Machine Learning Modeling
Today’s data-driven organizations use machine learning (ML) modeling to identify patterns and relationships within data and make predictions and decisions with unparalleled accuracy. Once trained, ML models can be deployed to analyze new data. In this article, we outline the steps involved in machine learning modeling, explore types of ML models, and share how this technology is being implemented in modern businesses.
What is Machine Learning Modeling?
Machine learning modeling is the process of creating and training an algorithm (or model) to make predictions or decisions based on patterns and relationships within a data set.
The process of creating an ML model varies depending on the application, but it typically includes the following steps:
Data collection—The first step is to gather relevant data to train and evaluate the model. Strategic data selection is vital to the success of the model.
Data preprocessing—The data must be cleaned and prepared, including removing duplicates, resolving missing values, normalizing or scaling features, and splitting the data into training and testing sets.
Feature engineering—Feature engineering uses domain knowledge to transform data into features (variables) that ML algorithms can understand to improve the model's performance.
Model selection here are many different types of ML models. Choosing a model depends on the type of problem to be solved (classification, regression, clustering, etc.), available data, and various other factors.
Model training—The model is trained on the collected and prepared data by feeding it input features and corresponding target variables.
Model evaluation—It’s important to assess the model's performance using evaluation metrics appropriate to the problem. This step ensures that the model will effectively generalize to new data. Machine learning modeling is an iterative process, so if the model isn’t performing as expected, adjustments are critical.
Model optimization—Optimization involves improving the model's performance by tuning it’s hyperparameters—configurations set before training.
Deployment—Once a model is built, tested, and optimized, it’s ready to be deployed to make predictions or take action on new data.
Operations—Models in production need to be governed and monitored to ensure results and predictions can be trusted.
Primary Types of Machine Learning Models
Different kinds of machine learning modeling techniques are suited to address different types of problems. Selecting the best type of model is the key to using machine learning effectively and efficiently.
In this technique, labeled data sets train the model to produce a set of desired outputs. Since some of the input data is already tagged with the correct output, the training data acts as a supervisor, providing the model with the instruction required to correctly predict the output. Real-world applications of supervised learning algorithms include spam filtering, image recognition, and fraud detection.
As the name suggests, models are not supervised using a training data set with unsupervised learning. Instead, this ML modeling technique trains the model on unlabeled data without any specific desired output. These models are designed to discover patterns, structures, or relationships within the data, such as grouping objects together with common characteristics. Ecommerce recommendation engines and customer segmentation are two common applications of unsupervised learning models.
A hybrid approach combines elements of supervised and unsupervised learning. With semi-supervised learning, the model is trained on a data set that contains a small amount of labeled data and a large amount of unlabeled data. The labeled examples provide a level of supervision, while the unlabeled examples help train the model to discover hidden patterns or improve the model's performance. Semi-supervised learning plays an important role in web content classification and is used by internet search engines to label and rank search results.
Reinforcement learning involves requiring the algorithm to train itself through a series of trial-and-error experiments. This modeling technique does not rely on training data. Instead, the algorithm learns by interacting with its environment, receiving feedback from the environment based on its actions. Common examples of reinforcement learning include autonomous driving systems and the segmentation of medical images such as CT scans.
Deep learning is a type of machine learning that uses multiple layers of neural networks to simulate the way the human brain processes information. Deep learning uses these neural networks to ingest vast amounts of data from multiple data sources and learn without the aid of human intervention. Many artificial intelligence (AI) applications and services are driven by deep learning technology, including voice-enabled television remotes, facial recognition programs, and virtual assistants.
ML Modeling for Data Science
Machine learning modeling is an indispensable tool for extracting insights, making predictions, and automating actions on data. Here are just a few ways machine learning is being used in today’s organizations.
Most predictive analytics models include a machine learning algorithm. Predictive models analyze historical data to predict future outcomes. They are valuable for many business tasks, including sales forecasting, demand planning, risk assessment, and fraud detection.
Customer segmentation and personalization
Machine learning allows businesses to more effectively segment their customer base and better understand the behavioral patterns of their customers. By analyzing customer data such as demographics, purchase history, social media activity, and other types of online behavior, machine learning models can help teams identify customer segments. With this information, companies can create highly targeted marketing campaigns and recommendation systems and provide highly personalized experiences.
Machine learning models can be trained to detect many types of aberrations in data, including abnormal network activity and suspicious patterns in user behavior, making them essential to many cybersecurity initiatives. ML-enabled anomaly detection also plays a vital role in manufacturing, including predictive maintenance and quality control.
Optimization and resource allocation
Machine learning can be used to improve the efficiency of business processes, helping businesses better control costs and improve productivity. By analyzing large data sets and identifying difficult-to-detect relationships, ML models can be used in supply chain management, logistics, resource allocation, and scheduling.
Natural Language Processing (NLP) and sentiment analysis
NLP is a subset of machine learning that enables computers to understand and analyze human language. By providing computers with a way to extract meaning from written and verbal communication, businesses can uncover insights from customer feedback, social media posts, online reviews, help tickets, conversations with customer support agents, and more. Sentiment analysis uses machine learning and other technologies to understand the emotional context of communication. Using this analytical technique, businesses can gain a better understanding of how their customers feel about their products or services, providing them with the information needed to enhance customer satisfaction and improve their offerings.
Move Your ML Initiatives Forward with Snowflake
Snowflake offers robust support for machine learning and AI data science applications. Governed access to data and performance speed is a key factor in supporting robust machine learning models. With Snowflake’s elastic and performant multi-cluster compute architecture, you can easily scale processing to any amount of data or number of users. Snowflake can also effectively help you scale and automate data preparation responsibilities, reducing data-related burdens from machine learning modeling. Snowflake’s Snowpark enables you to use Python to transform data into ML-powered insights with built-in integrations to Python’s rich, open-source ecosystem to streamline workflows.