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BrainChip Showcases Vision and Learning Capabilities of its Akida Neural Processing IP and Device at tinyML Summit 2020 – Yahoo Finance

BrainChip Holdings Ltd. (ASX: BRN), a leading provider of ultra-low power, high-performance edge AI technology, today announced that it will present its revolutionary new breed of neuromorphic processing IP and Device in two sessions at the tinyML Summit at the Samsung Strategy & Innovation Center in San Jose, California February 12-13.

In the Poster Session, "Bio-Inspired Edge Learning on the Akida Event-Based Neural Processor," representatives from BrainChip will explain to attendees how the companys Akida Neuromorphic System-on-Chip processes standard vision CNNs using industry standard flows and distinguishes itself from traditional deep-learning accelerators through key features of design choices and bio-inspired learning algorithm. These features allow Akida to require 40 to 60 percent fewer computations to process a given CNN when compared to a DLA, as well as allowing it to perform learning directly on the chip.

BrainChip will also demonstrate "On-Chip Learning with Akida" in a presentation by Senior Field Applications Engineer Chris Anastasi. The demonstration will involve capturing a few hand gestures and hand positions from the audience using a Dynamic Vision Sensor camera and performing live learning and classification using the Akida neuromorphic platform. This will showcase the fast and lightweight unsupervised live learning capability of the spiking neural network (SNN) and the Akida neuromorphic chip, which takes much less data than a traditional deep neural network (DNN) counterpart and consuming much less power during training.

"We look forward to having the opportunity to share the advancements we have made with our flexible neural processing technology in our Poster Session and Demonstration at the tinyML Summit," said Louis DiNardo, CEO of BrainChip. "We recognize the growing need for low-power machine learning for emerging applications and architectures and have worked diligently to provide a solution that performs complex neural network training and inference for these systems. We believe that as a high-performance and ultra-power neural processor, Akida is ideally suited to be implemented at the Edge and IoT applications."

Akida is available as a licensable IP technology that can be integrated into ASIC devices and will be available as an integrated SoC, both suitable for applications such as surveillance, advanced driver assistance systems (ADAS), autonomous vehicles (AV), vision guided robotics, drones, augmented and virtual reality (AR/VR), acoustic analysis, and Industrial Internet-of-Things (IoT). Akida performs neural processing on the edge, which vastly reduces the computing resources required of the system host CPU. This unprecedented efficiency not only delivers faster results, it consumes only a tiny fraction of the power resources of traditional AI processing while enabling customers to develop solutions with industry standard flows, such as Tensorflow/Keras. Functions like training, learning, and inferencing are orders of magnitude more efficient with Akida.

Tiny machine learning is broadly defined as a fast growing field of machine learning technologies and applications including hardware (dedicated integrated circuits), algorithms and software capable of performing on-device sensor (vision, audio, IMU, biomedical, etc.) data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of always-on use-cases and targeting battery operated devices. tinyML Summit 2020 will continue the tradition of high-quality invited talks, poster and demo presentations, open and stimulating discussions, and significant networking opportunities. It will cover the whole stack of technologies (Systems-Hardware-Algorithms-Software-Applications) at the deep technical levels, a unique feature of the tinyML Summits. Additional information about the event is available at https://tinymlsummit.org/

Follow BrainChip on Twitter: https://twitter.com/BrainChip_inc Follow BrainChip on LinkedIn: https://www.linkedin.com/company/7792006

About BrainChip Holdings Ltd (ASX: BRN)

BrainChip is a global technology company that has developed a revolutionary advanced neural networking processor that brings artificial intelligence to the edge in a way that existing technologies are not capable. The solution is high performance, small, ultra-low power and enables a wide array of edge capabilities that include local training, learning and inference. The Company markets an innovative event-based neural network processor that is inspired by the spiking nature of the human brain and implements the network processor in an industry standard digital process. By mimicking brain processing BrainChip has pioneered a spiking neural network, called Akida, which is both scalable and flexible to address the requirements in edge devices. At the edge, sensor inputs are analyzed at the point of acquisition rather than transmission to the cloud or a datacenter. Akida is designed to provide a complete ultra-low power AI Edge Network for vision, audio and smart transducer applications. The reduction in system latency provides faster response and a more power efficient system that can reduce the large carbon footprint datacenters. Additional information is available at https://www.brainchipinc.com

View source version on businesswire.com: https://www.businesswire.com/news/home/20200207005137/en/

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VUniverse Named One of Five Finalists for SXSW Innovation Awards: AI & Machine Learning Category – Yahoo Finance

Company to Demonstrate Live at Finalists Showcase in Austin, TX on Saturday, March 14

NEW YORK, Feb. 5, 2020 /PRNewswire/ -- VUniverse, a personalized movie and show recommendation platform that enables users to browse their streaming services in one appa channel guide for the streaming universe, announced today its been named one of five finalists in the AI & Machine Learning category for the 23rd annual SXSW Innovation Awards.

