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Veeam reveals cloud storage use patterns: Azure and AWS for bulk, IBM has ardent fans, Google has not much – The Register

Data management software vendor Veeam has offered a snapshot (pardon the pun) of how its customers put different public clouds to work.

Senior vice president Anton Gostev's weekly missive to the Veeam community yesterday detailed how the November 2020 version 10 release of the vendor's software changed customer behaviour.

Gostev reported that of the five largest object storage repositories tended by Veeam, three were in Azure blob storage, on was in on-premises S3-compatible storage and the other was in Amazon S3. These individual object storage repositories were also five times larger than those Veeam had seen before, and ranged from from 1.8PB to 1PB.

The sizes dwindled from there, but remained significant the next ten repositories had over 500TB of backups, the next 30 over 300TB, the next 60 over 200TB and the next 200 over 100Tb.

And while these sizes are individual object storage repositories, customers could very well keep multiple repositories.

But that's enough about the biggest repositories. Surely they were just outliers and on average, customers are sticking within range, right? Nope. Most customers stored more data than they did the prior year as averages grew by about 50 per cent to 15TB and median value tripled to over 3TB.

Most of Veeam's users employ Amazon and Azure for their cloud storage. The few that chose IBM tended to trust Big Blue with more data than those who work in other clouds. Google customers were hard to find, which Gostev attributed to Veeam's V11 release still being new.

Customers with over 100TB were split evenly between cloud and S3-compatible on-prem storage. Dell EMC's ECS had the most on-prem object storage deployments, but this year had competition from Hitachi HCP and Scality.

Aside from the hyperscalers, the top S3-compatible cloud storage was Wasabi, which was third overall and is five times larger than Blackbaze, which had a better year due to S3 Object Lock API support. Customers didn't fully utilize that feature, as less than half of object repositories had the option enabled. Gostev chalked that up to customers trying to save a quid or two.

Gostev also revealed that work on Veeam V12 is under way, and he predicted it will drive on-prem object storage adoption due to the new version having support for backing up directly to object storage. The version will also allow adding multiple buckets to Capacity Tier and ensure they don't go beyond scalability thresholds.

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Veeam reveals cloud storage use patterns: Azure and AWS for bulk, IBM has ardent fans, Google has not much - The Register

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Global Cloud Storage Software Market expected to contribute to the growth of the segment over the forecast period 2021-2027 The UK Directory – The UK…

The Cloud Storage Software Market Research Report provides an up-to-date overviewof the present worldwide market scenario, as well as the most recent trends and drivers, as well as the overall market environment. Cloud Storage Software Advantages and growing applications in a variety of industries are driving the industry. Furthermore, the markets expansion is expected to be aided by the Growth benefits of Cloud Storage Software. This Market study is conducting using an Cloud Storage Software Market insights of primary and secondary information including inputs from key participants in the industry. The report contains a comprehensive market and vendor landscape in addition to an analysis of the key vendors. The Top Leading Key Players of Cloud Storage Software Market Industry areHPE, Amazon Web Services, Huawei Technologies, Microsoft, Oracle, Rackspace Hosting, Red Hat, IBM, Hitachi Data Systems, CA Technologies, Netapp, Dell EMC, Google, VMware.

Our Research Experts Represent a detailed Overview of the market by the way of study, synthesis, and summation of data from multiple sources by an analysis ofkey parameters such as profit, Growth, Loss Gross Margin, Current Demand Status, CAGR Value, Revenue Details, Latest Trends, COVID-19 Impact Analysis, pricing, competition, and promotions. It represents various market Features/facets by identifying the key industry influencers. The data presented is comprehensive, reliable, and a result of extensive research both primary and secondary. The Cloud Storage Software market research reports provide a complete competitive landscape and an in-depth Players selection methodology and analysis using qualitative and quantitative research to forecast the accurate market growth.

