What are the various challenges for machine learning practitioners?

While machine learning is rapidly evolving, it still has a long way to go. The reasons behind this are the various challenges an ML practitioner faces while developing an application. Let’s take a look at these challenges −

  • Data collection − Data plays the most important role in developing any machine learning application. Most of the work of an ML practitioner lies in collecting good quality data. If you are a beginner and want to experiment with machine learning, you can find datasets from Kaggle or UCI ML Repository. But if you want to implement real case scenarios or need to solve business problems, you need to collect the data either through web-scraping or from clients. Once collected, the data should be structured and stored in a database. For this, the ML practitioner would require additional knowledge of Big Data.
  • Quality of training data − Once data is collected, machine learning engineers need to do two things. One is to select an appropriate learning algorithm for a machine learning project and the other is to train the model using some of the acquired data. Here the biggest challenge is to choose good-quality training data. Quality of training data is important because the use of low-quality data leads to problems related to data preprocessing and feature extraction.
  • Non-representative training data − The training data should be representative i.e., it should generalize well for the new cases (the cases that are going to occur) also. Finding representative training data is a serious challenge for every ML practitioner because using non-representative training data can lead to false predictions.
  • Selecting relevant Features − If we use the training data containing the large number of irrelevant features, our ML model will never give the results as expected. Feature selection i.e., selecting good features for the success of an ML project, is one of the important aspects as well as another key challenge an ML practitioner should overcome.
  • Overfitting & underfitting the training data − The issue of overfitting happens when a ML model picked up the noise in the training data and learned it as concepts. Whereas the issue of underfitting, as the name entails, happens when it neither models the training data nor generalizes to new data. The goal of an ML practitioner should be to select a model at the sweet spot between underfitting and overfitting.
  • Deployment of the model − Another biggest challenge for lots of ML practitioners is to deploy their ML application successfully. It may be due to dependencies issues, low understanding of the business problem or underlying models, unstable ML models, etc.