The SXSW Innovation Awards recognizes the most exciting tech developments in the connected world. During the showcase on Saturday, March 14, 2020, VUniverse will offer first-look demos of its platform as attendees explore this year's most transformative and forward-thinking digital projects. They'll be invited to experience how VUniverse utilizes AI to cross-reference all streaming services a user subscribes to and then delivers personalized suggestions of what to watch.

"We're honored to be recognized as a finalist for the prestigious SXSW Innovation Awards and look forward to showcasing our technology that helps users navigate the increasingly ever-changing streaming service landscape," said VUniverse co-founder Evelyn Watters-Brady. "With VUniverse, viewers will spend less time searching and more time watching their favorite movies and shows, whether it be a box office hit or an obscure indie gem."

About VUniverse VUniverse is a personalized movie and show recommendation platform that enables users to browse their streaming services in one appa channel guide for the streaming universe. Using artificial intelligence, VUniverse creates a unique taste profile for every user and serves smart lists of curated titles using mood, genre, and user-generated tags, all based on content from the user's existing subscription services. Users can also create custom watchlists and share them with friends and family.

Media Contact Jessica Cheng jessica@relativity.ventures

View original content:http://www.prnewswire.com/news-releases/vuniverse-named-one-of-five-finalists-for-sxsw-innovation-awards-ai--machine-learning-category-300999113.html

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Global Machine Learning Market to garner returns to garner returns worth USD 20.83 Billion by 2024 – Northwest Trail

The Global Machine Learning Market is Set for a Rapid Growth and is Expected to Reach around USD 20.83 Billion by 2024 Research Report provides the newest industry data and industry future trends, allowing you to identify the products and end users driving Revenue growth and profitability. A leading market research firm,Zion Market Researchadded industry report onMachine Learning Marketconsisting of 110+ pages with TOC (Table of Contents) including a list of tables & figuresduring the forecast period and Machine Learning Market report offers a comprehensive research updates and information related to market growth, demand, opportunities in the global Machine Learning Market.

FREE | Request Sample Report of Machine Learning Market Report @www.zionmarketresearch.com/sample/machine-learning-market

Our Free Complimentary Sample Report Accommodate a Brief Introduction of the research report, TOC, List of Tables and Figures, Competitive Landscape and Geographic Segmentation, Innovation and Future Developments Based on Research Methodology

The global Machine Learning Market report offers an extensive analysis of the realistic data collected from the global Machine Learning Market. It demonstrates major drifts and key drivers playing an important role in the growth of the global Machine Learning Market during the foretold time. The report focuses on the analysis of the key features such as drivers, new development opportunities, and restraints influencing the expansion of the Machine Learning Market for the forecasted period.

The report covers a detailed analysis of the development of the Machine Learning Market for the upcoming time. The global Machine Learning Market is segmented based on the various product categories, delivery channels, and applications.

Major Market Players Included in This Report:

International Business Machines Corporation, Microsoft Corporation, Amazon Web ServicesInc., BigmlInc., Google Inc., Hewlett Packard Enterprise Development Lp, Intel Corporation, and others.

A complete value chain of the global Machine Learning Market is emphasized in the global Machine Learning Market report along with the review of the downstream and upstream components influencing the global Machine Learning Market. It analyzes the expansion of every segment of the Machine Learning Market. The data presented in the research report is collected from various industry organizations to estimate the development of each segment of the global Machine Learning Market in the coming period.

The global Machine Learning Market research report presents market dynamics and inclinations influencing the growth of the global Machine Learning Market. It uses SWOT analysis to review the competitive players of the Machine Learning Market. Furthermore, the report also includes a synopsis of the various business strategies of the key players of the Machine Learning Market.

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Promising Regions & Countries Mentioned In The Machine Learning Market Report:

The report focuses on the latest market trends and major growth opportunities assisting in the expansion of the global Machine Learning Market. On the basis of topography, the global Machine Learning Market is classified into Europe, North America, Latin America, Middle East & Africa, and the Asia Pacific.