Download FREE PDF Sample Report including COVID-19 Impact Analysis

Dont Miss Out Business Opportunities inCloud Storage Software Competition and Learn Important Industry Outlook. Speak with one of our research experts, and they will provide a business report based on your research requirements (Fill PDF Sample Form)

Our Research Analystare givingdetaileddata and information on the Cloud Storage Software markets leading players associated business processes. A particular section about the COVID-19 situation is provided for future tactics and predictions.

The Cloud Storage Software market is segmented as below, on the basis of Product Type, Application and Geography

By Types (Revenue, USD Million, 2021 2027):-Private Cloud, Public Cloud, Hybrid Cloud

By Applications (Revenue, USD Million, 2021 2027):-BFSI, Government & Education, Healthcare, Telecom & IT, Retail, Manufacturing, Media & Entertainment, Others

By Geography

Here is FEW Point which Describe Cloud Storage Software Market Report Shortly

Executive Summary

Market Landscape

Market Sizing

Five Forces Analysis

Market Segmentation by Application

Customer landscape

Vendor Analysis

Appendix

Check the feasibility and get a full Report Insight in short for Cloud Storage Software industry:https://www.marketresearchstore.com/market-insights/cloud-storage-software-market-797736

Analyses of COVID 19 Impact

The global supply chain was disrupted by the COVID 19 epidemic, which proved to be a setback for the global market. Exotic substances and plant extracts play a big role in the Cloud Storage Software industry. As a result, the supply chain must function properly in order for producers to supply the market on a consistent basis. The COVID 19 pandemic created travel and logistical barriers that hampered the delivery of Cloud Storage Software. Furthermore, a scarcity of manufacturing personnel had an impact on the market. Peoples attention shifted away from cosmetics and toward other necessary items.

Industry analysts, on the other hand, predict the market will recover in the post-pandemic phase. This segments recuperation will be aided by a shift toward self-care and the usage of pharmaceutical items. The pandemic phase slowed growth by a few percentage points, but it will recover in the next years. The resumption of normalcy following mass vaccination is a favourable element for Cloud Storage Software market expansion.

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Alexander: Making sure that deleted Android phone pictures are really gone – Minneapolis Star Tribune

Q: I transfer photos from my Android phone (a Moto G Stylus) to my computer, then delete the pictures from the phone. But the photos don't stay deleted from the phone. Is there a way to permanently delete pictures?

TOM KROCAK, New Brighton

A: There are several possible reasons why photos aren't being deleted from your phone. Here are some of them:

These steps should prevent a photo from unexpectedly returning to your phone, but they won't guarantee that the same photo was erased from an online back-up service. Why? Some cloud storage software allows you to delete a photo from the phone without deleting the same picture online. To be completely rid of a photo, log in to your online back-up service and make sure the picture has been deleted there, too.

Q: How can I get rid of some scam pop-up ads on my PC? They say that my McAfee antivirus software subscription is about to expire, and I should renew it. The ad is usually followed by other pop-ups claiming my PC is infected.

GREG HILLSTROM, Sarasota, Fla.

A: This issue is so common that it's called the "your McAfee subscription has expired" problem. If you click "renew now" on the bogus ad, you'll be asked to enter personal data that can be used by scammers to steal your money or identity. You can get rid of the problem by downloading and running the free version of Malwarebytes, a security program (see tinyurl.com/2zym6ctk).

The question in these cases is always how you acquired the ad software. Your PC can pick it up from unscrupulous websites. Or you may have inadvertently downloaded "potentially unwanted programs" (PUPS) that can display ads, gather information stored on your PC or aid in the download of malware. To avoid getting PUPS, be careful when downloading legitimate software. Choose the "custom" or "advanced" download settings that let you opt out of any additional software that's "bundled" with the program you want. You'll see a list of what will be downloaded, and you can usually opt out by unchecking a box beside every unwanted program.

E-mail tech questions tosteve.j.alexander@gmail.comor write to Tech Q&A, 650 3rd Av. S., Suite #1300, Minneapolis, MN 55488. Include name, city and telephone number.

