Splice Machine, a startup that offers offline and batch analysis tools to power intelligent apps for operational workflows, today launched version 3.1 of its platform. Splice Machine 3.1 introduces new features and functionality to support enterprises with real-time AI projects, including resource elasticity support on Kubernetes, GPU support, and extensions to Spark's machine learning libraries. Most companies struggle to develop working AI strategies. According to a recent survey by Rackspace, only 20% of enterprises report having mature AI and machine learning initiatives. Indeed, while Deloitte says 62% of respondents to its corporate October 2018 report deployed some form of AI, roughly 25% of companies see half their AI projects ultimately fail.

The amount of attention paid to Kubernetes has increased substantially over the past couple of years. What started out as a relatively obscure container management system open sourced by Google has turned into the must-have technology for running machine learning and advanced analytics applications, among other workloads. But is Kubernetes the real deal? Will K8s deliver on the hype, or turn into just another once-shiny thing that lost its luster? Kubernetes certainly seems to be the right technology for the right time.

Splice Machine develops a machine learning-enabled SQL database that is based on a closely engineered collection of distributed components, including HBase, Spark, and Zookeeper, not to mention H2O, TensorFlow, and Jupyter. Customers use it to build complex AI apps that include transactional, analytical, and ML components. The company just announced a Kubernetes operator for customers running in private cloud environments. Zweben said during a demo of Splice Machine's Kubernetes Ops Center. "When you pause on Splice Machine, it drains Kubernetes nodes and makes them available for other applications to use." Support for Kubernetes is not new at Splice Machine.

In retail, supply chain efficiency is essential. Inventory management, picking, packing and shipping are all time and resource-intensive processes which can have a dramatic impact on a business's bottom line. The problem is these are complex processes, particularly when it comes to large scale operations covering multiple outlets and territories. The fact they are often dependent on outside forces – suppliers, service providers and even weather – make getting it right even more difficult. This is why retailers – both big and, increasingly, smaller operations too – are keen adopters of Big Data-driven analytics technology. Creating efficiencies in complex systems which involve multiple, often compartmentalized processes is an area where this technology excels. In short, it's about the ability of machines to make lots of little savings and efficiencies, which together add up to very large ones. Monte Zweben – CEO of Splice Machine, which provides predictive systems for industry, talked me through three key areas where retailers are increasingly looking towards data-driven analytics in order to drive efficiencies in their supply chains. We also talked about why this approach is going to become increasingly important for businesses in all sectors which want to stay ahead of the pack and foster innovation. Filling your customers' needs more quickly Today's Internet of Things industry means that everything is connected and capable of collecting and sharing data on how it is operating. This means that everything can be measured and – through the use of advanced analytics tools such as machine learning – rigorously interrogated until it gives up all its secrets on how it works, and, crucially, how it interacts with every other part of an operation. All of that data can be collected on an inventory – origins, transit routes, times when it is scanned or its location and status are reported by RF (Radio Frequency) tags. "So, now you can build a machine learning model," Zweben says, "and that model could make a prediction about any aspect of the operation based on the data it's got. "What's the likelihood you're not going to be late with this order? What's the likelihood you'll be a day late? Five days? It's basically a classification problem." This means that in-depth simulations can be run, allowing the implications and knock-on effects of lateness or missed deadlines to be assessed before they become an issue, even if they can't be entirely eliminated due to a reliance on external influences. Where this is the case, remedial action can be taken ahead of inconvenience being caused to customers, who are certainly likely to be appreciative of an email apology when a shipment is likely to be delayed, rather than simply to be kept waiting.

Machine learning isn't a new concept, and you don't need to tell that to Monte Zweben. He's been involved in artificial intelligence research for 30 years and calls himself an "old school AI person." However, recent developments and a flood of new companies offering machine learning-powered applications have made the technology more accessible than ever. Zweben has previously worked as co-manager of NASA's principal artificial intelligence laboratory and is now CEO of Splice Machine, a SQL-on-Hadoop database company in San Francisco, working on a machine learning platform. As DevOps is slowly taking over the IT landscape, its vital that IT pros understand it before jumping right into the movement.

Once heavily invested in the AI and machine learning systems that helped run the NASA Space Shuttle, Splice Machine CEO Monte Zweben is trying to overhaul the structure that lies at its very core. Despite 2017 being described as the breakthrough year for machine learning and AI, the process by which computers learn complex skills and functions without human intervention is nothing new. One person who can attest to that is Monte Zweben, CEO of Splice Machine, who in a previous life was the deputy branch chief of NASA Ames Research Center's artificial intelligence (AI) hub. Spending seven years there during the 1980s and 1990s, Zweben and the rest of his team were using machine learning, not only to discover cosmic phenomena through radio telescopes, but also to maintain and plan the famous space shuttle missions. This, Zweben said in conversation with Siliconrepublic.com,

On a recent visit to the doctor, Edward Feigenbaum had the eerie experience of seeing one of his inventions used in a way he never expected: His 25-year-old concept was being used to diagnose a problem with his own breathing. "It's using artificial intelligence," the doctor patiently explained about the spirometer, which measures airflow. A professor of computer science and co-scientific director of the Knowledge Systems Laboratory at Stanford University, Feigenbaum is a pioneer of artificial intelligence (AI) -- the science of making machines think like humans. Dozens of applications have their roots in the Stanford lab he started in 1965 and in related software programs that solve complex problems the same way human experts do. Feigenbaum was the first person to realize that human intelligence springs not from rules of logic but from knowledge about particular problems (whether it's chemistry or auto mechanics) and about the world in general.

While "big data" can be a misunderstood buzzword in tech, there's no denying that the recent AI and machine learning push is dependent on the labeling and synthesis of huge amounts of training data. A new trend report by advisory firm Ovum predicts that the big data market--currently at $1.7 billion--will swell to $9.4 billion by 2020. So what do data insiders see happening in the coming year? TechRepublic spoke to several leaders in this field to find out. Here are five big data trends to watch in 2017, from the experts.

Most AI computing happens in data centers packed with hundreds or thousands of servers. Get ready for AI to show up where you'd least expect it. In 2016, tech companies like Google, Facebook, Apple and Microsoft launched dozens of products and services powered by artificial intelligence. Next year will be all about the rest of the business world embracing AI. Artificial intelligence is a 60-year-old term, and its promise has long seemed like it was forever over the horizon.

NASA's AI program has implemented Rather, it is to attempt to describe the lessons learned in the process of putting the program in setting up and carrying out the first together and carrying it out. Research and Development Program at the Did the plan work? How did National Aeronautics and Space Administration the program readjust? This AI program is sponsored by faced, and how would they be handled differently NASA's Office of Aeronautics and Space Technology. What are the heuristics used to The program conducts research and keep NASA's AI ship afloat in the churning development at the NASA centers (Ames, seas of government politics? It team never got lost in the process of setting also sponsors research in academia and industry, up the AI program, there were a few times primarily through Ames Research Center, when it was temporarily directionally disoriented. There were encounters with the NASA. The AI group at Ames, which is headed unforeseen that called for real-time reactive by Peter Friedland, has particular strengths in replanning.