Definition
A Machine Learning (ML) model is a program that has been trained on a dataset using an algorithm. By learning from the patterns found in the training data, the model can then identify patterns and make predictions on new data. ML models come in many types, and they can be categorized based on the techniques they use to recognize patterns.
Machine learning is a branch of artificial intelligence (AI) focused on training machines and computers to imitate the way humans learn, autonomously perform tasks, and improve their accuracy through exposure to more data and experiences. A machine learning model is a computer program that can make decisions automatically based on pattern recognition. It’s essentially a mathematical representation of relationships within a dataset that can be used to make classifications or informed decisions on unknown data.
Businesses can benefit from ML models to automate complex tasks, such as detecting patterns or anomalies within large volumes of data. However, given the many types of ML models available today, as well as the complexity of the model development and deployment process, choosing and implementing the right model for a particular use case can be challenging.
Machine Learning Model Overview
An ML model is a software program that can automatically recognize patterns. ML models are able to do this because they undergo a training process that exposes them to data and enables them to “learn” which patterns exist within the data.
This approach to pattern recognition and data processing makes ML models distinct from other types of software applications. Other types of programs require explicit logic to process information or make decisions. Developers must write code that defines how an application should behave in response to specific input.
Furthermore, ML models can make decisions or take action without requiring explicit instructions in their code that define how to respond to each user action or request. Instead, ML models rely on pattern recognition to guide their behavior. This capability is valuable because it means that ML models are effective for use cases where it would be impossible or infeasible to anticipate every possible data input and implement logic to handle it.
For instance, an ML model that powers a chatbot can formulate responses to open-ended user input without requiring instructions to identify each precise string that users may enter and generate an appropriate response. Instead, the model generates responses by comparing user input to question-response patterns within the model’s training data, and then generating responses to the input that follow the same patterns as those present in the training data.
ML models can be categorized based on the methods they use to process data and identify patterns. Common examples include:
- Linear regression: Linear regression, one of the simplest types of ML model techniques, determines connections between variables of interest and inputs by predicting continuous numerical values.
- Support Vector Machines (SVM): Another relatively simple type of model that works by separating data into categories for classification and regression.
- Decision trees: A more advanced model design that creates a tree-like model of decisions and their consequences.
- Random forest: A variation on decision-tree models that generates multiple decision trees and combines their output to make decisions.
- Boosting algorithm: An approach that assesses relationships between data points to convert weak learners (meaning relationships that are random or close to random) to strong learners (which entail meaningful relationships between data).
- Neural networks: An advanced type of ML model that mimics the structure and functioning of the human brain to recognize relationships within complex, multi-layered sets of data.
ML Model Training Techniques
In addition to the model designs described above, models can be categorized based on the data training methods they use. The three most common training techniques include:
- Supervised learning models: These models rely on labeled data for training. This means that before the data is exposed to the models for training, it receives tags or labels that identify the type of information represented within the data. Based on the tags or labels, the model can recognize patterns in the relationship between data. A challenge of supervised learning is that labeling data is a manual process that can take a long time.
- Unsupervised learning models: An unsupervised model trains on data that is not labeled. Instead, the model automatically identifies relationships between data, typically by parsing large volumes of data to recognize patterns embedded within it. Because unsupervised learning doesn’t require labeled data, acquiring training data for unsupervised models is typically easier than for supervised models; however, the lack of labeled data can also make it more challenging to guide the model toward specific types of output.
- Reinforcement learning: Under this approach, a model learns through trial and error by interacting with an environment and receiving feedback about the validity of each interaction. Reinforcement learning can help guide models toward optimal outcomes without requiring ML developers to label data before the training process begins.
Some ML models combine multiple approaches. For example, many of the Large Language Models (LLMs) behind today’s popular generative AI platforms, such as OpenAI’s GPT models, were trained mostly on unlabeled data. This means they were unsupervised for most of the training process. However, because they also received some labeled data, they can be considered partly supervised models.
Steps for Building an ML Model
Building an ML model is a multi-step process. Key stages include:
- Data Collection: A crucial step in the creation of a machine learning model, as it lays the foundation for building accurate models. In this phase of machine learning model development, relevant data is gathered from various sources to train the machine learning model and enable it to make accurate predictions.
- Data preprocessing: This step involves cleaning the dataset by replacing missing values, removing duplicate information, and normalizing or scaling data to ensure uniformity.
- Feature selection: Feature selection identifies the most relevant attributes to improve model performance and reduce complexity.
- Algorithm selection: Teams select an algorithm that aligns with the problem type (such as classification, regression, or clustering) and the nature of the dataset they are working with. They also factor in considerations like interpretability, the computational resources required to train the model, and the model’s ability to perform adequately when working with a large volume of data.
- Model training: Training involves exposing the model to a portion of the preprocessed data, as well as fine-tuned hyperparameters (which address priorities like optimizing model performance).
- Evaluation: After training is complete, the model is evaluated by comparing how it behaves in response to input data. If the model doesn’t operate as desired, developers may perform additional training based on new or expanded training data, or using modified hyperparameters.
After building a model, ML developers can complete model deployment. This is the process of making the model accessible to end-users in a production environment. The model then performs inference, the process by which it reacts to real-world user input.
Streamlining ML Model Management with JFrog
As Machine Learning models become an increasingly central part of the IT landscape, it is becoming critical for businesses to manage models efficiently and integrate model workflows seamlessly into their existing software development lifecycle (SDLC).
JFrog’s ML model management capabilities can help. JFrog bridges the gap between ML on the one hand and software development, security, and IT operations on the other by providing a central platform for storing models, data, and other artifacts, linking them to software development workflows, and deploying and operating models in production. Learn more by scheduling a demo.
Continue to explore more about AI and ML using the links below, or see the platform in action by scheduling a demo, or starting a free trial at your convenience.