What are the key steps to build an effective machine learning pipeline?

Building effective ML pipelines requires following a well-designed workflow that begins with data cleaning—removing NAN values, corrupted data, and duplicates—which is crucial as models cannot process missing values and duplicates can create bias. The next step involves data transformation, where you must determine the optimal representation format, potentially reducing dimensionality through techniques like PCA, and addressing imbalanced datasets. Additional considerations include properly encoding and decoding data (especially important for LLMs), evaluating whether to use all features or reduce them to principal components, and ensuring the model isn't biased toward specific classes of samples. Following this structured approach helps create robust models that perform optimally and demonstrates your understanding of ML concepts to potential recruiters.