Life cycle of machine learning project

Introduction

Laymen explanation

The data science life cycle commences with diagnosing a problem or issue and presenting a solution to those problems. Generally, data scientists set up a process to collect and analyze data on an ongoing basis. If you are interested to know its development life cycle, then this document helps you.

Technical explanation

Machine learning (ML) has been touted as one of the key enablers of the Fourth Industrial Revolution. In recent times, businesses explore new approaches to maximize their profits and reach, without compromising on customer services. Machine learning helps them mine data from relevant sources and analyze it to understand trends, behavior and more. As IT enterprises integrate ML-driven insights into their organizational framework, MLOps is leveraged to enhance the operations and deployment during the lifecycle of machine learning model development and usage.

Important Steps in supervised learning

Identification of the Problem

This step is applicable for any project (for example, software project)

Choice of a Representative Sample

You select a representative sample by determining which variables you need to answer the question or solve the problem posed in your project.

Select performance measure

Your next step is to select a performance measure. A typical performance measure for regression problems is the Root Mean Square Error (RMSE).

Check assumptions

It is good practice to list and verify the assumptions that were made so far (by you or others); this can catch serious issues early on.

Data Gathering

Collects the necessary data for the project. Some data scientists write their programs or work with data engineers and design applications that mine the required data.

Data visualisation

Its objective to find patterns. Our brains are very good at spotting patterns on pictures, but you may need to play around with visualisation parameters to make the patterns stand out. Refer here for detail

Data Cleaning

It involves transforming the data you collect into a convenient form and ensuring that it applies within the representative sample.

Development of a Data Model

Data model is the step most people associate with data science.

MLOps vision

MLOps works on integrating three domains as shown below. This writeup talks about software tools for the same.