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5 machine learning essentials nontechnical leaders need to understand

TechCrunch

We’re living in a phenomenal moment for machine learning (ML), what Sonali Sambhus , head of developer and ML platform at Square, describes as “the democratization of ML.” Consider upskilling your current team of software engineers into data/ML engineers or hire promising candidates and provide them with an ML education.

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Role of AI In Hiring Software Engineers

Hacker Earth Developers Blog

As the great resignation continues, many companies are turning to AI-driven HR software to increase retention rates and reduce costs. Looking beyond the conventional HR practices and managing every part of the software engineer lifecycle is a key to increasing talent acquisition margin.

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5 findings from O'Reilly's machine learning adoption survey companies should know

O'Reilly Media - Data

New survey results highlight the ways organizations are handling machine learning's move to the mainstream. As machine learning has become more widely adopted by businesses, O’Reilly set out to survey our audience to learn more about how companies approach this work. What metrics are used to evaluate success?

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Lessons learned turning machine learning models into real products and services

O'Reilly Media - Data

Today, just 15% of enterprises are using machine learning, but double that number already have it on their roadmaps for the upcoming year. However, in talking with CEOs looking to implement machine learning in their organizations, there seems to be a common problem in moving machine learning from science to production.

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Traceable AI nabs $60M to secure app APIs using machine learning

TechCrunch

Nagaraj, a Harness investor, has long been close within Bansal’s orbit, previously serving as the VP of software engineering at AppDynamics for seven years. Businesses need machine learning here. billion) and Harness (which recently raised a $230 million Series D). To have zero trust you need API clarity.

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Enhancing customer care through deep machine learning at Travelers

CIO

And we recognized as a company that we needed to start thinking about how we leverage advancements in technology and tremendous amounts of data across our ecosystem, and tie it with machine learning technology and other things advancing the field of analytics. But we have to bring in the right talent. more than 3,000 of themâ??that

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Are you ready for MLOps? 🫵

Xebia

Both the tech and the skills are there: Machine Learning technology is by now easy to use and widely available. So then let me re-iterate: why, still, are teams having troubles launching Machine Learning models into production? No longer is Machine Learning development only about training a ML model.