8 Things to Know When Building a Reactive Machine Learning System

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: 

1. 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.

2. 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.

3. 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.

4. 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). 

5. 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:

1. 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.

2. 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.

3. 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

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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.

From Novices to AI Experts: Training Your Employees on AI for Business Growth

Integrating AI into your business is one thing, but getting the full benefit and ensuring long-term returns is another. Many companies are investing a vast amount of time and resources to implement AI technologies, but they may be flying blind without some key considerations.

To give you an example of these considerations, let’s take a retail business that implements chatbots to handle customer inquiries. Seems simple enough right? Well, the fact is that having the system is just the tip of the iceberg. The next priority is to familiarize employees with the system and focus on how its capabilities and limitations can impact their work. This training should also outline protocols that explain how to collaborate with the chatbots and when to escalate customer inquiries to human agents.

Over time, employees will benefit significantly from understanding how the system functions. You should be able to ask, “How does the chatbot interpret data? What sort of things do you not let it handle? Can the system learn from customer interactions? What metrics are you using to evaluate performance?” and be able to get a clear answer to every single one. 

While this is just one small-scale example, think about an enterprise business with petabytes of data and AI systems deployed across several departments. The range of considerations will vary dramatically depending on the industry, company size, and of course the business's unique goals. Your employees are your biggest asset, and if they’re going to be working extensively with AI, there are certain things that companies need to incorporate to ensure a smooth operation.

1) Knowing When To Use It

A report from Entrepreneur noted, “Human creativity and ingenuity will always be required to find the problems AI can solve in the first place”. It’s interesting to see tools released to the public like Chat GPT or MidJourney that can automate tedious tasks, but it’s even more interesting to examine the different tasks people deploy those tools for. 

For instance, if you're an advertising agency and you’ve trained your employees on efficient ways to use AI to generate ad copy or certain graphic artwork. Those employees are going to view the tools entirely differently than the high school administrators who see them as a means for students to cheat. 

With that being said, there’s a lot of speculation on the “right and wrong” ways to use these tools which makes it all the more important for businesses to recognize where it fits. In this case, it’s best to have deployment protocols, something that can dictate not only appropriate use cases for AI within the organization but also specific guidelines for managing AI systems.

2) Using AI to Better Understand Your Customer

Artificial intelligence has 3 main functions that are the building blocks for how it understands someone:

• Processing data: This is going to take a large volume of data from places like your website interactions or social media engagement, for example, and then put that data through algorithms to analyze it and extract meaning from it.

• Pattern recognition: AI is masterful at recognizing patterns and anomalies within datasets which makes it very useful for both security (threat detection) and predictive analytics (which we will get to next). These factors play a pivotal role in AI’s ability to use the data it’s processed and understand user behaviour in a range of contexts. 

• Predictive analytics: By far this is one of the most valuable functions of artificial intelligence. Because AI can recognize patterns, organizations are able to make proactive moves rather than reactive ones. Once the systems recognized patterns, it can recognize needs, trends, preferences, and buying patterns, detect threats, and how to optimize resources among other factors.

So, if these systems seem to do it all, why would a company need to educate employees on how to use them? Again, it comes back to capabilities versus limitations. Do your users always want to run inquiries through a chatbot? Or are there some aspects of your operation that require human oversight?

Systems will need guidance to some extent, and regardless of whether two businesses are in the same industry - they will each have unique processes and goals. This is why the emphasis is on collaboration with these systems because a team that knows how to use AI as an extension will be a lot more effective than the team who uses it as a shortcut. 

3) Maximizing Output

Imagine a manufacturing company that implements predictive maintenance systems that can detect potential equipment failures and then schedule maintenance before it happens. However, the employees responsible for the maintenance don’t know how to interpret the system's recommendations. Seems like a good waste of investment on the company's part. 

When a company implements AI in its processes, the goal should be to always have actionable insights. From this, companies can consistently bridge the gap and maximize the results from the system's output. 