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Alexander: Making sure that deleted Android phone pictures are really gone - Minneapolis Star Tribune

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Cloud repatriation: Five reasons to repatriate data from cloud – ComputerWeekly.com

Moving to cloud computing is not necessarily the one-way street you would imagine. Although the cloud attracts an increasing percentage of enterprise IT spending a trend IT analysts expect to continue the cloud does not hold all the answers.

In some cases, organisations have found the need to move workloads and data back from the cloud so-called cloud repatriation.

Researchers at Forrester expect the public cloud infrastructure market to grow by 35% during 2021 to $120bn. This growth has been driven by the Covid-19 pandemic and, Forrester says, in particular by a move to cloud-based backup and recovery.

But even where the cloud is now the default choice for CIOs, enterprises also need to consider whether and when to move data back, or repatriate it, from cloud infrastructure. As yet, the number of organisations repatriating data is small, but data repatriation should be consideration in any cloud strategy.

With applications such as backup and recovery, the idea of moving data back is built in. But bringing data back on-premise can be driven by financial, practical or even regulatory considerations. Here we look at the main reasons for cloud repatriation.

Cloud computing is not always cheaper than on-premise options. And costs can change, because providers increase pricing, because requirements change or, often, because the organisation has underestimated some of the costs involved with operating in the cloud.

As an on-demand or pay-as-you-go service, higher cloud utilisation of storage or compute resources will mean a bigger bill. Organisations might find their projected storage requirements quickly exceed a budget. With on-premise systems, once the hardware is bought or leased, most costs will not change with utilisation.

With cloud, the more the service is used, the more it costs. This is the case with data storage generally, and with specific aspects such as data egress, costs for related resources such as security and management tools, or even database writes.

Another possibility is that the cloud provider could increase its fees. Depending on the contract, organisations could face rapid cost increases, potentially to the point where an on-premise option might be more economical.

Regulatory requirements should not be a reason to move data from the cloud, provided the migration was planned properly. And there is no inherent reason why a public cloud deployment would be less secure than on-premise architecture, as long as the correct security policies are followed and systems set up correctly.

Unfortunately, this is not always the case. Although security failures by public cloud providers are rare, misconfiguration of cloud infrastructure by customers is not uncommon. A data loss or breach could lead to the organisation deciding to move data back on-premise, even if only to minimise reputational damage.

When it comes to regulation, public cloud providers, including the hyperscalers, have taken steps to meet government and industry requirements. Specific cloud services are available for classified data, for HIPAA-compliant information, or for PCI-DSS, to give just some examples.

But the biggest concern is often the location of data. Although the large cloud providers now offer specific geographical zones for their storage, a business might still decide, or be required to decide, that the better option is to relocate data to an on-premise system or a local datacentre.

It is a misconception that regulation creates significant barriers to moving workloads to the cloud, says Adam Stringer, business resilience expert at PA Consulting. Regulators do demand rigour, just as they do for other outsourced arrangements, but there are many successful examples of highly regulated firms migrating to the cloud.

The key lies in careful planning, he says.

A further twist in the regulatory tale comes from investigations. If a regulator, law enforcement agency or a court requires extensive data forensics, this might be impossible, or at least very expensive, in the cloud. The alternative is to bring the data in-house.

Although the cloud provides almost limitless storage capacity, it depends on internet connections to operate. This, in turn, creates latency.

Some applications backup and recovery, email and office productivity, and software-as-a-service packages are not especially sensitive to latency. Enterprise-grade connectivity is now fast enough that users notice little in the way of lag.

For some workloads, however, which could include real-time analytics, databases, security applications and those connected to sensors and the internet of things, there may be more sensitivity to latency. Systems architects need to account for latency between the data source, storage or compute resources and the end-user, and latency between services in the cloud intra-cloud latency.

Although technologies such as edge computing, caching and network optimisation will cut latency, in other cases the simplest solution will be to bring the data back in-house, shortening communications paths and allowing the IT team to fine-tune storage, compute and networking to suit the applications and workloads.

Avoiding latency issues in the first place means analysing where most data is based. This deals with issues of data gravity. If most data is in the cloud, and processing is done in the cloud, data gravity will not be an issue. If data is constantly swapping between clouds and on-premise storage or compute resources, something is wrong.

