Overview of Machine Learning Pipeline

Machine learning is all about identifying patterns or relationships in data and using them to make accurate predictions. An ML pipeline is a multi-step process involving different stages that guide the development of an algorithm capable of making predictions or classifications based on input data. The term “training” refers to the process of feeding data to the ML Pipeline and allowing it to adjust its internal parameters to improve predictive accuracy.

In this lesson, we’ll understand the different stages of the ML pipeline.

Data collection

Data collection is the first step in any ML project. This phase involves gathering relevant data that represents the problem the model is expected to solve. The goal is to collect data that is as comprehensive and relevant as possible, covering all possible variations of the target problem. Data can come from various sources, such as sensors, web scraping, internal databases, or public datasets. The data quality directly impacts the model’s accuracy; therefore, thoroughness and relevance in data collection are essential.

Data preparation

Data preparation is the process of cleaning, transforming, and organizing the data to make it suitable for training a model. This phase is critical as raw data often contains errors, inconsistencies, or missing values that can negatively impact model performance. Data preparation involves tasks like removing duplicates, handling missing values, and normalizing data. Techniques like scaling, encoding categorical variables, and handling outliers are also part of this phase. Data splitting (into training, validation, and test sets) is often done here to ensure an unbiased evaluation later.

Essential data preparation concepts include:

• Data pruning: Removing irrelevant or noisy data points to ensure the dataset is representative and manageable.

• Imputation: Handling missing values using techniques like mean substitution or predictive modeling.

• Scaling and normalization: ...