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Workflow of a Machine Learning Project

Workflow of a Machine Learning Project

Last Updated on July 25, 2021 by Editorial Team

Author(s): Raman Kumar Jha

Machine Learning

Machine learning algorithms can learn from A to B or input to output mapping. If we take an example of a speech recognition software like Amazon Echo/Alexa, Google Home, Apple Siri which can be easily found in our homes, then there arises a question that how do they recognize when we say Alexa, hey Google, or Siri?

Speech Recognition Devices

So let’s go through the key steps of machine learning projects. If we want to build a machine learning system then there are mainly three steps that need to be followed(here speech recognition system is used for the explanation):

1. Collect Data:
Collecting data is the initial step to build any machine learning project. For a speech recognition system, we should go around asking people to say Hey Alexa or something else relevant to this so that we could record the audio for the project. In this way, we could collect lots of data for our project that would make it much efficient and accurate.

2. Train Data:
In this phase, now we will train the machine learning model with the help of various machine learning algorithms. By using the algorithm, we will train the model to learn input to output or A to B mapping. Here, as we talk about speech recognition, the system will be trained to learn and recognize whether the user said Hello Alexa or Hey through the collected audio.
Whenever an AI team starts to train the model, most of the time it would not work in the first attempt. AI team should train the model multiple times or in AI, it is said to iterate many times until the model starts to perform correctly.

3. Deploy Model:
This is the last but a very crucial step for a machine learning system. In this step, we will deploy the model and put it in a device. After that, it will be shipped to a small group of test users or a large group of users. The more the model is trained with a large and variety of data the more the chance of performing better.
If we take the example of a speech recognition system trained with a USA accent and then shipped to the UK, this model will not be able to recognize the audio of users as it was not trained with it.

Workflow for Machine Learning

This is the workflow of a machine learning project that will be helpful for each and every type of project. These are key points that always need to be considered before starting a new machine learning project.

Workflow of a Machine Learning Project was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

Published via Towards AI

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