Every day that a business isn’t working to differentiate itself from its competitors is a day it’s going backward in its industry. As sophisticated IT infrastructures become the minimum standard and with data-driven decision-making fueling innovation, businesses must be proactive about finding the technology that gives them a competitive edge.

One of the big topics right now when it comes to gaining this edge is integrating Reactive Machine Learning, which to say the least, can be a game-changer for the businesses who utilize it effectively.

What is a Reactive Machine Learning System?

Instead of telling you all the things a reactive system is, it’s better to tell you what it isn’t:

Reactive is the opposite of batch learning where a system takes one big dataset and then uses that to generate its insights and make predictions. Instead, it can process real-time data and respond immediately.

Reactive learning is not a deliberative agent which focuses on analysis and reasoning before taking action. A reactive system instead uses predetermined rules or patterns that are meant to make the system act quickly.
Reactive systems are not useful in complex decision-making processes such as long-term forecasting. Instead, something like fraud detection would benefit from a pre-determined system protocol.

This covers the basics. Machine Learning models are made from algorithms that analyze data, recognize patterns and outliers in that data, and then make predictions or decisions.

Where Does it Fit in a Business?

The evolution of the internet has exceeded comprehension looking back 20 years. With this, user standards have risen as well which has made integrating machine learning models essential for businesses to meet the standards of their industry.

3 Ways a Business Might Utilize Reactive ML:

Automating processes: Think about a chemical testing laboratory with a vast amount of highly sensitive data to be managed. Reactive ML can be used to prevent errors by automating the analysis aspect. As a result, the laboratory cuts down its processing time and increases the efficiency of instrumentation.
Energy consumption: Take a utility provider, for example. Reactive ML can optimize how much energy is consumed using real-time data to determine the appropriate adjustment. In addition to this, it can implement demand response programs by taking past data and identifying patterns to make recommendations on energy usage.
Personalizing recommended content: This is what streaming services like Netflix or Disney+ use in the “suggested” section, or social media platforms for the type of content someone is fed. In this case, ML algorithms will be used to analyze user data and recognize patterns that determine what they’re fed.

How to Build It

There’s a lot that goes into building a reactive ML system and the specifics will always vary just as with the construction of any complex IT platform. What businesses must do to carry it out effectively can be understood with these basics principles:

Gather data: Collect relevant data that you want to train and validate the reactive ML algorithms on. Make sure that the data is accurate and diverse, and that it fits in the problems domain. Then, clean and preprocess that data to remove noise and handle missing values.
Train the algorithms: Choose the ML algorithms that you think best fit the problem at hand. Train the algorithms using your gathered data, adjust hyperparameters and then evaluate performance. Consider using techniques like cross-validation to ensure the system is well-rounded and that you’ll avoid overfitting.
Integrate the system: Once you’ve developed the necessary software infrastructure to integrate the reactive ML system, connect components. This may involve building pipelines, creating ingestion and processing mechanisms, and implementing decision-making modules based on the trained data we mentioned previously.
Test and evaluate: This is an essential piece of this puzzle. Use the appropriate evaluation metrics to assess the accuracy and effectiveness of the system. Then fine-tune the system based on the results and make the necessary iterations as you go (which leads to the next point).
Monitor and maintain: Consistently monitor the performance of the reactive ML system in your production environment. In addition to this, update the model periodically as new data becomes available or when business requirements change. And lastly, regularly assess the system's impact on organizational outcomes and make adjustments as needed.

Again, these are very baseline as every project is going to have unique variables and every business is going to have unique goals. With that said, the most important part of digital transformation is what comes next, so with that in mind, consider this:

How scalable is the system? Whether you’re using a distributed computing framework, cloud services, or anything of the sort, the system needs to be designed while thinking about the volume of data and user requests it will need to handle.
What are your requirements for processing speed? If your reactive ML system needs to respond in real-time to user requests or traffic, processing speed becomes a major concern. To ensure it fits your ideal framework, you can optimize algorithms and hardware or use a distributed framework such as Apache Spark. Again, monitor the changes you make and keep looking for opportunities to refine.
How does it fit with current systems? When introducing reactive ML into your current systems, you have to consider how it will fit and interact with the infrastructure. APIs or connectors that enable data exchange are what you’re going to need if you want interoperability with existing systems.

The Takeaway

Finding components to build a framework that will support a business long-term is a never-ending quest. Doing what other companies are doing without an in-depth analysis of how the things you want to introduce will serve you long-term could set you back. It’s best to consult with an organization who’s overseen various projects

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.