What once was a shortage of coding and software engineering expertise has now translated into a shortage of skills in artificial intelligence and algorithmic engineering – machine learning talent.
copyright by www.softwaretestingnews.co.uk
According to a recent survey on enterprise AIOps adoption , 67% of enterprise IT organisations in the US have experimented with artificial intelligence (AI) and machine learning (ML) for data management and incident remediation.
What’s more, global research and advisory firm, Gartner, fully expects that artificial intelligence is expected to create more jobs than it replaces by 2020 . AI is moving fast and enterprises need new talent today, not tomorrow; and not just any old talent.
Here, I’m going to discuss some of the things I’ve learnt and some of the practical questions you can pose to uncover and secure the talent you need to help both your enterprise and potential employees succeed and excel.
No skills to pay the bills
Skills gaps are cited as among the biggest hurdles to AIOps adoption and implementation, and a recent EY survey of 200 senior leaders found that 56% see talent shortages as the single, largest barrier to implementing AI in business operations in 2018.
It’s clear that finding machine learning engineers is not an easy task. They’re a bit of a unicorn, combining engineering fundamentals with data modelling and statistical analysis. But, with the right framework, it’s entirely possible to build a team that has the right mix of data science, engineering know-how, and even a little robot emotional intelligence .
Math in the machine
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To start, machine learning engineers need a deep expertise in predictive modelling and statistical analysis. I always look for engineers who can combine core engineering fundamentals with the ability to see patterns in data and translate that into action.
Not only do they need to be able to manipulate code and build software, but they also need to understand how mathematical models can create insights, and how those insights can drive action in order to establish a candidate’s potential and knowledge.[…]
read more – copyright by www.softwaretestingnews.co.uk
What once was a shortage of coding and software engineering expertise has now translated into a shortage of skills in artificial intelligence and algorithmic engineering – machine learning talent.
copyright by www.softwaretestingnews.co.uk
What’s more, global research and advisory firm, Gartner, fully expects that artificial intelligence is expected to create more jobs than it replaces by 2020 . AI is moving fast and enterprises need new talent today, not tomorrow; and not just any old talent.
Here, I’m going to discuss some of the things I’ve learnt and some of the practical questions you can pose to uncover and secure the talent you need to help both your enterprise and potential employees succeed and excel.
No skills to pay the bills
Skills gaps are cited as among the biggest hurdles to AIOps adoption and implementation, and a recent EY survey of 200 senior leaders found that 56% see talent shortages as the single, largest barrier to implementing AI in business operations in 2018.
It’s clear that finding machine learning engineers is not an easy task. They’re a bit of a unicorn, combining engineering fundamentals with data modelling and statistical analysis. But, with the right framework, it’s entirely possible to build a team that has the right mix of data science, engineering know-how, and even a little robot emotional intelligence .
Math in the machine
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
To start, machine learning engineers need a deep expertise in predictive modelling and statistical analysis. I always look for engineers who can combine core engineering fundamentals with the ability to see patterns in data and translate that into action.
Not only do they need to be able to manipulate code and build software, but they also need to understand how mathematical models can create insights, and how those insights can drive action in order to establish a candidate’s potential and knowledge.[…]
read more – copyright by www.softwaretestingnews.co.uk
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