At TAZI, we have been providing our customers with robust and Adaptive Machine Learning (ML) solutions since 2016. Continuous/Adaptive Learning has become a hot topic due to the frequency and magnitude of change in business environments, speed of data and the quantity and complexity of Machine Learning models in use. Our product and intellectual property has been aligned with market needs since day one and now, we have a new patent that supports this alignment even further: “Continuously learning, stable and robust online machine learning system”, US Patent No 11,315,030.

What is Continuous/Adaptive Learning?

Gartner defines Adaptive Machine Learning as the capability of frequently retraining ML models when online in their runtime environment, rather than only training ML models when offline in their development environment. This capability allows ML applications to adapt more quickly to changing or new real-world circumstances that were not foreseen or available during development.

With Batch Learning, you train your models with a set of data from the past allowing you to have an ML model that performs well on that data. But as soon as a change happens in the world, such as changes in the economy, in your competitors’ strategy, your new product release or a new regulation, the patterns in your data start to change and your ML model becomes outdated. You have to work with outdated models until retrained models become production-ready. This not only affects your business flow but also requires time and effort from valuable and scarce IT and data science teams as well as the use of computational resources such as cloud storage and processing. 

Continuous/Adaptive Learning on the other hand gives you the power of using ML models that are robust and adaptable. Once you integrate your data source and Adaptive ML system, you can continuously benefit from it.

Why is Continuous/Adaptive Learning better?

After ensuring you have an accurate and working ML system, maintaining those qualities comes as the next challenge.

Continuous learning:

  • Ensures algorithms that adapt to changing real-life dynamics
  • Guarantees more accurate models with less effort
  • Reduces computational cost of modeling

The graph below shows the change in accuracy of two separate models: one trained using batch learning (red)  and one trained using continuous learning (blue). The continuously trained model becomes superior to the batch trained model as new patterns in data emerges.

‍ See our white paper “Why Should You Choose Continuous Machine Learning over Batch: Double the Efficiency of Call Center for Bank Marketing” to discover more.

How are we strong in Adaptive ML?

TAZI has 3 other pending US patents for its Explainable AI and Human in the Loop technologies, which help Continuous Learning to succeed by including business users in the whole Machine Learning lifecycle. In addition to being recognized as a Gartner Cool Vendor in AI Core Technologies (2019), TAZI has also been cited in seven other Gartner reports as a provider of Responsible AI, Explainable AI and as a Citizen Data Science Machine Learning Platform.  We have more than 60 years of relevant experience in ML and software development. We have been actively serving our active customers with our Adaptive ML technology since 2016 and have been developing our no-code platform and solutions to serve our customers’ needs. ‍

Impact on Businesses:

Since data behavior keeps changing, businesses also need machine learning systems that can include new features or can function when some old patterns disappear. Some actions, such as CRM, sales, marketing, and pricing decisions are planned based on the machine learning outputs. With Adaptive ML, models remain accurate longer and suffer less from the changes in the market. 

At TAZI, we make sure your business outcomes are aligned with the current state of the world. We are aware of the constant change in your business environment and our solutions are ready to adapt.

For further information, visit our Continuous Learning™ Page

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