What is the Future of Machine Learning for IT?

How will things change for IT organizations with the advancement of ML and AI? IT personnel will probably spend less time monitoring the network for issues and more time supporting innovative industry solutions. In this chapter, we’ll find out how the workload will change for ITOps as ML matures.

Content Summary

Considering the Trends and Advances in Machine Learning
Driving Industry Innovations
Assessing Patients Using AI and Automation
Machine Learning and AI in Manufacturing
Benefits of ML for ITOps across Industries
Conclusion

Considering the Trends and Advances in Machine Learning

Many forces will impact how and when companies will use ML. Three particularly interesting areas include multiple data algorithms, observability platforms, and federated ML.

Multiple Data Algorithms

A big trend in AI for operations is applying capabilities from one data type to multiple data types. This began with different probabilistic methods such as AI, ML, and statistical analysis being applied to a single data type that was either metrics, logs, or transactions.

Soon, data scientists will design algorithms for multiple data sets together. The algorithm will look at the metric, log, and transaction data together, how they correlate, and what signals can be filtered out to make troubleshooting easier and faster.

These algorithms for multiple data types will help IT organizations save time by enhancing early warning systems and filtering signals more effectively.

Observability Platforms

Observability platforms are designed to look at metrics, traces, and logs, bringing them together to find the connection between the different data types. This data provides IT organizations with a broad view across the customer experience, employee productivity, and digital infrastructure to understand how the business is performing. In addition, incorporating ML and automation into these platforms reduces the time required to prevent system problems proactively.

Federated Machine Learning

Collaborative learning, known as federated learning, is an ML technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples without exchanging them. At the moment, it is being used to satisfy stringent global privacy regulations while making sure the business can still use the data. It adds a layer of data protection to ML technologies by decentralizing the ML model and distributing the algorithmic learning across more than one device.

This helps solve one of the key issues that arise from ML techniques today: aggregating large volumes of possibly sensitive data to train the model and keeping that data in one place. Gathering this data puts organizations at risk for malicious actors and privacy breaches. Federated learning models create a framework such that the devices do not exchange or share any data, and no centralized location is relied on to send information anywhere. Only the owner of the data has access to the information.

Still, federated learning is not entirely risk-free. But it’s a new approach that has been gaining traction (since about 2017). Adaptive ML can be combined with federated ML to use model improvements in multiple locations and usage contexts. This approach can enable, for instance, autonomous systems, such as self-driving vehicles or smart robots. For ITOps organizations, this might lead to sophisticated, automated network monitoring for businesses or governmental agencies required to follow restrictive privacy regulations.

Driving Industry Innovations

Many industries started investing in ML and AI long before the global pandemic. Forced lockdowns helped companies in the healthcare and pharmaceutical industry and those in manufacturing to accelerate their digital transformation plans.

Machine Learning in Healthcare and Pharmaceuticals

Covid-19 was, in many ways, a catalyst for advancing technologies in the pharma, medicine, and health sector. As a result, IT organizations in this industry support clinicians and medical facilities that have a renewed focus on remote patient monitoring and telehealth, nursing, and patient care in general.

AI technology is rapidly expanding into other healthcare areas, including early detection of diseases, treatment, and research. And it’s not as new as you might think. Three years ago, Japan was already addressing its doctor shortage with AI. The technology will continue to evolve and play a more prominent role, especially as the world manages Covid-19. From IoT to automation, ML and AI offer exciting opportunities to take healthcare to the next level.

Improving Patient Care with Connected Devices and IoT

Remote medicine today relies on connected devices and remote sensor technology. Devices include monitors for insulin levels and heart rate, which allow clinicians to manage chronic conditions without being in the same room with a patient. IoT devices can also produce the data needed to help determine a patient’s healthcare needs in ways that can improve patient outcomes substantially.

Assessing Patients Using AI and Automation

AI is already being widely used as a diagnostic error prevention tool in radiology. Now AI is being used to help automate triage situations and help with prescription handling. This can ease the burden of clinicians and allow them to focus on more advanced diagnoses and ultimately see more patients. In addition, Chatbots—now ubiquitous across so many industries—are an easy, familiar tool for patients to engage with for care, and they reduce costs for healthcare companies.

Refining Drug Development and Efficacy

Device and drug efficacy can be improved through data analytics with AI engines. Comparative studies are made significantly easier and cheaper by using ML models to assess the effectiveness of new drugs to market or new medical devices. AI can also identify patterns between different drugs and diseases to help with the rapid prototyping of new drugs or new uses for existing drugs.

Machine Learning and AI in Manufacturing

Like many industries, manufacturers have been on a digital transformation journey for a couple of decades. The move towards automation is being accelerated by AI, robotics, and the public cloud. It shows up in nearly every aspect of the modern manufacturing business, even customer service and marketing. The evolution of IoT and sensor technologies alongside advanced digital modeling allows today’s manufacturing businesses to drive innovation and change at an accelerated pace. Analyzing data from digital prototypes can accelerate the development of a product significantly. Let’s explore the ML and AI trends in manufacturing to glimpse what the ITOps groups might soon be supporting.

Customer Service and Analytics

An effective AI implementation is built on data. Strategies for initiatives like predictive maintenance, digital customer success applications, and BPM rely on accurate environmental data. By leveraging environmental sensor data and AI, for instance, it is possible to predict changes in temperature, humidity, or other factors that could take a plant offline.

ChatBots

Companies in the manufacturing sector have successfully implemented ML and AI in several areas, but they are just starting to consider using chatbots, which are widely used in other industries. Implementing an effective chatbot could reduce customer service costs considerably and improve customer satisfaction.

Digital Twin

Originally pioneered by NASA, the Digital Twin has been one of the top technology trends for more than five years now. Mirroring physical environments in a digital framework is key to product innovation, improving processes, reducing downtime and waste, as well as improving customer experience. With the advancement of IoT and AI, Digital Twin technology is fast becoming the mainstream as manufacturers attempt to keep up with the market leaders in their field. Ensuring these models are populated with the right data is important—and that can be an easier task with ML.

Intelligent Automation and RPA

Automating repetitive processes with robotic process automation (RPA) can help beyond just the manufacturing process. It is also useful in other areas like order processing and logistical operations, where staff have to repeat multiple tasks that could be automated. The obvious contenders for RPA, like customer service, are now automated in most companies. RPA is increasingly being applied to other time-consuming processes within a business, resulting in better employee and customer satisfaction and productivity.

Industrial IoT

Industrial IoT (IIoT) offers manufacturers great benefits, including cost savings and better visibility of plant operations. It can also help IT organizations discover the root cause of major issues like outages.

Benefits of ML for ITOps across Industries

These industry examples showcase how ML and AI are changing healthcare and manufacturing for the better. However, these use cases also highlight the changing responsibilities for ITOps personnel. Fortunately, ITOps can apply ML to offload manual, repetitive tasks, such as monitoring networks, anomaly detection, and root cause analysis. This gives ITOps teams the ability to focus on supporting these innovative and strategic solutions.

Conclusion

In this chapter, we explored how the workload might change for IT organizations as ML matures, using industry examples. The future of an IT organization enhanced by ML will combine algorithms with advancements in personalization, resulting in IT personnel spending less time monitoring for IT issues and more time focusing on innovating. In our next chapter, we’ll look at the rise of Quantum Computing and how it could benefit businesses across many industries.