// EDITOR ’ S QUESTION ?
RAMPRAKASH RAMAMOORTHY , DIRECTOR , AI RESEARCH , MANAGE ENGINE
S ince the onset of the pandemic , the first touchpoint for many businesses has been digital . Organisations must remain digitally competitive to stay afloat , and they achieve this by implementing newer technologies like Machine Learning . Another factor is the ongoing AI summer , during which there have been a lot of investments in AI and other associated technologies , which in turn has increased the adoption of Machine Learning across the globe .
Because Machine Learning enables enterprise software to move from process automation to decision automation , using Machine Learning involves rewriting current , traditionally deterministic processes and workflows to make them probabilistic .
For instance , a traditional anomaly system uses the bell curve to identify anomalies , whereas a Machine Learning-powered anomaly system identifies anomalies along with the probability of an outage occurring . CIOs have to drive these changes and incentivise teams to use and integrate new technologies like Machine Learning into their everyday workflows by citing the impact they could have on business growth .
Machine Learning has impacted almost every field given the growth of digitisation across domains and IT is no exception . AIOps is a trending topic in enterprise IT right now . Vendors currently deploy AI across service delivery , operations monitoring , security and Endpoint Management . The list of AI use cases includes anomaly detection , forecasting , outage prediction , root cause analysis , chatbots , malware and ransomware detection , phishing detection and agent prediction .
The right Machine Learning system should work with the enterprise ' s available data quantum . In the consumer space , people talk about terabytes of data . But in IT , data sets can be much smaller , sometimes just a few hundred rows . Choosing an accurate Machine Learning model for the amount of data available is key .
The model must also work in the organisation ' s preferred deployment modes . Typically , ML models are restricted to cloud deployments , but in IT , an anti-ransomware model needs to be deployed at the Edge .
It is also crucial that the vendor employs security and privacy best practices and that the models remain bias-free when deployed .
ManageEngine is very optimistic about how Machine Learning could change the way we work in the near future , and we are continually investing in the technology . Ensuring the data is bias-free , implementing an explainable model with integrated systems that work with multiple deployment modes , and better identifying the right use cases will improve the ROI from ML deployments . �
CIOS HAVE TO DRIVE THESE CHANGES AND INCENTIVISE TEAMS TO USE AND INTEGRATE NEW TECHNOLOGIES LIKE MACHINE LEARNING INTO THEIR EVERYDAY WORKFLOWS BY CITING THE IMPACT THEY COULD HAVE ON BUSINESS GROWTH .
Intelligent SME . tech