Machine learning is a subfield of artificial intelligence and a method of data analysis that automates analytical model building. Using a variety of algorithms that are trained on big data, machine learning creates self-learning models that can then be used to make accurate predictions based on historical data.
It is a vast and rapidly growing field, with various industries, from linguistics (with its large language models) to finance (which can use machine learning to forecast trends), all interested in learning and successfully applying it to their various sectors.
For this IT Hub guide, we’ll discuss what machine learning is, its possible uses in remote monitoring and management (RMM), and how MSP leaders can effectively optimize it in their organizations.
How does machine learning work?
UC Berkeley has divided the process of machine learning into three parts:
- Decision-making:During this step, artificial intelligence generates a rough pattern or framework of a specific question or action based on all the data it has gathered.
- Trial and error: AI then measures how good or viable the prediction is by comparing it with known examples (if they are available). Was the decision-making process accurate? If it wasn’t, how far of a “miss” was it?
- Optimization and updating: This is where AI analyzes and evaluates its prediction and updates its prediction or decision, so it won’t repeat its mistake (or “miss”) in the future.
Keep in mind that AI models are capable of performing all three steps multiple times with great accuracy and reliability in seconds.
In its most simplistic definition, machine learning attempts to identify patterns and make accurate decisions (and predictions) with minimal human intervention while still sounding “natural” or human-like. After all, it wouldn’t matter how accurately a machine can answer a question if the human responding does not understand what is being said.
Ideally, machine learning augments current technologies to improve operational efficiency and reduce the risk of human error.
Types of machine learning
According to Nvidia, there are four different machine learning models.
- Supervised learning: In this learning model, AI models are trained next to a full set of labeled data. “Labeled” data means that every example or data point in the training data set is tagged with a specific answer. For example, a labeled dataset of endpoints would specifically classify which photos were of laptops, mobile phones, or IoT devices.
- Unsupervised learning: Conversely, unsupervised learning is a machine learning model that does not provide any explicit instructions on any data set. Instead, AI models are given examples without a specific “correct” answer. Machines then attempt to find structure in the data, whether that’s through clustering, association or finding an anomaly in the sample.
- Semi-supervised learning: This is the happy medium between supervised and unsupervised learning. Here, a training data set includes both labeled and unlabeled data. This is useful when you do not have a clean set of examples with linear descriptions but still need structured and relevant associations.
- Reinforcement learning: Similar to video games, reinforcement training encourages AI models to find the best way to accomplish a goal or improve performance for a task to receive a reward. The main goal in this type of learning model is for the AI model to predict the next step to earn the biggest final reward.
Finally, there is a new type of learning model called deep learning, which teaches AI to process data similar to and inspired by the human brain. It is a type of machine learning that uses artificial neural networks to recognize complex patterns in pictures, texts, sounds, and other non-linear data to produce accurate predictions.
Common machine learning algorithms
- Neural networks: Neural networks mimic the human brain and can recognize patterns in natural language translation, image and speech recognition, and more.
- Linear regression: This is a statistical model that is used to predict numerical values based on a linear relationship between different values.
- Logistic regression: This is a sub-type of supervised learning that makes predictions for categorical response variables, such as “yes” or “no” answers.
- Clustering: This is part of unsupervised learning and covers identifying patterns in data points that can be grouped.
- Decision trees: These are used to predict numerical values and classify them into categories. These trees are often visually represented as a tree, with each branching sequence linked to specific decisions.
- Random forests: This machine learning algorithm predicts a value or category by combining multiple decision trees and then making the most mathematically probable decision.
Advantages and disadvantages of machine learning algorithms
The most obvious advantage of machine learning is that IT enterprises can learn new and more efficient ways to handle and process their data. This adds to their overall data governance plan.
Because these AI models can self-learn and identify patterns and trends in huge volumes, businesses can continuously improve and enhance their services—leading to a much more personalized customer experience journey.
That said, machine learning requires regular and extensive training in accurate and unbiased datasets. This involves strictly following the “garbage in/ garbage out” (GIGO) strategy, an IT expression that evaluates data integrity (or the quality of output) by the reliability and accuracy of the initial data inputted within the dataset.
Machine learning use cases in RMM
Machine learning is expected to radically transform the way MSPs, MSSPs, and IT enterprises remotely monitor and manage their endpoints for improved IT efficiency. Some use cases to consider are:
Predictive and proactive maintenance
Recall that machine learning, when trained properly, can accurately predict trends based on historical data and past patterns. This helps in improved proactive IT management, allowing you to predict probabilities of component failure or outdated hardware or software based on past events.
Threat detection
Machine learning can contribute to a stronger cybersecurity strategy. Compared to traditional security systems, machine learning can detect unusual patterns within your IT network that may signal an impending or recent cyberattack.
Patch management
Machine learning can significantly enhance patch management by automating and optimizing the process. By analyzing historical data on patch installations, security vulnerabilities, and system performance, machine learning algorithms can predict potential risks and prioritize patches accordingly.
Helpdesk and ticketing
By analyzing historical data on customer inquiries, machine learning algorithms can identify common issues, provide automated responses, and even suggest solutions. This reduces response times, improves customer satisfaction, and frees up human agents to handle more complex problems.
Managing complexities in IT management and resource allocation
One of the top four IT challenges in 2024 is managing the ever-expanding IT infrastructure, especially as your organization grows. Machine learning simplifies decision-making by analyzing trends and usage patterns and predicting future demand. This helps optimize the allocation of resources, such as power or bandwidth.
And this is just what can be discerned this year. As technologies evolve, so too will machine learning and its possible use cases for RMM. It is likely that the best RMM tools will further maximize machine learning in the future in their various features and functionalities to deliver better service to its end-users.
NinjaOne embraces machine learning
NinjaOne is the trusted endpoint management company for 17,000+ customers worldwide. Its robust, all-in-one RMM solution allows you to efficiently monitor and manage your Windows, macOS, and Linux endpoints in a single pane of glass. In addition, the company takes special care and pride in incorporating the latest technologies and innovations to drive radical efficiency to MSPs of all sizes from day one.
If you’re ready, request a free quote, sign up for a 14-day free trial, or watch a demo.
