Real-time AI is driving the adoption of Kubernetes across apps, streaming data, databases and machine learning models. The lessons learned from cloud-native technologies and DevOps principles can accelerate the maturity of DataOps and MLOps. App dev leads and software engineers have a unique opportunity to take a leadership role in accelerating the real-time AI execution across apps, data and machine learning. Join us for a fast paced hybrid-cloud journey that looks at the data dimensions for real-time AI and shares the characteristics and capabilities to win the real-time AI race.

Oracle launched a new service this week designed to make life easier for data scientists building machine learning (ML) models. The Oracle Cloud Data Science Platform was created specifically to improve the effectiveness of data science teams, the company said, with such capabilities as shared projects, model catalogs, team security policies, reproducibility and auditability. "Effective machine learning models are the foundation of successful data science projects," said Greg Pavlik, senior vice president of product development in Oracle's Data and AI Services group, "but the volume and variety of data facing enterprises can stall these initiatives before they ever get off the ground. With Oracle Cloud Infrastructure Data Science, we're improving the productivity of individual data scientists by automating their entire workflow and adding strong team support for collaboration to help ensure that data science projects deliver real value to businesses." The platform was unveiled at a London event by Oracle CEO Safra Catz.

On Thursday the developers of PyTorch announced PyTorch Mobile, which they say will allow for "end-to-end workflow from Python to deployment on iOS and Android." PyTorch Mobile is part of PyTorch 1.3, which currently is an "experimental release" that the organization will be "building on over the next couple of months." PyTorch 1.2 was released in August. New features coming will include preprocessing and integration APIs, support for ARM CPUs and QNNPACK (a quantized neural network package designed for PyTorch), build-level optimization, and performance enhancements for mobile CPUs/GPUs. Android builds will use the Maven plug-in and iOS will use CocoaPods with Swift.

Integrating DataOps, artificial intelligence (AI) and machine learning (ML), Hitachi Vantara is touting big data technology in a Tuesday announcement of its Lumada Manufacturing Insights, a suite of Industrial Internet-of-Things (IoT) products. DataOps, which is an "automated, process-oriented methodology, used by analytic and data teams," according to a Wikipedia article, appears to be a major emphasis for Santa Clara, Calif.-based Hitachi Vantara, a wholly owned subsidiary of Japan's Hitachi, Ltd. "We know DataOps," pops out in large white letters on the first page of the company Web site. After asking if the Web visitor knows about the methodology, the site proclaims: "You know there's value in your data. But you've only scratched the surface. To get the full value out of your data, you need to get the right data to the right place at the right time. DataOps helps you do that."

Spending on artificial intelligence (AI) hardware and software is predicted to expand two-and-a-half times from current expenditure of $37.5 billion to $97.9 billion, according to a new report by International Data Corporation (IDC). That is a compound annual growth rate (CAGR) of 28.4 percent during the five-year 2018-2023 period of the report's forecast. Developers will see increased spending for AI software as it is predicted to overtake hardware purchases by 2023, according to the IDC Worldwide Artificial Intelligence Systems Spending Guide. In 2019 hardware spending for AI infrastructure is outpacing software purchases, IDC reports. But by 2023, it predicts that spending for AI software and AI software platforms will see 36.7 percent CAGR.

A big new developer survey shows that Python has finally passed Java in the programming language popularity wars, propelled by its heavy use in machine learning and data science projects. "Python has reached 8.2 million active developers and has taken the No. 2 spot, surpassing Java in terms of popularity," says the brand-new "Developer Economics State of the Developer Nation 16th Edition" report in which SlashData Ltd. polled more than 19,000 developers in 165 countries. A previous edition of the survey last fall predicted that Python would overtake Java, stating: "Python has reached 7 million active developers and is closing in on Java in terms of popularity, thanks to 62 percent of machine learning developers and data scientists who now use Python." The new report sees that "closing in" prediction coming true, noting that Python "is the second-fastest growing language community in absolute terms with 2.2 million net new Python developers in 2018. The rise of machine learning is a clear factor in its popularity. A whopping 69 percent of machine learning developers and data scientists now use Python (compared to 24 percent of them using R)."

A new survey of software engineers from careers firm Hired shows they want to learn machine learning, love Python, hate PHP and make a lot of money, especially in San Francisco where search engineer salaries average about $157,000. Furthermore, according to the 2019 State of Software Engineers Report, blockchain engineers are highly sought after, seeing a huge 517 percent year-over-year increase in demand, far exceeding the 132 percent increase in demand for No. 2, security engineers. Hired said it publishes the report to fuel career conversations among developers and to provide data they can use to achieve goals. Topping the list of those goals is learning machine learning. "Machine learning is the No. 1 technology engineers want to learn," the report said.

While perhaps not offering new conclusions about the hottest technologies in the software development arena, a new Developer Economics survey from SlashData provides more hard evidence that data science and machine learning are the top skills developers want to learn. SlashData describes itself as an analyst for the developer economy, helping enterprises understand software developer audiences and measure the ROI of their developer strategies. To that end, it recently conducted the 15th edition of its Developer Economics survey of more than 20,500 developers in 167 countries. "Data science is the top skill to learn in 2019," SlashData said. It noted that 45 percent of developers want to gain expertise in data science and machine learning, with other most-wanted skills including UI design (33 percent) and cloud-native development (25 percent).

Remember when Big Data used to be all about Hadoop, and then Spark, Kafka and other tools, evolving into real-time analysis and other breakthroughs to help enterprises glean important business insights from their accumulated information? The latest case in point is MapR Technologies -- one of the original "Big Three" Hadoop-based vendors -- yesterday announcing a "major data platform update for AI and analytics." MapR used to describe itself in news releases as the "provider of the top-ranked distribution for Apache Hadoop" and now says it offers "the industry's leading data platform for AI and analytics." That shift to AI is also reflected in various ways by the other two members of the "Big Three," Cloudera -- "the modern platform for machine learning and analytics optimized for the cloud" -- and Hortonworks, which has detailed "our bigger strategy to help clients advance toward artificial intelligence (AI) through the development of a data platform." For its part, MapR yesterday said ""Customers have made it clear that traditional approaches to managing and processing data for AI and analytics leave critical gaps.

Artificial intelligence (AI) breakthroughs continue to be infused in developer tooling, with a new machine learning framework for the upcoming iOS 12 being the latest example. At the ongoing 2018 Apple Worldwide Developers Conference in San Jose, Calif., the company introduced the new Core ML 2 framework, which can be used by developers in a variety of ways across a variety of applications on Apple's flagship mobile OS, available now in preview to members of the $100-per-year Apple Developer Program and later this month in a public beta and coming to all in a fall device software update. Apple said the framework allows for easy integration of machine learning models, helping developers build intelligent apps with a minimum of code. "In addition to supporting extensive deep learning with over 30 layer types, it also supports standard models such as tree ensembles, SVMs, and generalized linear models," Apple said. "Because it's built on top of low level technologies like Metal and Accelerate, Core ML seamlessly takes advantage of the CPU and GPU to provide maximum performance and efficiency. You can run machine learning models on the device so data doesn't need to leave the device to be analyzed."