There are well-known issues faced by many machine learning developer in their day-to-day life.

Data Quality Issues:

• Getting the data for specific needs is not an easy task for data scientists.
• If data is obtained but there are still many issues so we need to preprocess them and convert them into problem fit.
• We need to apply the various methods by using the pandas and sklearn library for preprocessing the data.
• Data has many missing values, mismatched values, Outliers, and so on.

Feature Selection and Engineering

• The feature simply means columns in data.
• Lots of features are not good for machine learning models because all features are not equally important for prediction.
• For that, we need to apply feature engineering. Get the features only that are important for the specific problem.
• Some of the methods for Feature selection:
• Correlation coefficient
• Fisher’s Test
• Information Gain

Overfitting and Underfitting

• Overfitting and Underfitting are well-known issues in machine learning.
• Overfitting means the model is performing best in training but not well in testing data.
• Underfitting means the model is not performing well in training data.
• These are issues caused by insufficient data.

Model Complexity

• Selecting an appropriate model architecture
• Controlling model complexity to avoid overfitting

Exploding and Vanishing Gradients

• This obstacle was a major barrier to training large networks.
• This problem is more prevalent in deep networks with many layers, such as deep neural networks (DNNs) and recurrent neural networks (RNNs).

Transfer Learning Challenges

• Choosing the specific pre-trained model for our use cases.
• Finetuning the pre-trained model is also hard.

Data Leakage

• Unintentional inclusion of information from the test set in the training process

Deployment Challenges

• Many large models are not easily handled in production and also need more space for it.