Sometimes, organisations repatriate data simply because the move to the cloud has not met expectations. In this case, they might try to save face, according to Forresters Naveen Chhabra. They tried to retrofit an app in the cloud while architecturally they should not have, he says.

It could be that the workload was not suited to the cloud, or cloud migration was poorly planned or executed. If your data architecture is a mess and you move your data to the cloud, you just end up with a mess in the cloud, says PAs Stringer. A move to the cloud will not, in itself, fix IT design issues, he adds.

And where organisations want to use the cloud either as a redeployment or a greenfield project they need to apply the same or higher standards of design. Architectural rigour is as important for cloud deployments as it is for on-prem, says Stringer. If they dont get that right, businesses will end up having to repatriate parts of their estate.

This does not mean repatriation will be easy, or even that it will fix the problem. But at least it will give the IT team the chance to reset, analyse what went wrong, and replan how cloud could be used more effectively in the future.

Provider failure is perhaps the ultimate reason to repatriate data. The customer will probably have no choice. Hopefully, the provider will give some notice and a realistic timescale for organisations to take back their data or move it to another cloud provider.

But it is possible that a provider could cease trading without notice, or that technical or environmental problems could force it to cease operating without notice. In that case, firms will need to rely on alternative copies of their data, on-premise or with another cloud.

Fortunately, complete provider failure is rare. But the experience gained from recent cloud outages suggests that at the very least, organisations need a plan for how to secure and retrieve their data if it does happen. And on-premise technology is likely to be central to any recovery plan, even if only until the organisation can source new cloud capacity.

The question to ask before moving a workload to the cloud is: does this increase the resilience of the customer or market-facing service? says PAs Stringer. If youre only moving to reduce costs, the overheads of building resilience back in at a later date could offset any benefit.

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Cloud repatriation: Five reasons to repatriate data from cloud - ComputerWeekly.com

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JetStream’s Azure-native DR finally takes off – TechTarget

After months of delays, JetStream's Azure-native disaster-recovery-as-a-service product has finally made its last boarding call.

This week, JetStream DR for Azure VMware Solution (AVS) became generally available in Azure Marketplace. The software enables customers to perform on-premises-to-AVS or AVS-to-AVS failovers for their VMware virtual machines. This version of JetStream DR is deeply integrated with Azure -- it's discovered and deployed through Azure Marketplace, viewable through Azure Portal and billed directly through Azure.

JetStream DR for AVS continuously captures and replicates data from VMware environments and stores the copies in Azure Blob Storage, an object storage platform for unstructured data. Continuous data capture translates to near-zero recovery point objectives (RPOs), and storing the copies in Azure Blob translates to lower costs compared to using a file system as a repository.

In a failover scenario, the software restores the copies from Azure Blob into AVS. A standard recovery from Azure Blob can take one or more hours as the data is rehydrated at the time the failover happens, but JetStream DR also supports continuous rehydration into vSAN and Azure NetApp Files for recovery time objectives (RTOs) of a few minutes. Customers can divide their VMs along these two recovery methods based on criticality.

First introduced in December 2020, JetStream DR for AVS would become generally available in "early 2021," according to JetStream Software at the time. Instead, the vendor spent time improving the software before the official launch, said JetStream Software president and co-founder Rich Petersen, which included closer integration with AVS infrastructure, making deployment more automated, building a capacity planning tool so customers could accurately assess the storage costs of using the software and ensuring the product complied with Microsoft's privacy policies.

We had to do a lot of the unsexy but essential work. Rich PetersenPresident and co-founder, JetStream Software

"We had to do a lot of the unsexy but essential work," Petersen said.

JetStream DR for AVS costs $35 per month, per VM and will appear as a line item in customers' Azure bills.

JetStream DR for AVS addresses an important area of need within Microsoft's cloud, said Andrew Smith, a research manager at IDC. Azure VMs can be protected with Azure Site Recovery, but VMware VMs running in Azure need their own DR tool. By offering native disaster recovery as a service (DRaaS) and enabling Azure Blob as the repository, Microsoft can position AVS as cheaper DR than its AWS equivalent, VMware Cloud on AWS.