The Machine Learning Market report provides company market size, share analysis in order to give a broader overview of the key players in the market. Additionally, the report also includes key strategic developments of the market including acquisitions & mergers, new product launch, agreements, partnerships, collaborations & joint ventures, research & development, product and regional expansion of major participants involved in the market on the global and regional basis.

Browse Press Release:www.zionmarketresearch.com/news/machine-learning-market

Following 15 Chapters represents the Machine Learning Market globally:

Chapter 1,enlist the goal of global Machine Learning Market covering the market introduction, product image, market summary, development scope, Machine Learning Market presence;

Chapter 2,studies the key global Machine Learning Market competitors, their sales volume, market profits and price of Machine Learning Market in 2018 and 2026;

Chapter 3,shows the competitive landscape view of global Machine Learning Market on the basis of dominant market players and their share in the market growth in 2018 and 2026;

Chapter 4,conducts the region-wise study of the global Machine Learning Market based on the sales ratio in each region, and market share from 2018 to 2026;

Chapter 5,6,7,8 and 9demonstrates the key countries present in these regions which have revenue share in Machine Learning Market;

Chapter 10 and 11describes the market based on Machine Learning Market product category, a wide range of applications, growth based on market trend, type and application from 2018 to 2026;

Chapter 12shows the global Machine Learning Market plans during the forecast period from 2018 to 2026 separated by regions, type, and product application.

Chapter 13, 14, 15mentions the global Machine Learning Market sales channels, market vendors, dealers, market information and study conclusions, appendix and data sources.

Inquire more about this report @www.zionmarketresearch.com/inquiry/machine-learning-market

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Country-level bifurcation of data in terms of Product Type (Concentration, Temperature, Combustion, Conductivity, and Others) and Application (Petrochemical, Metallurgy, Electricity, and Others) for any specific country/countries.

Expansion of scope and data forecasts until 2030

Company Market Share for specific country/countries and regions

Customized Report Framework for Go-To Market Strategy

Customized Report Framework for Merger & Acquisitions and Partnerships/JVs Feasibility

Customized Report Framework for New Product/Service Launch and/or Expansion

Detailed Report and Deck for any specific Company operating in Machine Learning Market

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AI Hype: Why the Reality Often Falls Short of Expectations – insideBIGDATA

In this special guest feature, AJ Abdallat, CEO of Beyond Limits, takes a look at the tech industrys hype cycle, in particular for how it often falls short of expectations when related to AI. Beyond Limits is a full-stack Artificial Intelligence engineering company creating advanced software solutions that go beyond conventional AI. Founded in 2014, Beyond Limits is transforming proven technologies from Caltech and NASAs Jet Propulsion Laboratory into advanced AI solutions, hardened to industrial strength, and put to work for forward-looking companies on earth.

Despite what we see in science fiction, artificialintelligence (AI) is not likely going to produce sentient machines that will takeover Earth, subordinate human beings, or change the hierarchy of the planets foodchain. Nor will it be humanitys savior.

AI essentially equates to the ability of machines to performtasks that usually require human reasoning. The concept of artificialintelligence has existed for more than 60 years, and modern AI systems arerevolutionizing how people live and work. However, conventional AI solutions donot use the technology to its fullest potential.

Decisions are usually made inside black boxes

Conventional AI solutions operate inside black boxes, unableto explain or substantiate their reasoning or decisions. These solutions dependon intricate neural networks that are too complex for people to understand. Companiesutilizing conventional AI approaches primarily are in somewhat of a quandarybecause they dont know how or why the system produces its conclusions, and mostAI firms refuse to divulge, or are unable to divulge, the inner workings oftheir technology.

However, these smart systems arent generally all thatsmart. They can process very large, complex data sets, but cannot employhuman-like reasoning or problem-solving. They see data as a series ofnumbers, label those numbers based on how they were trained, and depend onrecognition to solve problems. When presented with data, a conventional AIsystem asks itself if it has seen the information before and, if so, how itlabeled that data last time. It cannot diagnose or solve problems in real-timeunless it has the ability to communicate with human operators.

Scenarios do exist where AI users may not be as concerned about collecting information around reasoning because the consequences of a negative outcome are minimal, such as algorithms that recommend items based on consumers purchasing or viewing history. However, trusting the decisions of black box-oriented AI is extremely problematic in high-value, high-risk industries such as finance, healthcare, and energy where machines may be tasked to make recommendations on which millions of dollars, or the safety and well being of humans, hang in the balance.

