01-03-2019, 06:32 PM
Assessing Progress in Automation Technologies
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<p>To assess the state of adoption of machine learning (ML) and AI, we recently conducted <a href="https://www.oreilly.com/data/free/state-of-machine-learning-adoption-in-the-enterprise.csp">a survey</a> that garnered more than 11,000 respondents. As I pointed out in previous posts, we learned many companies are still in the early stages of deploying machine learning:</p>
<p><img alt="machine learning adoption" src="http://www.sickgaming.net/blog/wp-content/uploads/2019/01/assessing-progress-in-automation-technologies.jpg" /></p>
<p>Companies cite “lack of data” and “lack of skilled people” as the main factors holding back adoption. In many instances, “lack of data” is literally the state of affairs: companies have yet to collect and store the data needed to train the ML models they desire. The “skills gap” is real and persistent.</p>
<p>Read more at <a href="https://www.oreilly.com/ideas/assessing-progress-in-automation-technologies">O’Reilly</a></p>
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<div style="margin: 5px 5% 10px 5%;"><img src="http://www.sickgaming.net/blog/wp-content/uploads/2019/01/assessing-progress-in-automation-technologies.jpg" width="1908" height="1076" title="" alt="" /></div><div><div class="lcom-stacked__main">
<div class="panel-pane pane-entity-field pane-node-body">
<div class="field field-name-body field-type-text-with-summary field-label-hidden">
<div class="field-items">
<div class="field-item even">
<p>To assess the state of adoption of machine learning (ML) and AI, we recently conducted <a href="https://www.oreilly.com/data/free/state-of-machine-learning-adoption-in-the-enterprise.csp">a survey</a> that garnered more than 11,000 respondents. As I pointed out in previous posts, we learned many companies are still in the early stages of deploying machine learning:</p>
<p><img alt="machine learning adoption" src="http://www.sickgaming.net/blog/wp-content/uploads/2019/01/assessing-progress-in-automation-technologies.jpg" /></p>
<p>Companies cite “lack of data” and “lack of skilled people” as the main factors holding back adoption. In many instances, “lack of data” is literally the state of affairs: companies have yet to collect and store the data needed to train the ML models they desire. The “skills gap” is real and persistent.</p>
<p>Read more at <a href="https://www.oreilly.com/ideas/assessing-progress-in-automation-technologies">O’Reilly</a></p>
</div>
</div>
</div></div>
</p></div>
<p><a href="https://www.linux.com/popup/nojs" class="ctools-use-modal ctools-modal-subscription-modal-style element-invisible" title="">Click Here!</a> </p>
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