"It's filling a gap in the Azure ecosystem," Smith said.

JetStream DR for AVS' main competition will be from VMware's native DR tools: Site Recovery Manager and Cloud Disaster Recovery, the latter of which is based on technology from Datrium, a company VMware acquired in July 2020, he added.

However, JetStream has the advantage over VMware's native DR because it's a single product, according to Petersen. Site Recovery Manager replicates VMs to an active VMware Cloud to deliver immediate availability during a failover scenario, while Cloud Disaster Recovery replicates VMs to Amazon S3. Customers would use the former for critical VMs and the latter for non-critical ones. JetStream DR for AVS can handle both RTO needs.

DRaaS has been growing more popular because it addresses two of customers' current biggest concerns: ransomware and rising infrastructure complexity, Smith said. Data loss is one potential impact of a ransomware attack, but customers are also worried about time loss. A good DR plan will minimize how long a company's systems are unavailable during an attack, and DRaaS has the additional benefit of offloading the burden of DR to a service provider.

Additionally, customers are turning to DRaaS because running DR in-house is too much of an IT burden, Smith said. Most customers can't -- or don't want to -- devote IT resources to develop and maintain an off-site location, schedule regular failover tests or otherwise perform all the tasks necessary for guaranteeing their DR site will work when they need it.

"Nobody wants to spend a lot of time doing DR testing, developing and maintaining runbooks and all that other stuff," Smith said.

Johnny Yu covers enterprise data protection news for TechTarget's storage sites SearchDataBackup and SearchDisasterRecovery. Before joining TechTarget in June 2018, he wrote for USA Today's consumer product review site Reviewed.com.

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JetStream's Azure-native DR finally takes off - TechTarget

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Anthropology, AI, and the Future of Human Society #CFP – Patheos

This call for papers grabbed my attention:

The Call for Panels has been extended till 21 Nov 2021(23:59GMT) !

Anthropology, AI and the Future of Human SocietyVirtual Conference 6-10 June 2022

https://www.therai.org.uk/conferences/anthropology-ai-and-the-future-of-human-society

Without in any way wishing to limit the possibilities, we suggest below a few of the potential areas of interest:

The arts as well as the sciences are invited, for this is an area of human speculation where both have made very great contributions, and we see the different approaches as being mutually stimulating.

Contact Info:

Hanine Habig

Royal Anthropological Institute

Via RelCFP. There is also a call for applications for fellowships to support research related to the Association for Computing Machinery. Of related interest, here are some other items of news that connect in some way with this theme:

Scott McLemee provided an overview of several books forthcoming from university presses on these topics.

A new search engine is trying to stem the tide of clickbait and misinformation

Yuval Noah Harari Believes This Simple Story Can Save the Planet

CNN poll suggests most think Facebook is making our lives worse.

Even Instagram thinks you should take a break from Instagram.

Phillip K. Dicks novel Vulcans Hammer will be made into a movie.

Teslas inaccurately-named self driving beta has caused a major crash.

Jeana Jorgensens brand new flash fiction story Moral Module 6 also intersects with this topic at least somewhat and is worth reading regardless of whether youre interested in the theme that otherwise holds this blog post together!

Finally, you can watch AlphaGo: The Movie online!

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Anthropology, AI, and the Future of Human Society #CFP - Patheos

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ML Kit | Google Developers

Machine learning for mobile developers

ML Kit brings Googles machine learning expertise to mobile developers in a powerful and easy-to-use package. Make your iOS and Android apps more engaging, personalized, and helpful with solutions that are optimized to run on device.

Video and image analysis APIs to label images and detect barcodes, text, faces, and objects.

Scan and process barcodes. Supports most standard 1D and 2D formats.

Identify objects, locations, activities, animal species, products, and more. Use a general-purpose base model or tailor to your use case with a custom TensorFlow Lite model.

