DevOps teams seeking to step up their mojo in developing cutting-edge artificial intelligence (AI) features are facing a big skills bottleneck when it comes to data analytics and machine learning modeling.
copyright by devops.com
DevOps teams seeking to step up their mojo in developing cutting-edge artificial intelligence (AI) features are facing a big skills bottleneck when it comes to data analytics and machine learning modeling. As a result, the market is seeing an influx of self-service machine learning models and machine learning-as-a-service offerings designed to help development teams more easily integrate AI capabilities into their software.
This is coming in direct response to an explosion in demand for AI capabilities in the enterprise. According to Gartner analysts, AI adoption in the enterprise tripled in the past year . A report last fall from MIT Sloan Management Review and Boston Consulting Group found that 91% of enterprises believe that AI will deliver new business growth to them by 2023.
The trouble is that folding AI functions and predictive
Tools such as these are not always going to obviate the need for data scientists and machine learning/AI specialists on DevOps teams. As Farhadi noted, even a simple service like AI2Go requires development teams to choose the right models for the right situations. Nevertheless, this burgeoning market could take the pressure off teams by minimizing the scale of specialized recruiting, while reducing bottlenecks and dreaded re-work.
into software requires a whole new level of expertise in data science and machine learning from cross-functional DevOps teams. They need added skills to choose the right algorithmic approaches, acquire and manage data, train the models and integrate them into the code base and underlying infrastructure so that everything works properly under the hood.
“We have heard from customers everywhere that they want to adopt machine learning but struggle to actually get models into production,” said Eric Boyd, vice president of cognitive and AI for Microsoft.
DevOps leaders today already face an uphill battle to keep their developer ranks staffed with well-trained software engineers. Piling on additional requirements for very specialized machine learning and data science expertise further strains those recruitment efforts. According to the most recent Harvey Nash/KPMG CIO Survey, the top one and two technical realms that suffer the biggest skills shortages today are in data science and AI, with 46% and 38% of CIOs respectively reporting recruiting pain in those areas.
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“There’s a huge imbalance between the demand in the market and the supply of the very best AI experience to actually train these models,” said Ali Farhadi, CEO of Xnor.ai, a Seattle firm that just last week released a self-service platform for developers. Called AI2Go, the platform provides pre-trained deep learning models to quickly integrate AI features such as facial recognition and object classification directly into their software.
“We want to enable anyone who can code to benefit from AI,” Farhadi said.[…]
read more – copyright by devops.com
DevOps teams seeking to step up their mojo in developing cutting-edge artificial intelligence (AI) features are facing a big skills bottleneck when it comes to data analytics and machine learning modeling.
copyright by devops.com
This is coming in direct response to an explosion in demand for AI capabilities in the enterprise. According to Gartner analysts, AI adoption in the enterprise tripled in the past year . A report last fall from MIT Sloan Management Review and Boston Consulting Group found that 91% of enterprises believe that AI will deliver new business growth to them by 2023.
The trouble is that folding AI functions and predictive
Tools such as these are not always going to obviate the need for data scientists and machine learning/AI specialists on DevOps teams. As Farhadi noted, even a simple service like AI2Go requires development teams to choose the right models for the right situations. Nevertheless, this burgeoning market could take the pressure off teams by minimizing the scale of specialized recruiting, while reducing bottlenecks and dreaded re-work.
into software requires a whole new level of expertise in data science and machine learning from cross-functional DevOps teams. They need added skills to choose the right algorithmic approaches, acquire and manage data, train the models and integrate them into the code base and underlying infrastructure so that everything works properly under the hood.
“We have heard from customers everywhere that they want to adopt machine learning but struggle to actually get models into production,” said Eric Boyd, vice president of cognitive and AI for Microsoft.
DevOps leaders today already face an uphill battle to keep their developer ranks staffed with well-trained software engineers. Piling on additional requirements for very specialized machine learning and data science expertise further strains those recruitment efforts. According to the most recent Harvey Nash/KPMG CIO Survey, the top one and two technical realms that suffer the biggest skills shortages today are in data science and AI, with 46% and 38% of CIOs respectively reporting recruiting pain in those areas.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
“There’s a huge imbalance between the demand in the market and the supply of the very best AI experience to actually train these models,” said Ali Farhadi, CEO of Xnor.ai, a Seattle firm that just last week released a self-service platform for developers. Called AI2Go, the platform provides pre-trained deep learning models to quickly integrate AI features such as facial recognition and object classification directly into their software.
“We want to enable anyone who can code to benefit from AI,” Farhadi said.[…]
read more – copyright by devops.com
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