## 2 Background and Related Work  
### Machine Learning  
To identify the needs for machine learning in a general context, it is essential to understand the conventional ML process and its relationship with data science. While data science focuses on obtaining raw data and transforming it into useful information for decision-making, ML builds on this foundation by using data to develop predictive models that automate complex tasks such as classification, regression, diagnosis, planning, and recognition. The ML process offers a structured and repeatable approach for extracting patterns and insights from data, enabling applications across a wide range of domains. The ML process typically involves two primary activities: training and prediction, including data collection, preprocessing, and feature extraction (Figure 1). Data collection is the initial step, where raw data is systematically gathered from various sources to ensure it adequately represents the problem space. This data must then undergo preprocessing to address issues such as missing values, duplicates, noise, or inconsistencies. Preprocessing also involves transforming the data into a usable format (e.g., normalization or scaling) and reducing its complexity through dimensionality reduction techniques. In the training phase, the preprocessed data is used to build a model capable of learning from historical patterns. The data is typically divided into subsets, including a training set for fitting the model and a validation set for tuning its hyperparameters. Feature extraction plays a significant role here, as it focuses on identifying and selecting the most relevant variables (features) for analysis, allowing the model to concentrate on meaningful correlations. The training phase concludes with an evaluation of the model using a separate test dataset to measure its performance and generalizability, resulting in the creation of the final ML model. In the prediction phase, the trained ML model is deployed to process new data and make predictions. Similar to the training phase, new data must be preprocessed, and relevant features must be extracted before being fed into the model. The model then outputs predictions or classifications that support decision-making and enable automated responses to various tasks or problems. Figure 1 illustrates this traditional ML workflow, emphasizing the iterative nature of the training and prediction activities. The key concepts of machine learning are shown in Figure 2. The model distinguishes between different dataset forms including raw, preprocessed, labeled, unlabeled, and augmented data, which represent essential building blocks across the ML lifecycle. Different types of analytics can be distinguished, including descriptive analytics (what happened), diagnostic analytics (why it happened), predictive analytics (what is likely to happen), prognostic analytics (what will happen if trends continue), and prescriptive analytics (what should be done). Data analytics is performed using ML algorithms. Different types of ML can be distinguished based on how they learn from data including Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Self-Supervised, and Reinforcement Learning. ML techniques are applied to a variety of tasks across numerous fields. Different ML tasks include Regression, Classification, Clustering, Dimensionality Reduction, Anomaly Detection, Ranking, and Generative Modeling. ML training strategy refers to how a model learns from data, such as in batch mode (training on the full dataset), online mode (incrementally updating with new data), transfer learning (adapting a pretrained model to a new task), and active learning (where the model selectively queries the most informative data points to optimize learning efficiency with minimal labeled data). Infrastructure mode describes where and how training occurs--centrally on a single server, distributed across multiple machines, or in federated setups where training happens locally on devices without sharing raw data.