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…

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.

Though gaining…

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…

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…

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.

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