Moving data science into production ... the challenge inherent in automatically deploying data science into production brings many data science projects to a grinding halt. First, IT too often ...

According to Gartner analyst Nick Heudecker, over 85% of data science projects fail. A report from Dimensional Research indicated that only 4% of companies have succeeded in deploying ML models to ...

In 2018, every organization has a data strategy. But what makes a great one? Hilary Mason is the GM for Machine Learning at Cloudera. She was the Founder of Fast Forward Labs, acquired by Cloudera ...

But if this is a universal understanding, that AI empirically provides a competitive edge, why do only 13% of data science projects, or just one out of every 10, actually make it into production?

The economic shock created by the pandemic and its recovery has already highlighted the competitive advantage of effective deployment ... of an analytics project, data science expertise is required.

a lack of understanding is hampering the ability of business leaders to effectively deploy data science, machine learning and artificial intelligence projects to solve business problems.

This is not an easy task. A typical enterprise data science project is highly complex and requires deployment of an interdisciplinary team that involves assembling data engineers, developers ...

Organizations that seek the most accurate results from their AI projects will simply have ... Or — if they really needed to — data science teams could build their own models in-house, from ...

The new three-course specialization is designed to help data professionals master the essentials of machine learning and efficiently deploy data science projects at scale in the AWS cloud.