Imperfect edge conditions complicate matters

Enterprises are increasingly deploying AI systems to monitor IoT devices in far-flung environments where humans are not always present, and internet connectivity is spotty at best; think highway cams, drones that survey farmlands, or an oil rig infrastructure in the middle of the ocean. One-quarter of organizations with established IoT strategies are also investing in AI.

Cognitive AI solves these problems

Cognitive AI solutions solve theseissues by employing human-like problem-solving and reasoning skills that letusers see inside the black box. They do not replace complex neural networks appliedby conventional solutions, but instead interpret their outputs and usenatural-language declarations to provide an annotated narrative that humans canunderstand. Cognitive AI systems understand how they solve problems and arealso aware of the context that makes the information relevant. So instead of beingasked to implicitly trust the conclusions of a machine, with cognitive AI, humanusers can actually obtain audit trails that substantiate the systems recommendationswith evidence, risk assessment, certainty, and uncertainty.

The level of explainability generatedby an AI system is based on its use case. In general, the higher the stakes,the more explainability is needed. A robust cognitive AI system should have theautonomy to adjust the depth of its explanations based on who is viewing theinformation and in what context.

Audit trails in the form of decisiontrees are one of the most helpful methods for illustrating the cognitive AI reasoningprocess behind recommendations. The top of a tree represents the minimum amountof information explaining a decision-process, while the bottom denotes explanationsthat go into the greatest amount of detail. For this reason, explainability isclassified into two categories, top-down or bottom-up.

The top-down approach is for endusers who dont require intricate details, only a positive or negative pointof reference about whether or not an answer is correct. For example, a managermay think that a panel on a solar farm isnt working properly and simply needsto know the status of the solar panel; a cognitive AI system could generate aprediction around how much energy the panel will generate in its currentcondition.

On the other hand, a bottom-upapproach would be more useful for engineers dispatched to fix the problem.These users could query the cognitive AI system at any point along its decisiontree and obtain detailed information and suggestions to remedy the problem.

If the ultimate expectation for AI is to live up to its promise of transforming society, human users must be comfortable with the idea of trusting machine-generated decisions. Cognitive, explainable AI makes this possible. It breaks down organizational silos and bridges gaps between IT personnel and non-technical executive decision-makers of an organization, enabling optimal effectiveness in governance, compliance, risk management and quality assurance, while improving accountability.

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The Top 7 Google Cloud Books You Need to Read in 2020 – Solutions Review

For companies that operate Google Cloud Platform deployments, books are an excellent resource for learning how to effectively take advantage of a cloud environment. To that end, weve listed the top seven Google Cloud books that you should add to your reading list below. These books are intended for beginners and experts alike and are written by authors with proficiency and/or recognition in operating Google Cloud.

If youre looking for a managed service provider to help you manage your Google Cloud deployments, you should check out our free MSP Buyers Guide! The guide contains profiles on the top cloud managed service providers for AWS, Azure, and Google Cloud, as well as questions you should ask vendors and yourself before buying. We also offer anMSP Vendor Map that outlines those vendors in a Venn diagram to make it easy for you to select potential providers.

by Ernesto Garbarino

Use this beginners guide to understand and work with Kubernetes on the Google Cloud Platform and go from single monolithic Pods (the smallest unit deployed and managed by Kubernetes) all the way up to distributed, fault-tolerant stateful backing stores. You need only a familiarity with Linux, Bash, and Python to successfully use this book. Proficiency in Docker or cloud technology is not required. You will follow a learn-by-doing approach, running small experiments and observing the effects.

by Ted Hunter, Steven Porter, and Legorie Pajan

You will get started by learning how to use App Engine to access Googles scalable hosting and build software that runs on this framework. With the help of Google Compute Engine, youll be able to host your workload on virtual machine instances. The later chapters will help you to explore ways to implement authentication and security, Cloud APIs, and command-line and deployment management.

by Jose L. Ugia Gonzalez and S. P. T. Krishnan

Building Your Next Big Thing with Google Cloud Platform shows you how to take advantage of the Google Cloud Platform technologies to build all kinds of cloud-hosted software and services for both public and private consumption. Whether you need a simple virtual server to run your legacy application or you need to architect a sophisticated high-traffic web application, Cloud Platform provides all the tools and products required to create innovative applications and a robust infrastructure to manage them.