Recognizes handwritten text and handdrawn shapes on a digital surface, such as a touch screen. Recognizes 300+ languages, emojis and basic shapes.

Separate the background from users within a scene and focus on what matters.

Natural language processing APIs to identify and translate between 58 languages and provide reply suggestions.

Determine the language of a string of text with only a few words.

Generate reply suggestions in text conversations.

Detect and locate entities (such as addresses, date/time, phone numbers, and more) and take action based on those entities. Works in 15 languages.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }] [{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]

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ML Kit | Google Developers

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IEEE: Most Important 2022 Tech Is AI/Machine Learning, Cloud and 5G – Virtualization Review

News

IEEE says the most important technologies in 2022 will be AI/machine learning, cloud computing and 5G wireless.

That comes in a new report published by the large technical professional organization titled "The Impact of Technology in 2022 and Beyond: an IEEE Global Study," based on an October survey of 350 chief information officers, chief technology officers and technology leaders from the U.S., U.K., China, India and Brazil who were asked about key technology trends, priorities and predictions for 2022 and beyond.

"Among total respondents, more than one in five (21 percent) say AI and machine learning, cloud computing (20 percent), and 5G (17 percent) will be the most important technologies next year," IEEE said in a Nov. 18 announcement. "Because of the global pandemic, technology leaders surveyed said in 2021 they accelerated adoption of cloud computing (60 percent), AI and machine learning (51 percent), and 5G (46 percent), among others."

The report includes respondent data for 12 questions, starting off with: "Which will be the most important technology in 2022?" Although "Other" was the top answer (25 percent of respondents), the three technologies listed above weren't far behind. Other answers were "Augmented and Virtual Reality (AR/VR)" at 9 percent and "Predictive AI" at 7 percent.

"AI is working all around us," the report quotes Shelly Gupta, IEEE graduate student member, as saying. "It has entered into almost every sector enable its growth. The AI industry will continue to proliferate. In turn, it will continue to drive massive innovation that will fuel many existing industries."

The "big three" technologies listed above as being most important for 2022 are also the top three answers to the second question, "Which technologies did you accelerate adopting in 2021 due to the pandemic?" though in a different order: cloud computing (60 percent), AI/machine learning (51 percent) and 5G (46 percent).

"Cloud computing has had a huge boost due to remote work as well as accelerated trends in digital transformation," said Tom Coughlin, who holds the title of IEEE Life Fellow among several others.

The other 10 questions and their top answers are:

"Time and time again technology rises to meet the biggest challenges of society," the report said. "The innovators and technologists that bring new ideas to life serve as catalysts of positive global change."

About the Author

David Ramel is an editor and writer for Converge360.

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IEEE: Most Important 2022 Tech Is AI/Machine Learning, Cloud and 5G - Virtualization Review

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MCubed does web workshops: Join Mark Whitehorns one-day introduction to machine learning next month – The Register

Event You want to know more about the ins and outs of machine learning, but cant figure out where to start? Our AI practitioners' conference MCubed and The Register regular Mark Whitehorn have got you covered.

Join us on December 9 for an interactive online workshop to learn all about ML types and algorithms, and find out about strengths and weaknesses of different approaches by using them yourself.

This limited one-day online workshop is geared towards anyone who wants to gain an understanding of machine learning no matter your background. Mark will start with the basics, asking and answering what is machine learning, before diving deeper into the different types of systems you keep hearing about.

Once youre familiar with supervised, unsupervised, and reinforcement learning, things will get hands-on with practical exercises using common algorithms such as clustering and, of course, neural networks.

In the process, youll also investigate the pros and cons of different approaches, which should help you in assessing what could work for a specific task and what isnt an option, and learn how the things youve just tried relate to what Big Biz are using. However, its not all code and algorithms in the world of ML, which is why Mark will also give you a taster of what else there is to think about when realizing machine learning projects, such as data sourcing, model training, and evaluation.

Since Python has turned into the language of choice for many ML practitioners, exercises and experiments will be performed in Python mostly, so installing it along with an IDE will help you make the most of the workshop if you havent already.

