What Are the Benefits of Online ML vs.
Traditional Batch?
There is a field in machine learning called online learning, and it refers to an array of machine learning practices and techniques that learn from a selected source of data provided over time. In online learning methodology, each new batch of data updates the existing machine learning models continuously.
In other words, online machine learning is a machine learning method of gathering, analyzing, and interpreting data on the go. It is an effective way to analyze large chunks of data and treat pieces of information as a running stream that grows more knowledgeable the more the stream flows.
On the other hand, traditional offline learning, or batch learning, sees data as something static and is based on the assumption that all necessary data is available at the time of learning. When compared, an online ML model allows the user to create an ever-growing and improving data model that can be gradually changed with each new data set.
In the world of big data, online ML has many benefits that can help enterprises gather and analyze huge chunks of raw data in a timely, cost-efficient manner.
The Benefits of Online ML
To better understand how useful online ML is, we’ll look at the latest centralized and decentralized ML messaging, or better yet, interactive ad recommendation systems. These systems use online ML to provide internet users with browsing recommendations based on their past interactions.
They gather user data to build user preference ML-based models by tracking their browsing and purchasing activities. Since user behavior changes all the time, new recommendations must include the latest preferences.
Because of that, online ML is a logical choice for acquiring the most accurate data. In the era of big data, online learning has never been more important, simply because of the three things it can do to characterize big data:
- Volume – refers to the amount of data, both in the number of variables per data point and the number of data points.
- Velocity – velocity is the speed at which a user accumulates/gathers data. In the data-driven world, modern data gathering methods allow users to accumulate vast amounts of data at amazing speed.
- Variety – there are many forms of data on the web – text, audio, image, video, etc. The problem is that these forms tend to change over time.
Since big data constantly moves and changes, businesses need a new learning style that allows them to cope with the constant change and the challenges of big data. That’s where online machine learning comes into play.
The Advantages of Online ML
Since online ML helps you deal with big data challenges in terms of volume, velocity, and variety, let’s delve deeper into the details.
1. Volume: Online ML offers the advantage to build locally
One of the biggest advantages of online ML is the ability to analyze large quantities of data. Online ML doesn’t require a considerable amount of memory or data storage that a traditional batch does. Instead, it requires smaller data storage.
Online ML works by providing constant updates to its learning model. It only needs new data points to function. Since it doesn’t require you to store data in memory, it allows the user to build local databases with a much smaller amount of data storage, thus reducing compute cost.
If your business is operating with limited memory applications and high throughput, you can reap advantages from online ML’s ability to create a local model from streaming data.
2. Velocity: The ability to continually upgrade models
Another great advantage of online ML is the ability to continually upgrade models based on the latest data points from a dataset. Online ML is perfect for businesses that need fast learning model updates.
Speed is essential in online learning. Since an online learning model processes small amounts of data at a time, such a method allows users to handle high levels of data-computing complexity with ease. It saves time and is highly cost-efficient.
In comparison, traditional batch learning can be quite costly due to the need to constantly re-train offline models to provide them with the necessary updates. Online ML allows you to scale your learning needs and big data processing capabilities. If you have to work on deadlines and a budget, online ML is your best option to deliver the best results.
3. Variety: Online ML allows you to adapt to changes in big data
Online ML methods work by including a forgetting factor in the equation. This factor allows
the user to determine the pace at which their online learning model forgets already used data. It achieves this by gradually discounting certain data sets based on their current importance.
The end result is fantastic – the model automatically updates itself by adapting to changes in the dataset. These changes can be anything from customer behavior shifts to industry and market changes. However, what really makes online ML models so interesting is their ability to change with the changes in data.
That’s how online ML helps businesses identify and adapt to sudden changes in the market. Even though online ML offers all these benefits and advantages, it isn’t without its challenges. Compared to traditional batch learning, online models are a much better option for change detections, frequent updates, and the need to analyze large amounts of data in a timely and cost-efficient manner.
Conclusion
If we take all of the above into consideration, it’s safe to conclude that online ML has quickly become one of the most valuable tools/methods for coping with constant advances in the fields of big data, IoT, machine learning, natural language processing, and artificial intelligence.
Its capabilities to process huge chunks of data at high speeds allow a wide range of businesses to adapt to big data changes quickly. If you need a scalable learning model to gather valuable, actionable, and competitive intel from big data, online ML is the way to go.