As your tech stack increases, every new device (network devices, servers, applications) creates a large amount of distributed log data. This forms part of what is called “machine data”, which is growing 50x faster than traditional business data. In fact, everything in your stack is continuously writing new events to your log files, including error logs that contain a record of critical errors encountered by a running application, operating system, or server.

In 1927, the world was introduced to the origins of Artificial Intelligence (AI) in the form of a robot in the movie Metropolis. Throughout nearly a century since then, movies have continued to iterate on the complexities of AI, as both a fun take on it and serious commentary on the potential concerns and consequences. This is all well and good, but as AI has continued to evolve, we find ourselves asking, “how can we actually use this to make our lives easier?”

Every company in the world needs to reduce risk and uncertainty in its IT operations, ITOM. The best way to do that is by combining AI and digital workflows. It’s all about applying machine learning to operational data so that you can generate insights about potential system issues, and then launch automated workflows that resolve problems fast—ideally, before they impact customers. Today’s partnership announcement between IBM and ServiceNow is great news for enterprise customers.

From a security breach to a complete system outage, when an incident occurs and your network or service is impacted, it’s typically the result of a chain of events. A problem with one service has impacted another service, and so on until finally, you’re facing a problem that’s compromising availability and damaging your customer experience. In the event of a serious incident, your team’s immediate response is to focus on identifying the root cause and restoring service.

Think about your last online order. If you’re a frequent online shopper and have created profiles for sites you visit often (*raises hand*), then you’re probably familiar with customized recommendations. Based on your purchase history, location, and other factors, the website may suggest other items you might be interested in buying. And if you’re on the site long enough, chatbots may appear asking if you have questions or need assistance locating something.

Machine learning — the practice of writing algorithms that improve automatically through experience — has become a buzzword nowadays that connotes to something otherworldly and on the bleeding edge of technology. I’m here to tell you while that may be true, getting started with machine learning doesn’t have to be hard!

Five worthy reads is a regular column on five noteworthy items we have discovered while researching trending and timeless topics. This week, we highlight the urgent need for ethical AI due to increased adoption of AI enabled technologies by businesses during the COVID-19 pandemic.

Once you’ve reached the point where you want to deploy your machine learning models to production, you will eventually need to monitor operations and performance. You might also want to receive alerts in case of any unexpected behavior or inconsistencies with your model or your data quality. This is where you most likely start learning about various aspects of Machine Learning Operations (MLOps).

As there are more advances in data use, businesses must ensure they’re delivering value by utilizing all data sources present in their environments. Irrespective of the source of data, be it operational or transactional systems, smart devices, social media, video, or text, it’s what the business does with the data available that determines its value strategy.