This doesnt mean the course is for Pythonistas only, however. If youre not familiar with the language, exercises will be transformed into demonstrations providing you insight into the inner workings of the associated code, before we start altering some of the parameters together. Like that, you get to find out how each parameter influences the learning that is performed, leaving you in top shape to continue in whatever language (or no-code ML system) you feel comfortable with.

Your trainer, Professor Mark Whitehorn, works as a consultant for national and international organisations, such as the Bank of England, Standard Life, and Sainsburys, designing analytical systems and data science solutions. He is also the Emeritus Professor of Analytics at the University of Dundee where he teaches a master's course in data science and conducts research into the development of analytical systems and proteomics. You can get a taster of his brilliant teaching skills here.

If this sounds interesting to you, head over to the MCubed website to secure your spot now. Tickets are very limited to make sure we can answer all your questions and everyone is getting proper support throughout the day so dont wait for too long.

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MCubed does web workshops: Join Mark Whitehorns one-day introduction to machine learning next month - The Register

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DEWC, AIML partner on AI and machine learning to enhance RF signal detection – Defence Connect

key enablers | 19 November 2021 | Reporter

By: Reporter

DEWC Systems and the Australian Institute for Machine Learning (AIML) have agreed to partner on research to better detect radio signals in complex environments.

DEWC Systems and the Australian Institute for Machine Learning (AIML) have agreed to partner on research to better detect radio signals in complex environments.

DEWC Systems and the University of Adelaides Australian Institute for Machine Learning (AIML) have announced the commencement of a partnership to better understand how to apply artificial intelligence and machine learning to detect radio frequencies in difficult environments using MOESS and Wombat S3 technology.

As of yet, both organisations have already undertaken significant research on Phase 1 of the Miniaturised Orbital Electronic Sensor System (MOESS) project with the collaboration hoping to enhance the research yet further.

The original goal of the MOESS was to develop a platform to perform an array of applications and develop an automatic signal classification process. The Wombat 3 is a ground-based version of the MOESS.

Chief technology officer of DEWC Systems Dr Paul Gardner-Stephen will lead the project, which hopes to develop a framework for AI-enabled spectrum monitoring and automatic signal classification.

Radio spectrum is very congested, with a wide range of signals and interference sources, which can make it very difficult to identify and correctly classify the signals present. This is why we are turning to AI and ML, to bring the necessary algorithmic power necessary to solve this problem, Gardner-Stephen said.

"This will enable the creation of applications that work on DEWCs MOESS and Wombat S3 (Wombat Smart Sensor Suite) platforms to identify unexpected signals from among the forest of wireless communications, to help defence identify and respond to threats as they emerge.

According to Gardner-Stephen, both the MOESS and Wombat 3 platforms are highly capable software defined radio (SDR) platforms with on-board artificial intelligence and machine learning processors.

Since the project is oriented around creating an example framework, using two of DEWC Systems software defined radio (SDR) products, both DEWC Systems and AIML can create the kinds of improved situation awareness applications that use those features to generate the types of capabilities that will support defence in their mission, he explained.

In addition to directly working towards the creation of an important capability, it will also act to catalyse awareness of some of the kinds of applications that are possible with these platforms.

Subscribe to the Defence Connect daily newsletter. Be the first to hear the latest developments in the defence industry.

Chief executive of DEWC Systems Ian Spencer noted that the company innovates with academic institutions to develop leading technology.

Whilst we provide direction and guidance of the project, AIML will be bringing their deep understanding and cutting-edge technology of AI and machine learning. This is what DEWC Systems does. We collaborate with universities and other industry sectors to develop novel and effective solutions to support the ADO, Spencer said.

It is hoped that the technology developed throughout the partnership will support machine learning and artificial intelligence needs of Defence.

[Related:Veteran-owned SMEs DEWC Systems and J3Seven aim to solve mission critical challenges]

DEWC, AIML partner on AI and machine learning to enhance RF signal detection

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DEWC, AIML partner on AI and machine learning to enhance RF signal detection - Defence Connect

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