Machine Learning (ML) Platforms

Machine Learning (ML) is a subtype of Artificial Intelligence (AI) technology centered on creating computerized systems that mimic human learning capabilities. Machine learning works by creating and training self-learning algorithms to identify patterns and data relationships within a large data set. ML analyzes this historical data and uses what it has learned to derive insights that can be used to make predictions, classify and organize data, automate processes, or create new content at scale.

The difference between Artificial Intelligence (AI) and machine learning is that AI refers to the broader technology that can simulate human thinking and learning capabilities, and ML is a specific type of AI technology that relies on trained algorithms to create self-learning models that can be adapted and scaled to perform and automate tasks.

Machine learning allows machines to learn and improve over time, making it an essential technology for improving software programs. Organizations leverage ML to support a variety of important goals and initiatives, including data analysis, customer service, finance, and cybersecurity.

Key Capabilities of Machine Learning (ML)

The key capabilities of machine learning include:

• Scalable Automation: ML automates the process of identifying meaningful, relevant patterns in large datasets. This reduces the need for human intervention and makes it possible to create and scale automation across various business applications.
• Training: Machine learning algorithms train machines how to learn autonomously and complete tasks by feeding large amounts of data, examples, and instructions into the system. This data is analyzed and labeled to identify patterns, and training is an iterative process where ML uses labeled data to close the gap between predictions and actual outcomes.
• Supervised, Unsupervised, and Reinforcement Learning: Supervised learning utilizes labeled datasets to train models to map inputs to outputs based on that data, while unsupervised learning finds patterns in unlabeled data without predefined input and output objectives. Reinforcement learning trains agents on decision-making skills or policies by providing a reward or penalty based on their interactions in various situations.
• Predictive Modeling: ML algorithms can create models using historical and current data to predict future actions or events. Predictive modeling can form the basis for predictive analytics tools, which can be used to predict sales patterns, forecast financials, and provide insight into buying patterns and behaviors.
• Pattern Identification and Analysis: ML algorithms can identify and analyze patterns in data to find correlations, classify information, and provide meaningful data analysis.
• Deep Learning: Deep learning is a type of machine learning that leverages Deep Neural Networks (DNN), which are modeled after the human brain. Deep learning software leverages at least three neural networks (and oftentimes, many more) that are trained on extensive, large datasets to recognize patterns, determine data relationships, and make predictions and decisions autonomously.
• Generative AI: Generative AI is a subcategory of ML that focuses on creating new data or content that mirrors the trained dataset. Generative AI tools don’t rely on predictive modeling but rather learn from data patterns to create new and unique content based on input instructions or parameters.