by Judy Raj, Janani Ravi, and Vitthal Srinivasan

The book guides you on how to scale your system and boost its security, helping you become a skilled high-level cloud architect. As you progress, you will discover how individual cloud services are configured and used. The later chapters will even provide you with insights into the best GCP services and help you understand how and when to use them, regardless of how big or small your infrastructure is.

by JJ Geewax

Author and Google software engineer JJ Geewax is your guide as you try everything from hosting a simple WordPress web app to commanding cloud-based AI services for computer vision and natural language processing. Along the way, youll discover how to maximize cloud-based data storage, roll out serverless applications with Cloud Functions, and manage containers with Kubernetes.

by Marc Cohen, Kathryn Hurley, and Paul Newson

Learn how to run large-scale, data-intensive workloads with Compute Engine, Googles cloud platform. Written by Google engineers, this tutorial walks you through the details of this Infrastructure as a Service by showing you how to develop a project with it from beginning to end. Youll learn best practices for using Compute Engine, with a focus on solving practical problems.

by Cesar Anton Dorantes

We will explore G Suite tools in depth so you and your team get everything you need -combination of tools, settings and practices- to succeed in an intuitive, safe and collaborative way. While learning G Suite tools you will also learn how to use Google Sites to create from your corporate site to internal tools, live reports that seamlessly integrate with documents, and external Services.

Looking for more info on managed service providers for your cloud solutions? OurMSP Buyers Guidecontains profiles on the top cloud managed service providers for AWS, Azure, and Google Cloud, as well as questions you should ask vendors and yourself before buying. We also offer anMSP Vendor Mapthat outlines those vendors in a Venn diagram to make it easy for you to select potential providers.

Check us out onTwitterfor the latest in Enterprise Cloud news and developments!

Dan is a tech writer who writes about Enterprise Cloud Strategy and Network Monitoring for Solutions Review. He graduated from Fitchburg State University with a Bachelor's in Professional Writing. You can reach him at dhein@solutionsreview.com

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Teresa Carlson, AWS Worldwide Public Sector VP, Named to 2020 Wash100 for Cloud Innovation, Business Expansion and New Educational Platforms -…

Teresa Carlson

Executive Mosaic is honored to present Teresa Carlson, vice president of the worldwide public sector business at Amazon Web Services, as an inductee into the 2020 edition of the Wash100 Award for driving cloud innovation, expanding AWS' influence and advocating for cloud computing education within Virginia schools.

This marks Carlsons sixth consecutive Wash100 Award. She secured her 2019 Wash100 Award for modernizing cloud-based platforms and applications to inspire innovation in government markets. Carlsons major initiative in 2018 was integrating on-demand security evaluation service designed to improve the security of the Amazon Elastic Compute Cloud platform.

Carlson has continued to push for improvement on cloud data. She has utilized AWS technology services to enhance government and industry platforms. Most notably, Carlson helped renew a potential $130.2 million contract to provide public cloud hosting services to the U.K.'s Home Office in Jan. 2020.

AWS began work under the extended contract on Dec. 12, 2019, and will continue to provide cloud technology under Carlsons lead throughout 2023. AWS has secured $59.1 million of the total Home Offices spending on cloud services.

Not only has Carlson expanded the company through major contract awards, but she also orchestrated the partnership between AWS and Tyler Technologies in Oct. 2019, enabling the company to develop more extensive cloud services.

Carlson has helped lead the strategic collaboration agreement, and provided expansive cloud technologies and training services to the company. The partnership will continue to broaden the presence of AWS next-generation cloud services and Carlsons team intends to assist government clients design, manage and deliver their programs through AWS services. Work will take place at Tyler-operated data centers throughout 2020.

AWSs strategic collaboration with Tyler extends our relationship and encourages greater engagement for the benefit of public sector customers, said Carlson.

While Carlson has supervised the new partners within the industry, she has also broken ground within the government contracting sector. In June 2019, she announced that AWS will continue to work with the Department of Defense (DoD) and the intelligence community to integrate cloud technology into the federal government and strengthen the departments information processing systems, security and defense capabilities.

AWS, along with our partner community, stands ready to support and serve the most important DoD and intelligence communities mission of protecting and serving our country, Carlson noted.

Elsewhere, Carlson has continued to create new educational platforms to combat the shortage of a specialized cloud workforce and continue to grow cloud integration for federal agencies and industry.

