What is Adaptive ML (Online Machine Learning)?
Virtually every business relies on data to survive. Sales data gives you insights into your business’s performance. Customer data tells you more about your target audience and their behaviors. Marketing data helps you understand how to improve brand awareness and expand your reach. Competitor data, transaction data, financial data, employee data – the list goes on. Without it all, you wouldn’t be able to make informed decisions that could take your company forward.
But it’s one thing collecting data, and quite another analyzing it and transforming it into valuable, actionable information. And that’s precisely where machine learning comes in.
An AI/ML infrastructure brings an abundance of benefits to any industry, but only if you use the correct machine learning model for your needs. Most commonly, businesses rely on traditional ML to handle data collection, analysis, and predictions, but adaptive ML has started taking the spotlight.
Let’s dive deeper into the world of machine learning and see what makes adaptive ML so much more powerful than traditional ML.
Why Traditional Machine Learning Doesn’t Cut It
While it’s only recently that machine learning as a concept started drawing attention, the field has quite a lengthy history. ML’s beginnings date back to the early 1950s, although we had to wait about 40 years more for the significant breakthroughs in the field that made ML so accessible today. From the ‘90s onwards, machine learning started to thrive and reshape industries from the core, especially so with the introduction of the traditional or batch machine learning model.
Traditional ML involves only two primary pipelines – one for training (responsible for data collection) and the other for making predictions (responsible for data analysis). Before an ML model is sent out into the world, it goes through a round of training during which its parameters for data collection and analysis are set. To train the model, developers use batch learning techniques where the model receives the entire data set at once to generate the best predictions.
Although you’ll commonly see AI products being marketed as “always learning, always evolving,” that’s typically not the case if the product relies on this traditional batch-based ML model. Traditional machine learning is static; it depends on parameters that don’t change, making it great for horizontal scalability but causing problems in dynamic industries where data changes quickly.
Since there are only two pipelines for data collection and analysis, and since traditional ML models rely on past data to generate new predictions, you can never have true, real-time insights critical in industries such as e-commerce, for example, where trends are constantly changing.
Overcoming the traditional ML flaws
To overcome the inherent flaws of traditional ML models, developers typically commit to one of two approaches:
- Manually training for new data
- Scheduling automatic training for new data
Manual training for new data is a time-consuming process that doesn’t deliver much better results, so most developers opt for the second option.
However, it’s still not ideal. Even if automatic training and deployment are scheduled daily, your ML model would still be using stale data to make predictions, perhaps just an hour old, but still old.
To perform a successful digital transformation and get as close to real-time predictions and real-time learning as possible, you need a model that relies on adaptive ML.
The Adaptive ML/ Online Machine Learning Advantage
Adaptive machine learning is a more advanced solution that takes real-time data collection and analysis seriously. As its name would suggest, it easily adapts to new information and provides insights almost instantaneously.
Instead of having a two-channel or two-pipeline approach like traditional ML, adaptive ML relies on a single channel. As opposed to batch learning, adaptive learning collects and analyzes data in sequential order, not all at once. This enables adaptive ML models to monitor and learn from the changes in both input and output values; it allows the model to adapt its data collection, grouping, and analysis methods based on new information.
So, as long as there’s a stream of information coming in, adaptive machine learning models will continue updating and changing to provide you with the best predictors for future data. You’ll receive high performance and the utmost precision. Perhaps more importantly, you’ll get a system that runs in real-time that doesn’t run the risk of getting outdated or obsolete, making the cost of running AI infrastructure well worth it.
The Pros and Cons of Adaptive Machine Learning
Adaptive machine learning brings several unique benefits that could be useful across industries. Its main pros include:
The adaptive ML model’s robustness and efficiency lie in its ability to handle large quantities of data with ease. Its agility lies in its capacity to adapt to changes and adjust its operational conditions to meet your current needs. Thanks to its single-channel approach and real-time data collection and analysis capabilities, adaptive ML models can provide accurate insights and precise predictions in a matter of seconds.
All these benefits combined make for a sustainable system that makes ML models easily scalable, capable of handling massive datasets in real-time.
However, there’s a catch. Adaptive machine learning models are more prone to catastrophic interference – artificial neural networks tend to forget old information as they acquire new information. Fortunately, this can be easily avoided with incremental learning.
Applications of Adaptive Machine Learning
Considering its agility, precision, and real-time capabilities, adaptive ML can be valuable across industries and niches:
- Financial sector – for detecting fraudulent transactions, predicting trends, automating trading systems;
- Manufacturing sector – for predicting system malfunctions and solving supply chain issues;
- Healthcare – for providing quick, accurate, and affordable diagnoses and alerting of problems before they arise;
- Marketing – for perfecting marketing campaigns based on real-time insights;
- E-commerce – for predicting trends and devising effective strategies;
As more and more industries start relying on adaptive ML technology, it will become evident just how powerful these models can be.
Adaptive or online machine learning is the most sophisticated technology to hit the market. It has extensive capabilities and is set on a path to changing how we collect, analyze, and process data. Adaptive ML constantly learns from changes in both input and output values, making it far more dynamic than traditional ML.