Data Training Your Machine Learning Project

Category: AI Insights

Published date: 05.03.2020

Read time: 5 min

Many people believe that the most difficult problem connected with machine learning projects has something to do with neural nets and other technical aspects. However, an even bigger problem is getting the right data in the format that you need it. This involved getting the data that matches the outcomes you would like to predict. For example, if you are building a facial recognition system, you do not need cute pictures of cats. Furthermore, you cannot start creating your project until you have the right data and it has been annotated.

Let’s take a look at the data training stage in closer detail and why data annotation plays a crucial role at this stage of the project.

Data Training

The Importance of Data Training

the data training set that you run through the neural network will probably be the largest data set that you will be using in the entire project. The reason is that you need a large data sample to enable the machine to recognizes what it needs to regardless of the circumstance. For example, if you are creating an AI project for self-driving cars, you will need the machine learning algorithms to recognize all of the things that can be encountered on the road. This includes street signs, other vehicles, pedestrians and a lot of other things.

What compounds this problem is that everything that we mentioned above can be found in all kinds of varieties, shapes, and sizes. Nonetheless, your product needs to be able to identify them and perform the correct action. For your data training process to work, you will need human data annotators who can take the raw data and label it with everything you would like the machines to recognize. This usually involves thousands and thousands of images and hours spent label everything in the images.

A lot of businesses choose to outsource their data annotation services because it is very time consuming and their time can be better spent on their core business functions. Having said this, the human data annotators need to understand that their job is very important and needs to be done with great precision and care. The entire data training stage and the outcome of the entire project depends on it.

Testing the Functionality

Once the data annotation and the data training processes are over, it is time to test the quality of jobs performed in the previous stages. In order to do this, you will need a test data set. During the testing stage, you will need to evaluate the job done by the data annotation teams but also look for any biases. In fact, researchers have noticed that the AI product that was created can have the same biases as people. The reason is that the people who labeled the data using their own judgment and opinions and now there are faults in the end-product.

This just goes to show once again the importance of the data annotation teams during the data training and other processes because if this part is not done right, you will have to go back to the beginning and fix everything.

Obtaining a Sizeable Data Training Set

Not having enough data to train the machine learning algorithms can be just as bad as doing the data annotation part poorly. One of the biggest problems is obtaining a large enough data set because it takes a lot of time to accumulate all of the files, especially if you have paper documents. If your timeframe for the project does not allow you enough to collect the needed amount of data, you can outsource this part of the project as well as long as you have determined the objective of the machine learning solution.

Regardless of how big of a data training set you need or how much data you need to be annotated, there are data annotation services out there that will be glad to help you and can handle any amount of data your project requires. It is important to avoid any missteps in the early stage stages that could cause your project to be delayed.

Posted by Il’ya Dudkin


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