Machine learning (or ML) is the process of humans teaching systems to learn from data instead of spelling out every rule by hand (also known as prompting). You feed it information, it looks for patterns and with enough information and time, it improves at its ability to spot potential issues, make guesses, and suggest actionable next steps.
Most companies today use machine learning in some capacity. Whether in finance, healthcare, retail, or other industries, machine learning has become a powerful asset. It helps people work faster and more efficiently by processing data at speeds and accuracy levels humans cannot match.
That said, there are some challenges to be aware of before adopting it into your business.
Benefits of machine learning
Let’s start off with some of the pros of machine learning and how you can use them to your benefit.
Improved decision-making
ML can sort through vast amounts of data faster than a human. For example, in finance it’s often used to flag suspicious transactions as they happen, which helps cut down fraud and protect customers in real-time.
Reducing repetitive work
In healthcare, ML can detect a case that may be high-risk, sort x-ray results, or help with early triage. This allows clinicians to spend more time on patient care instead of having hours drained by manually sorting through admin tasks or sets of data that could delay answers or treatment.
Better customer experience
Retailers use machine learning to recommend products based on what people want. It feels less like spam and more like a custom service when it’s done well. This can lead to more engagement, sales, and higher customer satisfaction..
Predicting issues and mitigating risk
Machine learning is often used to spot warning signs early. Manufacturers and infrastructure teams use it to predict equipment issues. Banks and insurers use it to assess risk, monitor compliance, and catch unusual behavior.
Growing without scaling teams
ML helps organizations do more with less. In the public sector it could help organizations to make city planning or resource management smoother and more informed from actual public data.
New ideas and innovation
In healthcare, machine learning can play a large role in research. This could be anything from drug discovery to clinical trials. ML helps teams test ideas faster and pick up on trends quicker, which can lead to better outcomes and shorter timelines.
Making sense of complex data
Not all data formats can fit into a spreadsheet. That’s where machine learning comes in. ML can handle any type of data from text, audio, video, and logs alongside structured data. For AI infrastructure teams, that could mean picking up on anomalies in system logs and improving performance in real-time.
Challenges of machine learning
As beneficial as machine learning can be, there are two sides to every coin. Here are some challenges to keep in mind when it comes to using ML.
Your data matters
ML is only as good as the data behind it. Incomplete or biased data can lead to bad predictions and poor decisions, sometimes with real-world consequences.
Decision-making restrictions
Some ML algorithms are difficult to understand. In regulated fields like finance and healthcare, being able to explain why a decision was made isn’t optional or something that can rely on guesswork, even by ML. This is why it’s imperative to have a human review AI suggestions rather than trusting it blindly.
ML and existing systems
If you’re working with an older or outdated tech stack, integrating ML can be a bit tricky. It takes proper planning, coordination, and patience to do it right. It’s important to take the time to learn different MLs, how they work, and how to best set one up for your needs.
ML is a skill set
Machine learning requires specialized skills, and those skills are in high demand and growing. Many industries are finding it difficult to find and hire people with the knowledge needed to build and maintain these systems. The more niche the field, the more niche the skill set needs to be.
Legal and ethical responsibilities
Privacy ad security need to be taken into consideration when you determine how to use MLs to your advantage. Industries handling sensitive data need to stay compliant with regulations like HIPAA and GDPR, which adds another layer of complexity that must be factored in early.
Ongoing upkeep
ML models don’t stay the same. As data changes, ML systems need to be monitored and re-trained so they don’t lose accuracy or effectiveness with time.
Move forward with confidence
Machine learning offers enormous potential to transform your ITOps, shifting your organization from a reactive state to a proactive and innovative one. While the challenges of data, complexity, and operational risk are real, they are not insurmountable. With the right platform, you can harness the power of AI and ML to build more resilient services, improve efficiency, and empower your teams. The PagerDuty Operations Cloud provides the automation, intelligence, and control you need to move forward with confidence.
See how PagerDuty’s AIOps capabilities can help you harness the power of machine learning. Request a demo today.