This is going to lead us to the next point…

4) Leverage Actionable Insights

Having insights is one thing; taking proactive steps to translate them into tangible outcomes is another, and it’s where the most value is in terms of longevity. Here are 3 general ways businesses can make this happen:

Communication and collaboration: Insights should be distributed to stakeholders across all departments to ensure they have the necessary information to consider during their decision-making processes. 

Facilitate Discussion and Feedback: you should encourage regular feedback sessions where stakeholders can share their thoughts. This is going to create a collaborative environment that lets you consider alternative ways to execute based on AI-generated insights. Additionally, actively listening to feedback and implementing it will create a positive work culture that’s going to make your organization's transition to AI a lot more effective. 

Measure and Evaluate the Impact: Establish KPIs or metrics to measure the effectiveness of each decision made. This feedback loop allows for consistent refinement and streamlining of the decision-making process, again, moving toward a long-term sustainable internal process. 

The Company Who Does Vs. The Company Who Doesn’t

The main reason we’ve alluded to sustainability for training employees on AI is the stark difference between the future of companies that invest in training and those that neglect it. Think about the financial industry 15 years from now, “The company who does” will not only implement new AI systems, but they will educate employees about their significance which will make them more inclined and comfortable when leveraging AI tools. In the long term, processes will be a lot more efficient and the customer experience will stand out.

“The company that doesn’t” is one that may have an AI system, but neglects research, training, and education for employees. In the short term, they might see results from the system, however in the long term, without the necessary research, training, and education, their potential is capped. 

What’s Next for AI?

There’s so much refinement and constant innovation happening in the field of AI which emphasizes the importance of consistently learning more about this technology as a business. As soon as you become complacent in your industry, it’s already starting to work against you, which is why you need a well-educated team that is committed to staying up to date with the latest developments in AI and trends in the industry. 

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.

4 Ways to Optimize Your Business Using AI

At this moment, companies might have at least 6 months to get their act together before AI comes back with some hard changes for their industry. 

I want you to imagine your business right now, whether you’re the owner, an employee, or any sort of stakeholder. If you’ve been around for longer than 10 years, there’s a big chance you’ve had several major disruptions in your industry that you were forced to overcome.

Now, we stand at the cusp of yet another disruption that will completely transform not only the way your business operates, but the world itself. If you think that’s an overstatement, stop and think; how much does your company currently spend on employee salaries per month? I want you to think about that number and then look around at some AI service providers and see how much you could implement with that sum. 

It’s a harsh vision, especially to think that jobs people have done for decades could be taken over by AI. However, this article isn’t meant to instill fear about job security, we simply believe that by understanding the transformative potential of AI and its specific features, companies can make informed decisions to stay well ahead of the curve and remain competitive. With that said, here are 4 ways businesses can optimize their operations to adjust to today’s demands:

1. Managing The Supply Chain

Predictive analytics are miraculous in their range of abilities. Most notable is supply chain management, where the AI system can easily identify fluctuations in demand well in advance. This enables proactive planning and minimizes the carrying costs of inventory. 

What AI algorithms can do here specifically is analyze historical data, market trends, weather patterns, and social media sentiment to predict future demand with unbelievable accuracy. This is a big advancement whether your company actually produces material goods or offers a specific service.

For service-oriented businesses, effectively managing operational costs while meeting customer needs is the name of the game. The reason I bring this up is because predictive analytics are often exclusively discussed regarding their ability to serve companies heavy on manufacturing demands. But that goes against the whole basis of AI pertaining to its ability to manage data in just about any industry, so we’ll lay it out clearly.

For service providers, AI algorithms can forecast service demand which then allows companies to allocate their staff and resources more effectively. There’s always been that phrase business owners use: “Well I can’t predict the future, but…”. Now there’s no need for this, as AI algorithms can make accurate predictions.

