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Five Practical Challenges in Enterprise AI / ML

By dotData

Updated for 2022 According to a recent Gartner blog about analytics and BI solutions, only 20% of analytical insights will deliver business outcomes through 2022. Another article by VentureBeat AI reported that 87% of data science projects never make it into production. And a global survey by Dimensional Research concluded that 78% of their AI/ML projects stall at some stage before deployment. Even in 2022, as many as 68% of data scientists admit to abandoning 40% to 80% of their Data Science projects. These results indicate an exceptionally high failure rate across analytics, data science, and machine learning projects. There are many reasons why so many projects fail to meet their business objectives. In this blog, we look at the top practical challenges that enterprise AI projects face and how you can mitigate them: Start with business problems you need to solveWhile AI is an incredibly powerful technology, it is…

Shattering 5 Misconceptions about Automated Machine Learning

By Sachin Andhare

Ask data engineers about the most frustrating part of their job and the answer will most likely include “data preparation.”  Talk to a data scientist about the AI/ML workflow and what bogs them down, the answer invariably will be feature engineering. Analytics and data science leaders are well aware of the limitations of current AI/ML development platforms. They often lament about their team's ability to only manage a few projects per year. BI leaders, on the other hand, have been trying to embed predictive analytics in their dashboards but face the daunting task of learning how to build AI/ML models. Automated machine learning (AutoML) was built specifically to address some of the challenges of data science - the underlying practice at the heart of both problems. Like every new technology, there is a lot of confusion surrounding AutoML. Here are the top 5 misconceptions about AutoML: 1. AutoML means selecting the algorithms…