Companies in every industry must effectively process large amounts of data to find the valuable information they need to operate. Whether a company is processing customer, financial, or competitor data, they need machine learning (ML) to mine for gems. Machine learning allows companies to analyze and turn the collected data into useful (and actionable) information. Many companies in various industries take advantage of machine learning to collect and analyze data and make predictions. However, as incredible a tool as ML is, a new type of ML is emerging on the scene: adaptive machine learning (adaptive ML).
What is Adaptive Machine Learning?
Adaptive machine learning builds on traditional machine learning to create a more advanced solution to real-time environments with variable data. As its name suggests, adaptive machine learning can adapt to rapidly changing data sets, making it more applicable to real-world situations. Adaptive machine learning is more robust and efficient than traditional ML and incorporates agility, increased accuracy, and greater sustainability. Adaptive ML can process large quantities of data while its operational conditions can be more easily adjusted as the needs of the company using it changes. Adaptive ML can quickly adapt to new information and provide real-time insight into how that data can be used.
Adaptive ML vs Traditional ML
Traditional ML beginnings date back to the 1950s, but it wasn’t until the ’90s that machine learning began to grow rapidly and become the industry-shaping force it is today. Traditional ML changed industries from the inside out with its ability to transform vast quantities of data into usable, and therefore valuable, information.
Traditional ML uses two main pipelines for data collection or training and data analysis and prediction. It’s a two-way road—one way for information to get in and one way to get out. This makes it highly applicable for horizontal scalability but less for changeable industries, such as the financial sector, where large amounts of data change rapidly.
Adaptive ML uses a single-channel structure, which means it collects new information in real time. It is also able to use different techniques for gathering data, as well as different ways of grouping and analyzing that data. And it can learn from the data while it’s being collected, giving it more responsiveness than traditional ML. In short, traditional ML is static, while adaptive ML is precisely that: adaptive.
Compared to traditional ML, adaptive ML can change according to new data and provide rapid insights. This model overcomes the shortcomings of traditional ML, which rely on manual training for new data. In industries where new data is constantly arising, adaptive ML is ideal because it functions best while receiving a constant stream of new information. Another advantage is that an adaptive ML model is less likely to become obsolete. This is a risk with traditional ML, making costly AI infrastructure risky. But, with adaptive ML, the costs offer a high ROI as companies take advantage of the precision and high performance of adaptive ML.
Uses of Adaptive ML
Dynamic industries with rapid changes in data stand to benefit significantly from adaptive ML. Some of these industries include:
1. The Financial Industry
Adaptive ML offers many benefits to the financial sector, including better fraud detection and high-frequency trading automation.
2. The Healthcare Industry
Patient monitoring is more precise with adaptive ML, allowing the appropriate staff to respond to a patient promptly.
3. The Marketing Industry
Customer behavior can be more accurately analyzed, leading to tremendous success with personalized offers.
4. The E-Commerce Industry
Millions of people shop online, and companies that can most accurately analyze buying behavior can more successfully cross-sell.
Benefits of Adaptive ML
1. More efficient
Companies can use adaptive ML to find relevant solutions faster. Since adaptive ML works on a single channel, it can create faster solutions with the newest data at hand.
2. More pertinent data
Adaptive ML uses more pertinent data than used with traditional ML. Rather than using old, static data, adaptive ML is constantly taking in new, more relevant data. It can change its behavior based on this new data.
3. Able to learn from the past
The longer an adaptive ML program runs, the more it learns. It can lower the chances of repeating a mistake by remembering that it happened and adjusting itself accordingly. The more information fed to an adaptive ML, the “smarter” and more accurate it becomes.
Adaptive ML with Encora
Companies that use a skilled team of software engineers from Encora to tap into the power of adaptive ML will significantly increase their competitive advantage. They will have more access to industry-relevant data and the ability to turn that data into useful, actionable information quickly. Companies can gain valuable agility within their industry using adaptive ML. Reach out to Encora today for more information.