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How Anaconda's data science platform will help IBM speed up enterprise machine learning adoption - TechRepublic

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On Monday, IBM announced that it has partnered with Continuum Analytics to offer open data science platform Anaconda on IBM Cognitive Systems. Anaconda, which is powered by Python, will also integrate with IBM's PowerAI software for machine learning and deep learning, making it easier and faster for businesses to analyze and gain insights from data-intensive cognitive workloads. "Anaconda is an important capability for developers building cognitive solutions, and now it's available on IBM's high performance deep learning platform," said Bob Picciano, senior vice president of Cognitive Systems, in a press release. "Anaconda on IBM Cognitive Systems empowers developers and data scientists to build and deploy deep learning applications that are ready to scale." Deep learning--one of the fastest-growing fields of machine learning, the release noted--makes it possible to process datasets that include up to billions of elements, and to find predictive models in that data.


Is the automotive industry paying software engineers enough?

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"The best minds of my generation are thinking about how to make people click ads," Jeff Hammerbacher, former head of Facebook's data team, once lamented. After all, those same "best minds" could instead be writing software for automotive companies. Given that a truck like the Ford F-150 now includes more than 150 million lines of code, and that software increasingly defines the value of a car, the auto companies certainly could use the help. And yet, despite the pressing need for more software expertise in Detroit and other hotbeds of automotive manufacturing activity, it's not clear that they've gotten serious about software. The automotive sector has had to hit the brakes on sales over the past year, given tight supplies of the semiconductors that increasingly compose a car.


Developer burnout isn't going away. Employers need to act now

ZDNet

Big workloads continue to have a huge impact on resource-strapped software teams, with a new survey by Haystack finding that more than 8 in 10 developers suffer from burnout at work. Technology has played a key role in the fight against COVID-19, with IT teams helping businesses to adapt to remote working and digital-first operations. But this rapid adoption of technology has had a massive impact on those tasked with implementing it, with various reports highlighting the mental strain developers have faced over the past year as a result. Becoming a certified ethical hacker can be a rewarding career. Here are ZDNet's recommendations for the top certifications in 2021.


AI skills are a problem. AutoML can help

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O'Reilly just released its annual AI Adoption in the Enterprise survey, and the results are mostly unsurprising. For example, data scientists from organizations with mature artificial intelligence practices tend to turn to scikit-learn, TensorFlow, PyTorch and Keras. Also, supervised learning (82%) and deep learning (67%) were the most popular techniques used by survey respondents, whatever their phase of AI adoption. The biggest barrier to enterprise success with AI is difficulty finding people with the requisite skills. This is the exact same thing that plagues adoption in every technical market as a technology takes off.


The top 10 AI jobs in America

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As the rise of artificial intelligence continues to impact the workplace, with many employees fearing they may be eventually replaced, jobs in this advanced technology are more sought-after than ever. A new report from Indeed highlights the most in-demand jobs in AI, as well as the salaries that come with these positions. To create this list, Indeed took a look at job postings, by percentage that included an "AI" term, between February 2021 and April 2021. These "AI" definitions included the following phrases: "artificial intelligence," "ai engineer," "ai research," "ai scientist," "ai developer," "ai technica," "ai programmer," "ai architect," "machine learning," "ml engineer," "ml research," "ml scientist," "ml developer," "ml technical," "ml programmer," "ml architect," "natural language processing," "nlp," and "deep learning." Then Indeed figured out the average salary of these positions, incorporating their reported salary information over a period from May 2019 through April 2021.