In Sept. 2019, Carlson led the partnership between AWS and the Virginia community to implement its cloud computing curriculum to select universities within the state.

This is about quickly getting into the environment and creating economic development, for your family, your community, the state and for the United States, said Carlson.

With the addition, AWS has crafted a curriculum to grow cloud computing jobs in Virginia through the AWS Educate program. AWS educational program is comprised of grant initiatives for K-12 computer science programs and online cloud computing course at George Mason University.

Carlson created another educational program in June 2019. The program has enabled students to access the AWS Educate technology training and employment resources program that supports around 1,500 academic entities. Carlson said that the educational effort ensures that institutions are teaching relevant skills that would cut time alloted for retraining.

The new program will build on NOVA's associate degree curriculum that covers 63 credits of cloud services and other required AWS skills and competency-based credentials, Carlson noted.

Executive Mosaic congratulates AWS and Teresa Carlson for her 2020 Wash100 Award. Carlsons expansive cloud integration, valuable contracts, education platforms and partnerships proves that she is a notable figure in the GovCon industry and a leader in the ever-expanding tech industry.

About The Wash100

This year represents our sixth annual Wash100 Award selection. The Wash100 is the premier group of private and public sector leaders selected by Executive Mosaics organizational and editorial leadership as the most influential leaders in the GovCon sector. These leaders demonstrate skills in leadership, innovation, achievement, and vision.

Visit the Wash100 website, where viewers can submit 10 votes for the executives of consequence they believe will have the most significant impact in 2019.

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Cloud Technology Solutions CEO on keeping pace with Google Clouds growth – NS Tech

Back in 2008, when working on a programme for The Economist to migrate to Google Apps, James Doggart and his colleague realised there was no migration technology to allow organisations to easily switch over. It was on this basis that they formed Cloud Technology Solutions (CTS), and it started trading in 2009-2010.

Initially, the organisation began as a software provider. Under the CloudM brand, it developed software to help organisations benefit from shifting to G-Suite or Office 365. To date, it has migrated more than 11.5 million users and 23,000 domains to the cloud across 84 countries. Among the 35,000+ companies that the organisation has helped either with CloudM or with managed services are Netflix, Spotify, LinkedIn, Salesforce and the Cabinet Office.

Now, more than a decade on, CTS has 185 employees based in offices in Manchester, Edinburgh, Utrecht and Frankfurt. While the company focuses on both Microsoft and Google within its software division, for its managed services side, the company is very much focused on Google it is the largest dedicated Google Cloud practice in Europe.

Co-founder and CEO of the company, Doggart, tells NS Tech that his team were surprised with how long the market has taken to lean towards the cloud.

As our name suggests, we were born in the cloud. We realised that traditional IT was going to suffer and struggle with the distribution model of what cloud represented. It is a similar dynamic to the traditional retailer against e-commerce companies because it isnt just about technology but the business model has to change, he says.

Doggart explains that what compounded an already complicated task of persuading IT leaders to shift infrastructure to the cloud, was that traditional vendors started rebranding other elements of IT such as hosting as cloud confusing the market further.

What really enabled CTS to gather momentum was when Google started to heavily invest in the cloud space.

They realised they could get something significant out of it revenue-wise. They already had the largest infrastructure available but they werent exposing it to customers. I think its the fourth biggest server manufacturer on the planet but it only manufactured the servers for itself. So giving access to customers for that infrastructure started happening, he says.

It only started in earnest in the last three to four years, and the Google Cloud organisation went from 2,000 employees to 20,000 and so people started taking Google seriously for the enterprise in that it shifted from being a consumer-only company to an enterprise company too, he adds.

A big shift, has also been the realisation from organisations that cloud isnt just about a lift and shift approach or cost-cutting, but rather the exploitation of data, and the use of more powerful tools such as machine learning and artificial intelligence.

Google brought in $8.9bn in cloud revenue in 2019, a 53 per cent increase from 2018. The company disclosed quarterly and annual revenue for the first time this week, and despite the huge amount of revenue and growth, the company still lags behind the top two Amazon Web Services (AWS) and Microsoft Azure. For context, AWS brought in more cloud revenue in the most recent quarter $9.9bn than Google did in the whole of 2019.

Doggart believes its still very early days in the cloud computing space and that the investment Google is making will make a difference in the years to come. Indeed, AWS CEO Andy Jassy recently said that only three percent of all IT workloads are in the cloud.