2. The Core 4

Every business strategizing with AI is checking off one of the following boxes: efficiency, effectiveness, expertise, or innovation. According to a report from developer Jacob Bergdahl, each strategy is broken down in terms of the company’s data and will look something like this:

The efficiency strategy: Low data > Low work complexity

The effectiveness strategy: High data > Low work complexity

The expert strategy: Low data > High work complexity

The innovation strategy: High data > High work complexity

When strategizing with AI, first identify where your needs fall on this scale. If you don’t, you’ll end up with a solution that doesn’t meet your needs and isn’t sustainable. 

3. The Volume and Complexity of Data

Branching off the previous point, as markets continue to shift online, the amount of data businesses accumulate increases exponentially. With that being said, the number one reason businesses can survive this kind of change is through their ability and willingness to pivot. 

As a means to do so, the digital landscape has opened up new avenues for collecting as well as generating vast amounts of data from various sources such as user interactions, transactions, social media, etc. What’s important to know is that this rise in data gives businesses just as many opportunities as it does challenges.

On the one hand, the abundance of data gives businesses valuable insights into customer behaviour, market trends, and how their performance stacks up. It enables companies to make “data-driven decisions”, which are based on initiatives such as personalizing customer experiences, optimizing processes, and of course, identifying new growth opportunities. 

However, the sheer volume and complexity of this data can quickly become overwhelming without the right strategies and technologies in place. To mitigate this risk, businesses must integrate data management tools that can streamline data workflows. Here’s how this can happen:

Data Storage and Infrastructure: Cloud-based storage solutions, data lakes, and distributed databases all contribute to building a scalable and secure data storage infrastructure. By investing in the components needed for this infrastructure, companies will be a lot more flexible. 

Data Integration and Consolidation: Businesses often encounter data silos, where valuable information is scattered across different systems and departments. What implementing data integration strategies and tools can do is help consolidate and unify all of that data, which will give you an overall holistic view of your organization's operations and customer interactions.

Data-driven Decision-Making: This is a hot-button topic right now and for good reason. Encouraging a data-driven culture within an organization is vital to align the team with the vision behind change and ultimately where the industry is headed. Training employees on data literacy, promoting data-driven decision-making processes, and fostering a mindset that values data-driven insights will empower people to get behind these processes and leverage their capabilities.

4. Security

Cyber extortion and ransomware attacks have been on a huge upswing in 2023. In March, Ferrari, Skylink, and Alliance Healthcare were a few entities that faced serious issues with breaches. The threat of ransomware and malware becomes more significant as the volume of data that businesses work with increases.

For this reason and several others, artificial intelligence is a must for enterprise businesses. Here’s how it makes a difference:

Recognize Threats Early

An AI-powered security system is a digital detective that’s never off the clock. It will analyze data from your network traffic, log files, and user behaviour, to detect anomalies and potential indicators of cyber attacks. The machine learning algorithms embedded in these systems can identify irregularities that signal ransomware and malware, which will give you a response to early threat detection.

Respond to Threats Early and Automatically

When a security breach is detected, AI systems can trigger immediate responses, which might include isolating affected systems, blocking malicious connections, or initiating backup and recovery procedures. 

The difference between your business using an AI system and your competitor who’s too slow to strategize comes down to one thing: vulnerability. Your competitor is going to be highly susceptible to cyber threats and attacks without this threat detection and response system, while you'll have a robust defence in place.

Fixing Weak Spots

AI can help you find and prioritize vulnerabilities in your IT setup. It looks at things like system configurations, software versions, and patches to identify any weak points that ransomware and malware could exploit.

What to Know Going Forward

It’s a lot right? This information is as general as it gets when looking at the things companies need to do with AI because there’s so much subjectivity with IT. The best thing you can do as a business starting your journey with AI is to research and evaluate reputable AI service providers that offer solutions aligned with your business needs. Have consultations and ask for demonstrations to understand how their AI tools can be integrated into your existing infrastructure. Consider factors such as scalability, ease of implementation, and ongoing support to ensure a smooth transition.

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.