AutoML with Prevision.io
By: Zeineb Ghrib
Tags: Machine Learning, Automl, Python, Regression, Data Science

Overview of Prevision.io auto-ml platform

What is Automated Machine Learning?

Each data scientist, whatever his level of expertise, would tell you that applying traditional end to end machine learning process to real-world business problems is very tedious, time and resource consuming and challenging.

Automated machine learning addresses these issues by applying a systematic process of iterative and time consuming tasks required to develop a machine learning model. All repetitive steps such as model building / training/ selection / tuning .. are fully automated and parallelized.

What about Prevision.io ?

Prevision.io provides an automated machine learning platform to generate and deploy highly accurate predictive models on cloud or on-premise. We propose a friendly interface that can be used without any prior technical knowledge or infrastructure and build standalone models.

At the implementation level, the platform incorporates machine learning best practices from top-ranked kagglers / data scientists to ensure highly efficient models while keeping all the complexity aspects opaque to users.

Prevision.io activity is basically focused in France and is the only French vendor in the AI Cloud Provider. In January 2020, Prevision has been Named a Visionary in the Gartner Magic Quadrant.

How to use Prevision Auto-ML Solution?

Data scientists and machine learning developers who want to keep hold of their ML workflow code source can take advantage of the great power of Prevision AutoML services via Prevision python package without our front-end application. We developed a Software Development Toolkit that allows you to build and launch machine learning use cases within Prevision services. Hence, You can interact with the service in any Python environment (Jupyter Notebooks, Pycharm, VS Code, …). An R package is also provided.

In this post I will show you how to use automated machine learning via Prevision python package, to create a regression model to predict median housing prices. Then I will expose the traditional method and see how it would be much easier to use AutoML comparing to self-made programming machine learning model.

Pre-requisistes

MASTER_TOKEN:

In order to initialize client workspace via the SDK and interact with its corresponding Prevision.io plateform instance, an API Token is required for authentication . It is obtained by going to the user menu and clicking on the API key item:

To copy the KEY clipboard go to the right of the screen and click on copy as shown below:

Install:

git clone https://github.com/previsionio/prevision-python.git

python ./prevision-python/setup.py install

Setting configuration and connection

In the code block below we will import the Python package to initialize the client instance to interact with its Prevision.io workspace. This needs to be done once per session.

import previsionio as pio

import pandas as pd



URL = 'https://XXXX.prevision.io'

TOKEN = '''YOUR_MASTER_TOKEN'''



# initialize client workspace

pio.prevision_client.client.init_client(URL, TOKEN)

Do not forget to change the values of the TOKEN with the generated key and the URL endpoint with the name of your instance in order to continue running this notebook.