It’s no secret that machine learning (ML) has experienced tremendous growth and adoption over the last few years. And why not? This exciting technology has enabled us to utilize the power of machines for a wide variety of applications and industries. From image processing to predicting to medical diagnosis, ML has begun to reshape the way we live.
Every successful company plans for sustainability and growth. Forecasting the growth path helps companies set their short- and long-term business objectives and make important decisions to help them reach their goals. Short-term forecasts are important in quarterly and annual budget planning and for ensuring that daily business operations help achieve long-term goals.
We’ve all experienced it – executives with unrealistic expectations who vastly underestimate the amount of time our work can take. Most of us assume that to be the exception and not the norm. But when it comes to monitoring and troubleshooting, that seems to be the all too commonplace.
Customers today are faced with a wide variety of industry terminology: APM, IOTA, BPM, OI, BAM and AIOps, just to name a few. Using different terminology like this might help large vendors expand their market size with different positioning offerings, but it certainly doesn’t help their customers understand what they’re getting. Companies spend tens of millions of dollars to solve their problems with the wrong solutions and struggle to get value from it.
