+</div>
+
+# Examples Gallery | Unbox AI
+
+[![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=Unbox:%20The%20debugging%20workspace%20for%20ML%20&url=https://github.com/unboxai&via=unbox_ai&hashtags=mlops)
+[![PyPI Latest Release](https://img.shields.io/pypi/v/unboxapi.svg)](https://pypi.org/project/unboxapi/)
+[![downloads](https://pepy.tech/badge/unboxapi)](https://pepy.tech/project/unboxapi)
+
+This repository contains a gallery of sample notebooks illustrating the process of **creating projects and uploading models & datasets** to the Unbox platform.
+
+## Why this repository exists?
+
+This repository fulfils two main goals:
+
+1. It demonstrates our Python API in action
+2. It provides simple examples that may be similar to the problems you are solving at your organization.
+
+We take privacy & security very seriously and understand that your models and datasets might contain sensitive or proprietary information. [Reach out to us](mailto:[email protected]) if you are interested in our on-premise solution.
+
+In the meantime, pick a task, model framework or problem that shares similarities with something you are working on and start exploring Unbox right away!
+
+## What is Unbox?
+
+Unbox is a debugging workspace for ML & Data Science. Unbox combines and builds upon SOTA techniques in explainability, model and dataset versioning, synthetic data generation, data-centric testing and much more to form a powerful, **unified platform for model development**.
+
+👉 [Join our Slack community!](https://l.linklyhq.com/l/1DG73) We'd love to meet you and help you get started with Unbox!
+
+## Installation
+
+To run the notebooks in this repository, you'll need to have the `unboxapi` library installed.
+
+Install with PyPI (pip)
+
+```console
+pip install --upgrade unboxapi
+```
+
+or install with Anaconda (conda)
+
+```console
+conda install unboxapi --channel conda-forge
+```
+
+## Documentation
+
+This repository complements the rest of the documentation. Navigate [here](https://docs.unbox.ai) for a quickstart guide and for in-depth tutorials. The full Python library reference can be found [here](https://reference.unbox.ai).
+
+## Contributing
+
+All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome! Just send us a message on [Slack](https://l.linklyhq.com/l/1DG73).

examples/tabular-classification/churn-classifier/Churn_Modelling.csv

@@ -0,0 +1,4 @@
+unboxapi
+numpy==1.20.1
+pandas==1.1.4
+scikit-learn==0.24.1