In the vast and seemingly endless world of artificial intelligence (AI), machine learning (ML) has emerged as one of the most promising fields. With its business implications and enterprise benefits, there’s little wonder why organizations are embracing ML development as a key growth strategy.
What is a machine learning model?
Much like a workload is the sum of an application and its data, a machine learning model is (fundamentally) an algorithm trained by a great deal of data to recognize specific patterns, solve problems, or make predictions based on its findings.
In other words, ML models can generate outputs based on previously unseen data sets. For example, natural language processing (NLP) is a subset of machine learning that can recognize the intent behind sentences and word combinations. This capability is instrumental in the development of large language models (LLMs), a branch of ML that can actually generate and predict new content.
The final deployment of an ML model—usually in the form of a program; sometimes like a typical app with a user interface (UI), sometimes embedded into devices—may include additional rules and data structures. But the model's core elements are its algorithm, the data it was trained on, and the data it ingests while in active deployment.
It gradually learns on its own as more and more data passes through it and, as a result, can improve its performance as time goes by. ML models are used in numerous industries but have also found popular appeal through consumer-facing generative AI applications like DALL-E and ChatGPT.
4 categories of machine learning models
ML models, like all AI applications, are developed by ingesting large amounts of data. This training process, for all intents and purposes, is where the “learning” comes into play.
However, not all models learn the same way. As such, they’re normally categorized according to the type of algorithm used in the training process and whether it ingests labeled data, unlabeled data, or a combination of both. These four main categories include:
- Supervised learning. A supervised learning algorithm uses a labeled dataset where every input is associated with a corresponding output label. This mapping makes it easier for the algorithm to make predictions or generate new, unseen data.
- Unsupervised learning. In unsupervised learning, unlabeled datasets require the algorithm to explore the inherent structure and patterns within the data without explicit instruction. These models cannot make predictions but do have remarkable pattern recognition capabilities.
- Semi-supervised learning. This approach uses both labeled and unlabeled data. Models trained through semi-supervised learning can make predictions, but their capabilities may vary.
- Reinforcement learning. Models trained by reinforcement learning take a trial-and-error approach. This involves assigning positive and negative values to desired and undesired actions, the goal being to encourage the program to avoid negative training examples and seek out the positive.
Regardless of how they’re trained, all machine learning models should ultimately have the power to continuously improve without being explicitly programmed. Once deployed, they keep training and growing when exposed to new data.
Common types of ML models
Broadly speaking, you can also categorize machine learning models based on the nature of the problem they’re trying to solve. In ML, two functions stand out among the rest: classification and regression (a form of predictive modeling).
Classification models recognize and categorize objects or ideas into groups based on a predefined (and finite) set of group types (i.e., values). Therefore, they produce a discrete label or category that represents the different classes the model has learned to distinguish. For instance, an algorithm that determines whether an email is spam or not would be considered a classification model.
On the other hand, regression models predict continuous numeric values as opposed to labels. The output, being continuous, represents a quantity or measurement rather than a distinct category or group. An easy example of a regression model is one that’s used to predict the price of a commodity over time.
Major classification models
There are many popular ML models for classification. Because each one works a bit differently, they’re normally chosen based on the dataset you’re working with. Below are three of the most common:
- Naive Bayes models classify what an object is or is not based on predetermined categorical possibilities. Based on Bayes’ theorem of conditional probability, they assume all features within an input set are independent of each other. This often isn’t true in real-world scenarios—hence the name “naive.”
- k-Nearest Neighbor (KNN) models categorize data based on its proximity to a significant number of similar data points. This works by finding the most frequent or average characteristics among the objects in a data set.
- Support vector machines are models that facilitate complex classification based on degrees of polarity.
Major regression models
The range of regression models is just as diverse and varied as that of classification. Here are three of the most popular:
- Decision trees predict values based on a “branch” diagram of linked decisions. Functionally, this splits the dataset into smaller parts, plotting the value of any data points that connect to the problem statement. ML experts prefer this model in cases where there isn’t enough change in the dataset.
- Linear regression models predict values based on the linear relationship between different values. This algorithm is often used to describe a continuous response variable as a function of one or more predictor variables. It’s also one of the first models data scientists try when working with a new dataset because it’s especially easy to interpret.
- Ridge regression is used to estimate values when two or more independent variables in a dataset have a high correlation with one another. It’s extremely similar to linear regression, the key difference being that it introduces a small amount of bias (the inability to capture a sample’s true relationship). This, in turn, reduces variance—the degree to which a model’s accuracy changes with respect to different datasets.
It’s important to note that many machine learning models can be used for either classification or regression, such as decision trees and KNN algorithms. Neural networks—a subset of ML that mimics the human brain—can also function as either category but operate in a more complex fashion. This is especially true if they have more than two node layers, thus classifying them as deep learning models.