Doggart says that in order to keep up with the investment and growth of Googles cloud proposition, he has had to scale up his workforce in the last 24 months this has included an acquisition of Netherlands-based application development and machine learning company Qlouder in 2018.

CTS manages the Cabinet Offices G-Suite estate, as well as having 20 local government customers. When asked whether anything had changed when it came to dealing with government over the years, Doggart referred to 2010, when there had been encouragement from the government for departments to work with small and medium-sized businesses (SMBs) more.

We were winning work then that we wouldnt have won before 2010 because the big four professional services firms were taking a lot of business out of government especially central government. As well as encouraging them to work with SMBs, they also paid suppliers more promptly, he says.

Since then, he believes the government has not changed at all in the way it works with SMBs it hasnt gone backwards or forwards.

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7 online courses that will help you improve your technical skills – Ladders

In todays workplace, constant innovation makes it essential for employees to keep up with the latest technologies and applications. Want to excel at your job and lay the groundwork for future promotions?

Check out these seven e-courses from LinkedIn Learning, part of the LinkedIn platform. Each course is available for a monthly site membership fee, with no extra costs necessary. With instruction on blockchain, cloud computing, UX and more, youll 10x your workplace performance and fast-track yourself for future success!

Instructor: Jonathan Reichental, Ph.D.

Do you work in business leadership positions, data science, or IT management? Curious about blockchain technology? This quick e-course will introduce you to the improvements in security and efficiency that blockchain databases represent. Learn about the history of blockchain technology, practical applications, and risks of blockchain innovation.

Instructor: David Linthicum, Deloitte Consulting

Looking to explore the potentialities of cloud computing? Take an hour to enroll in this quick course, which will introduce you to the tools you need to take advantage of cloud computing opportunities. Find out what Saas, laaS, and PaaS are, learn about the data and applications necessary to move to the cloud, and explore the essentials of cloud monitoring, management, and security.

Instructor: Robin Hunt, developer and educator

This 90-minute course introduces basic concepts to those who work with data, both analysts and non-analysts alike. It begins by defining data analytics and the role of data analysts. Following this, it teaches you how to work with data sets and follow best practices for data analytics projects. Finally, it covers advanced techniques for repurposing, charting, and pivoting data and top shortcut and troubleshooting tips.

Instructor: Doug Rose

As one of the most promising fields in artificial intelligence, machine learning can be very helpful in interpreting and organizing data, programming computers, and even discovering new frontiers in science. This 75-minute course teaches you how to work with data and apply machine learning principles, as well as identify different types of machine learning and machine learning applications.

Instructor: Chris Nodder

Want to improve the customer experience on your e-commerce website or web interface? This micro-course introduces you to the elements of user experience (UX)including design, research, strategy, development, interactive and visual design, content, accessibility, localization, and data science. Learn about various UX career paths, the practical skills required by employers, and typical job profiles.

Instructor: Chris Bailey, technical architect

This e-course for software developers teaches how to go from merely hosting apps in the cloud to building and deploying cloud native apps with Node.js, Docker and Kubernetes. Centered on creating Node.js apps that capitalize on the clouds features, the course begins with an introduction to the concept of cloud native and segues into the steps involved in taking an existing Node.js app and packaging it with Docker, deploying the app to Kubernetes, and enhancing it with cloud native capabilities, including support for self-healing and metrics.

Instructors: Madecraft and John David Ariansen

This hour-long e-course introduces users to Power BI, a business intelligence technology that acts as a powerful data analytics tool. Start by identifying the difference between Power BI Desktop and the Power BI service, then continue by learning how to import and manipulate data. Finish with key skills like managing relationships, working with Data Analysis Expressions (DAX), and building visualizations and reports.

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Anurag Goel on Cloud Native Platforms, Developer Experience, and Scaling Kubernetes – InfoQ.com

In this podcast, Daniel Bryant sat down with Anurag Goel, Founder and CEO of Render. Topics covered included: the evolution of cloud platforms; simplifying developer experience; running large-scale workloads on Kubernetes; and the future of tooling and platforms within the cloud native computing space.

