Rolls-Royce develops an AI ethics framework and trust process, a UK consortium aims to bring quantum computing to the enterprise, and more. Keeping pace with news and developments in the real-time analytics market can be a daunting task. We want to help by providing a summary of some of the items our staff came across each week. Rolls-Royce has announced an AI ethics framework and trust process that can help gain society's trust of the technology and accelerate the next generation of industrialization, known as industry 5.0. The AI ethics framework is a method that any organization can use to ensure the decisions it takes to use AI in critical and non-critical applications are ethical.

"Supporting arrays, maps and structs allows customer to simplify data pipelines, unify more of their semi-structured data with their data warehouse as well as maintain better real world representation of their data from relationships between entities to customer orders with item level detail. A good example is groups of cell phone towers that are used for one call while driving on the highway." I have interviewed Mark Lyons, Director of Product Management at Vertica. We talked about the new Vertica 10.0 What is your role at Vertica? Mark Lyons: My role at Vertica is Director of Product Management. I have a team of 5 product managers covering analytics, security, storage integrations and cloud.

Description The Data Engineering team at Intuit's Small Business Group (SBG) is looking for a Senior Data Engineer – QE with a winning track record in Big Data, Data Warehousing, Visualization and Data Web Services. Responsibilities: Work with Data Engineers, Product Managers and Data Scientists to identify datasets needed for deep customer insights and for building operational propensity models. Work with data ingestion engineers to bring required source datasets into the data warehouse. Test ETL code to populate the dimensional model. Work with BI developers to ensure that the data warehouse is providing the required data and the required performance.

Machine learning and data science can deliver valuable insights from massive amounts of data. However, before the complex machine learning algorithms can be trained for prediction, you need to address the data exploration and preparation steps. Distributed analytical databases like Vertica can be help you address each step of the machine learning process with a combination of analytical capabilities and computational power. Join this Under the Hood webcast and see how Vertica's in-database advanced analytics and machine learning functions help you tackle data preparation and overcome the growing challenge of applying machine learning at scale.

Vertica is transforming the way organizations build, train and operationalize machine learning models. Are you ready to embrace the power of Big Data and accelerate business outcomes with no limits and no compromises?

Data Preparation for Machine Learning in Vertica Posted on Monday, May 8th, 2017 at 1:05 pm. This blog post was authored by Vincent Xu. Introduction Machine learning (ML) is an iterative process. From understanding data, preparing data, building models, testing models to deploying models, every step of the way requires careful examination and manipulation of the data. This is especially true at the beginning of this cycle where the raw data must be cleaned and prepared for modelling.

It's been a very big year for big data, and Hewlett Packard Enterprise Co. is helping enterprises get a handle on organizing and analyzing their data with tools that analyze in place and offer machine learning and cognitive capabilities, according to Jeff Veis, VP of marketing, big data platform, at Hewlett Packard Enterprise Co. Veis spoke to Dave Vellante (@dvellante) and Paul Gillin (@pgillin), co-hosts of theCUBE*, from the SiliconANGLE Media team, during HPE Discover EU, held in London. Veis also discussed HPE big data solutions, including Vertica, which enables users to conduct data analysis, regardless of where the data resides; IDOL, a software solution that provides a single environment for structured, semi-structured and unstructured data; and Haven, an on-demand platform of more than 60 advanced machine-learning APIs and services. One of the concepts Veis talked about was "analyze in place," or ways customers can get the value out of their datalake. With Vertica, they get the performance of a Vertica front end with the economy of Hadoop. Vertica's new Frontloader was released in September; it added MS Azure support, in addition to AWS.

Hewlett Packard Enterprise announced Tuesday the latest release of its Vertica analytics software, as well as new machine learning capabilities for Haven OnDemand. Vertica focuses on the analytics of structured information, such as data stored in rows and fields. As the latest iteration, Vertica 8, codenamed "Frontloader," introduces a unified architecture and new in-database analytics capabilities. As far and fast as cloud computing is embedding itself into the enterprise, there remain many cloud-resistant applications and services. HPE says Vertica 8 is designed to help businesses extract intelligence from data residing in multiple silos across the datacenter, including on-premise, private, and public clouds, and in Hadoop data lakes.

Vertica release improves performance, adds Hadoop and Spark support. Hewlett Packard Enterprise announced August 30 at its HPE Big Data Conference in Boston that it's making its library of machine learning services easier for developers to build into smart, "cognitive" applications through Haven OnDemand Combinations. In a second announcement at the event, HPE unveiled Vertica 8.0, the next release of the company's high-scale analytical database. Haven OnDemand is in the white-hot category of machine learning services. It's a domain that has seen dozens of acquisitions in recent years, led by leading tech companies including Amazon, Google, IBM, Intel, Microsoft and Salesforce. How will HPE differentiate Haven OnDemand as the big public cloud companies deepen their portfolios?

Machine learning has become a core component of companies' analytic initiatives and is no longer the "fine china" only brought out for special occasions, according to a manager with Hewlett-Packard Enterprise, which today announced that its Vertica analytics database now runs popular classes of machine learning algorithms. While previous versions of Vertica could run R algorithms -- as opposed to shipping them off to run on a Hadoop cluster or another adjacent system -- Vertica 8.0 will be the first version of the flagship columnar database that formally supports a broad collection of popular machine learning algorithms, according to Jeff Veis, vice president of marketing for Big Data Platforms at HPE (NYSE: HPE). "It used to be niche, or maybe like fine china for special occasions, to use machine learning, and now it's showing up as a must-have for almost all our customers," Veis tells Datanami. "It's becoming very important to do that form of advanced analytics. We brought that in-database so you can run it across your whole data set."