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from IPython.display import HTML, JSON
from google.colab import output

# first, register the python function to use
def array():
  return JSON([1,2,3])  # can send any JSON 
output.register_callback('array', array)

# then invoke it later in an %%html block
%%html

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'''Specialized Preprocessors'''

import nbconvert, nbformat, re, sys
from nbconvert.preprocessors import ExecutePreprocessor, Preprocessor
from traitlets import Dict, Unicode
from textwrap import dedent

from warnings import warn
def warn_deprecated(msg):
    '''Raise a DeprecationWarning'''

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Deploying a Production ready Django app on AWS

In this tutorial, I will be going over to how to deploy a Django app from start to finish using AWS and EC2. Recently, my partner Tu and I launched our app Hygge Homes (a vacation home rental app for searching and booking vacation homes based off Airbnb) and we wanted to share with other developers some of the lessons we learned along the way.

Following this tutorial, you will have an application that has:

• An AWS EC2 server configured to host your application
• SSL-certification with Certbot
• A custom domain name
• Continuous deployment with Github Actions/SSM Agent

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---
title: SF Crime 👮
description: Dashboard of San Fransisco Crime Dataset with Mercury
show-code: False
params:
    limit:
        input: slider
        label: Limit number of cases
        value: 1000
        min: 100

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Mercury Contributor License Agreement

I disavow any rights or claims to any changes submitted to the Mercury project and assign the copyright of those changes to MLJAR Sp. z o.o.

As far as the law allows, my contributions come as is, without any warranty or condition, and I will not be liable to anyone for any damages related to this software or this license, under any kind of legal claim.

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'''
MLJAR AutoML can work in 4 modes:
- Explain
- Perform
- Compete
- Optuna
'''

# training in Explain mode, perfect for data exploration
automl = AutoML(mode="Explain")

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