Subscribe on: Apple Podcasts Google Podcasts Soundcloud Spotify Overcast RSS Feed

Can you introduce yourself? -

What is your most typical use case you see online? -

How does this differ from classic IaaS or PaaS, with the spectrum of deployment approach spanning from "everything defined as code" to the Heroku-inspired "git push master"? -

Do you mean when you say that "legacy cloud" is focused on configuration? -

That's quite a claim in the reduction of team size! Have you got any public use cases you can share? -

I expect that Render is aiming towards automated DevOps and reducing toil? -

Why is now the right time to introduce another PaaS into the market? -

Do you run Render on a hosted Kubernetes service, or do you host it yourself? -

How are you using Kubernetes? -

Can I lift and shift my existing Kubernetes applications directly on to Render? -

Helm tries to lift the deployment abstraction above Kubernetes, but there are many other aspects of creating an application, such as building the code. Does Render have the concept of buildpacks? -

How do I connect services in the Render YAML? -

How does service discovery work in Render? -

Is there any concept of retries or circuit breaking at the network level? -

Can you do canary releases in Render, and if not, how do you do releases? -

How tricky is it for your team to offer a data store or a managed Redis in Kubernetes behind the scenes? -

What do you think about observability and understandability? -

As Render is building on top of CNCF components, what do you think of the CNCF landscape? -

How can technical leaders evaluate the choices between the cloud platforms and calculate the TCO? -

QCon is a practitioner-driven conference designed for technical team leads, architects, and project managers who influence software innovation in their teams. QCon takes place 8 times per year in London, New York, Munich, San Francisco, Sao Paolo, Beijing, Guangzhou & Shanghai. QCon London is at its 14th Edition and will take place Mar 2-5, 2020. 140+ expert practitioner speakers, 1600+ attendees and 18 tracks will cover topics driving the evolution of software development today. Visit qconlondon.com to get more details.

.From this page you also have access to our recorded show notes. They all have clickable links that will take you directly to that part of the audio.

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Anurag Goel on Cloud Native Platforms, Developer Experience, and Scaling Kubernetes - InfoQ.com

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Goodbye Google: Is it possible to live online without Google? – Stuff Magazines

While much of the fear and scorn around personal privacy and how Big Tech uses the data of its users are directed against Facebook, it tends to overlook what a central role in our lives is played by Google.

Google is by far the biggest search engine, provides the Android operating system used by over 90% of smartphones, is the largest provider of cloud-based email (through Gmail), is a major player in cloud hosting, and is dominant in mapping technology.

Ive long since discovered I can happily live without Facebook after I deleted it (and its Messenger app) off my iPhone two years ago. I havent missed anything.

Ive tried to move away from WhatsApp but have discovered that most people rely on this hugely popular messaging app, and they are willing to trade whatever privacy concerns for the convenience it offers.

But, as another privacy problem emerged from Google (it emailed videos to strangers in a glitch last November), it coincided with my efforts to try live without Google. Late last year, I began using another search engine the privacy orientated DuckDuckGo instead of Google. I set that as my default in my browsers and substituted the Google app itself for that from DuckDuckGo.

I can say that I havent been short-changed at all. Apart from a different look-and-feel, there is little to differentiate the search services Ive found. I still prefer some of the ways Google indexes news and photos, while its unified search page that shows a few items from each category is very useful. But I havent felt disadvantaged at all.

The hardest services to get unhooked from are Gmail, the excellent Maps and the thoroughly useful Photos. Like hundreds of millions of people, I host my email with Gmail and cant fault it for usability and functionality.

Of all the mapping services, I have always preferred Google Maps because its used by so many people it has the network effect on its side: The more people who use it, the better the service is.

Except, if youreBerlin-based artistSimon Weckert, who proved you canspoofa traffic jam by toying 99 smartphones around in a little red trolley.

What I particularly like about Maps is that you can save an offline version, which is particularly useful while travelling overseas because it doesnt use any data. Ive redownloaded Apple Maps and am trying that again.

Meanwhile, every geek agrees that Google Photos is the best service for archiving and organising your pictures. I recommend it to everyone who owns a smartphone because its so good especially if your phone is lost or stolen.

The problem with Maps and Photos is they want access to your location all the time. Why do they need it? If you follow Googles argument, they make it easier to plot your travels and identify where your photos were taken. My default setting for location-based requests from apps is only while using app because local info is relevant for some apps (Uber, maps, weather).

Gmail is unfortunately irreplaceable. I dont know any other cloud-based email service that is as good.

Ill let you know in a few months if its possible to live without Google, or how little of its services are the bare minimum.

This column first appeared inFinancial Mail

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Goodbye Google: Is it possible to live online without Google? - Stuff Magazines

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