Why Quality Data is the Lifeline of Machine Learning Models


We know Data is the new oil. Period!

If we can make all sorts of things with this Data, Why should we care to make it more authentic? Why remove noise (unwanted or irrelevant stuff) from the Data if we can create ML models out of it?

It’s always about the quality and not about quantity. You might have heard the phrase several times. It is apt for Data too. If you have scattered Data that contains more noise, then it’s of little use, no matter the quantity. On the other hand, if you have less Data but are more focused or are of good quality, you can be sure of building great ML models that will show fewer errors. Data Science is booming with opportunities. Enroll in this Data Science Certification and get a certificate that is recognized in top MNCs.

You have got the gist of the topic now. Let’s discuss it a little deeper to help you get a better understanding.

ML Pipeline

Before we start discussing the topic, let’s first understand what an ML Pipeline is? A Pipeline contains all the steps from the start till the end to complete a process. In this case, the ML pipeline commences from Data Collection to ML Model Production and Monitoring. ML Pipeline is cyclic and iterative to improve the quality and accuracy of the Model.

What are the different steps involved in making a good ML model?

  • Auditing Data
  • Ensuring the completeness of Data
  • Transforming Data
  • Feature Engineering
  • Training phase
  • Testing phase
  • Monitoring and Retraining phase

These are the fundamental steps in the ML Pipeline. Let me explain these processes in brief:

Auditing Data is the process where you have a birds-eye view of Data that you will be using to train the ML Model. You will look at the existing data, from where you have sourced it, its condition, your missing links, and determine the shortage of Data, if any, that you must have, to move ahead.

Ensuring the completeness of Data is the phase where after the 1st step, you fill in the Data that you require with the existing Dataset and figure out ways to integrate it with the Pipeline.

Transforming Data is the process where you will be cleaning the data to help the ML Model consider the Dataset as a unified source. All data, old or new, must be merged, properly labeled, and in harmony before plugging in the Data for the training phase.

Feature Engineering is where you have to decide which features are more efficient in helping the ML Model understand the underlying problem better. It’s like separating the whole Dataset containing images, videos, tabular sets, etc., into separate labeled Datasets and using a particular Dataset to help describe the underlying problem.

The Training Phase is the phase wherein you will be training the ML Model from the Dataset that you have collected, filled the gap, transformed, and determined the efficient features to help build the ML Model. Wish to join the exciting domain of ML? Check out this Machine Learning Course.

The Testing Phase is the phase where the ML Model that you build will undergo rigorous testing to determine the accuracy of the ML Model. You have to ensure that the Model is working properly before deploying it.

The Monitoring and Retraining Phase consists of processes that will monitor your deployed ML Models to check whether they perform as intended. And if any deviation from the training, deployed ML Model could be retrained through relevant Datasets.

Quality of Data Matters the most!

Data is fundamental to all emerging technologies. To build ML Models, you need Data, more importantly, you need quality Data. Because without proper Data, your built Model will be incorrect, perform poorly, and predict inaccurately.

The ML Pipeline is such that it requires Data at all steps. While building ML Model, the first checkpoint is to classify the aggregated and transformed Data into three parts; training, testing, monitoring, and retraining. You must not mix Data rather keep them separate and use them for ensuring the quality of the ML Model.

This Data is for detecting the best predictions and patterns in the Training Phase. It is to ensure the forecasts are correct and in line during the Testing Phase. And the last Dataset is used to determine new insights and Business predictions during the Monitoring and Retraining phase.

If you do not focus on the quality of Data from the start, it will become a huge problem later, for which there is no damage control. You must follow the Pipeline again for better results, thus incurring a heavy financial burden for Businesses. So, it’s all about the expertise in handling the Data. If you are interested in learning AI, take up an Artificial Intelligence Course Online to get up to speed in AI.

There are several questions to manage the Data well and understand its importance, and they are:

  • How to deal with Data?
  • How to classify them and based on what parameters?
  • What is the source of the Data?
  • How relevant is Data?
  • What is the Quality of Data?
  • What algorithms need to be applied?
  • How to make the Model better?
  • How to use Data differently for Training, Testing, Monitoring?
  • Is the insight produced relevant if not, how to ensure accuracy? Etc.

Summing up:

These modern-day technologies are nothing without Data. Merely possessing Data is not sufficient. You must handle it, master it, and make the most out of it to make a sensible development in any field.


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