Major ML model use cases
The myriad benefits of machine learning have many organizations eager to leverage ML capabilities to their advantage. As reported by Forrester, 53% of enterprises plan to improve business efficiency using ML, and 75% have already been developing their models for several years.
From faster decision-making and data analysis to greater productivity and personalization, ML’s potential is practically limitless. However, the best way to illustrate the value of machine learning models is through specific use cases. These applications can be found across numerous industries:
Banking and finance
Financial institutions have ample reason to invest in machine learning’s rapid emergence. Given its predictive functionality and knack for continuous improvement, ML’s applications are profound throughout the sector. For example:
- Fraud detection. ML models allow banks and other financial providers to identify anomalous transactions that fall outside normal parameters. This could include abnormal purchase amounts or transactions that happen in foreign countries. By detecting potential fraud, firms can alert customers of unusual activity for quick and effective mitigation.
- Price forecasting. Machine learning can be a powerful tool for investment banks in predicting price fluctuations. In fact, a 2023 World Bank study concluded that ML techniques have “tended to yield better forecasts” than traditional benchmarks when it comes to commodities like oil and metal.
- Credit scoring. Financial institutions are increasingly entrusting ML models to evaluate the risk associated with lending to individuals or entities. Rather than score these parties by hand, algorithms automate decisions and process large amounts of data in real time, thereby accelerating approvals and possibly reducing organizational risk.
Retail and customer service
Artificial intelligence, and machine learning especially, have enormous implications for customer satisfaction. Below are some of the most impactful applications:
- Sentiment analysis. According to McKinsey, ML can help contact center agents understand the emotion behind a caller’s words. Deep learning analysis of audio assesses the customer’s tone, which can also help interactive voice response systems know when to reroute callers to a human operator.
- Personalization. ML-powered chatbots can retain and access stored information about individual customers. This allows the bot to personalize the customer experience, improving satisfaction by not asking the user to repeat information. Moreover, because it learns over time, it gradually accelerates time to resolution.
- Inventory management. Businesses can track inventory and forecast demand in real time, using ML to avoid stockouts or overstocking.
Manufacturing
Machine learning is especially useful throughout the supply chain. Manufacturers are particularly poised to harness its capabilities, which include:
- Predictive maintenance. Rather than wait until equipment breaks down, companies can avoid downtime by proactively forecasting upkeep.
- Supply chain management. Much like retail, manufacturers can avoid costly shortages by knowing in real time whether they’ll soon need to order new parts and materials.
- Quality control. Feeding process and product data to an ML model allows the algorithm to eventually recognize defects. Ideally, this happens in real time, allowing engineers to intervene and avoid having to scrap expensive parts.
How do you build and train machine learning models?
Building a successful model takes time. But, with the following best practices, you can ease the development process and deploy a fully functioning ML system:
- Code for the algorithm. Either write from scratch or choose an algorithm from one of the many ML libraries (many of which are open source). Concurrently, gather various datasets to use for training, testing, and validation.
- Define hyperparameters. This will govern how the model learns, including learning rate, the number of hidden layers in a neural network, or the depth of a decision tree.
- Input the training data. How you do this will determine whether your learning is supervised, semi-supervised, or unsupervised.
- Test the model. This is how you see if it generally functions. Then, validate it to evaluate quality and accuracy using a set of testing data as the basis for your assessment.
- Tweak hyperparameters and parameters. The latter of the two will have been discovered during the training process and will determine how the model performs. Test and evaluate additional times as needed.
- Prepare for deployment. If the model functions at or above the baseline for acceptable performance, it can probably be deployed, but it's generally preferred to wait until models exceed what is effectively a bare minimum for accuracy and quality.
One potential exception to this process: ML models created using reinforcement learning. In this case, the model “learns by doing” in a deployment environment as much as (if not more than) it would during isolated training.
How to optimize ML model development with Teradata
In most cases, the ML model development process is far from easy. Many enterprises run into data-related challenges along the way, complicating and possibly derailing the effort. Consider these numbers, as reported by Forrester:
- 73% of organizations find transparency, traceability, and explainability of dataflows challenging
- 57% believe silos between data scientists and practitioners inhibit ML deployments
- 47% struggle to drive actionable insights from their current ML strategy
- 41% say internal data silos are their biggest ML challenge
- 36% say their biggest challenge is working with large, diverse, and messy datasets
Clearly, the volume and complexity of data being generated is a daunting obstacle. Fortunately, the capabilities of Teradata VantageCloud make it ideal as a data management platform for developing and deploying ML models, particularly with its AI/ML-based ClearScape Analytics™ engine. In combination, you can boost data preparation time by up to 70%, train models at the scale that's best for your organization, and accelerate deployment.
Connect with us to learn more about Teradata VantageCloud and how we can help your organization tap into the power of machine learning and trusted AI.