In this article, you will learn how to implement the MVC design pattern to create an application that will serve a machine learning model to make inferences. To create this application, we will use Tkinter which is the native library to create graphical interfaces with Python. However, we will not see in this article how to train a machine learning model and how to serialize it. Just as I would not discuss the library Tkinter in detail.
We will start by quickly seeing the theory of the MVC design pattern before implementing it in Python.
MVC is a design pattern…
If you are not dealing with big data you are probably using Pandas to write scripts to do some data processing. If so, then you are certainly using Jupyter because it allows seeing the results of the transformations applied. However, you may have already noticed that notebooks can quickly become messy.
When the start-up phase comes, the question of reproducibility and maintenance arises. Tools such as paper mill allow you to put a notebook directly into production. However, this does not guarantee reproducibility and readability for a future person who will be in charge of maintenance when you are gone.
The visualization of the data allows us to quickly bring insight. However, it can be difficult to deploy a data visualization solution because it requires most of the time either the acquisition of a license or a server.
In this article, we will see how to use free tools to build a dashboard to present some fake sales, you can download the data here in order to follow the tutorial.
In this post, it will be about deploying a scikit-learn machine learning model using serverless services because it allows us to let the model deploy and only pay for the time it will be used unlike using a server.
Before going further, you will need to have the following tool installed on your environment:
First, let’s see from a global point of view the application we are going to deploy.
If you work in the data analysis world, chances are you do a lot of data wrangling. If you use pandas in your data workflow, you’ve probably noticed that you often write the same bits of code.
Although some complex datasets or data exploratory require going to Jupyter notebooks, on the other hand, some datasets require simple processing, going through the process of setting up an environment, and creating a new notebook can be a little overwhelming.
So you probably end up opening it in a spreadsheet. …
If your organization does not have a data visualization solution like Tableau or PowerBI nor means to host a server to deploy open source solutions like Dash then you are probably stuck doing reports with Excel or exporting your notebooks.
In this post, I will walk you through an example of how you can do reports using Python and Vue.js and send them by email to stakeholders.
First, let’s see from a global point of view the process we are trying to automate. Reporting process generally consists of :
With the advent of microservices, there are a lot of APIs for interacting with, whether for orchestrated systems or to extract data. Using Python for the dedicated APIS workload is appropriate because of its versatile nature.
In this article, I share with you 3 code snippets that will help you better manage your APIS dependent workflows. I will use for demo purposes the JSON placeholder fake API.
When you want to make several calls to the same resource with a changing parameter, you have to move towards a concurrent approach. …
Data analyst | Computer science| Data engineering | Data science