Machine Learning Development Frameworks

ML development frameworks provide a toolkit for building and training ML models. Typical functionality provided by these frameworks includes:

• Data Handling: They assist with loading, preprocessing, and managing the data used to train and test the model. This might involve cleaning, formatting, and transforming the data into a suitable format for the chosen model.
• Model Building: These frameworks offer libraries of ML algorithms and tools to design the architecture of the constructed ML model. This includes specifying the type of model (e.g., neural network, decision tree), the number of layers and connections, and the mathematical operations performed within the model.
• Training and Optimization: Frameworks provide algorithms that iteratively adjust the model's internal parameters based on the training data and on the desired result. The goal is to optimize the model's performance in accomplishing the desired task (e.g., classification, ML regression). Some frameworks may support distributed training and enhance or fine-tune pretrained models.
• Evaluation: They offer tools to evaluate how well the trained model performs on unseen data. This might involve measuring accuracy, precision, and recall for classification tasks, or error rates for ML regression tasks (see 3.1.1).
• Deployment: Some frameworks provide capabilities for deploying the trained model for real-world use. This could involve converting the model into a format suitable for integration with web applications, mobile devices, edge devices or embedded systems. These frameworks can operate at different levels of abstraction. Some offer a lower-level application programming interface (API), providing developers with more control over model building but requiring more coding expertise. Others offer a higher-level API, simplifying model creation but offering fewer customization options.

Different frameworks can focus on different application domains. Some are general purpose and support a wide range of application areas. In contrast, others are more specialized, focusing on specific areas such as image recognition, speech recognition, and language translation.
Selecting the most appropriate framework can depend on several factors, such as:

• the application area;
• the need for a user-friendly interface for rapid prototyping;
• configurability for complex models;
• the expertise of the users;
• deployment considerations, as some frameworks are better suited for resource-constrained environments;
• level of (community) support;
• ecosystem maturity.