In our previous article — 5 Challenges to be prepared for while scaling ML models, we discussed the top five challenges in productionizing scalable Machine Learning (ML) models. Our focus for this…Continue
Added by Raghavendra Singh on September 21, 2020 at 3:36am — No Comments
Machine Learning (ML) models are designed for defined business goals. ML model productionizing refers to hosting, scaling, and running an ML Model on top of relevant datasets. ML models in production also need to be resilient and flexible for future changes and feedback. A recent study by Forrester states that improving customer experience, improving profitability & revenue growth as the key goals organizations plan to achieve specifically using ML initiatives.
Added by Raghavendra Singh on September 10, 2020 at 12:00pm — No Comments
Enterprise software, as well as other kinds, remains a complicated endeavor, thus necessitating the use of modern means to gauge, analyze, and adapt their performance. And one of the most popular technologies in the performance engineering market right now is machine learning. Since it has demonstrated an unparalleled ability to not only help foresee performance issues and fix them. When used in the right manner — this combination can also help performance engineering teams to steer clear of…Continue
Added by Ryan Williamson on August 30, 2020 at 11:00pm — No Comments
Sales Forecasting: Science or Instinct?
Recently, we had talked about marketing automation, a digital transformation in the field that involved both artificial intelligence and machine learning. Even Natural Language Processing (NLP) has come a long way, enough to change the content marketing game. But while marketing has shifted to a digital…Continue
Added by Jay Nair on June 5, 2018 at 5:30pm — No Comments
Technology is known to shift landscapes, even change the game. We saw that when the internet exploded in scale and popularity, as computers became smarter, and the world goes through the digital transformation. An easy example is in traditional marketing, which now borders on the irrelevant, unable to hold a candle to its more modern counterparts.
Added by Jay Nair on May 18, 2018 at 4:30pm — No Comments