This latest financing round was led by Sierra Ventures, with participation from the company’s pre-seed lead investor Origin Ventures and brings its total financing to date to $27 million. Hammoq startup has Automated More than a Half Million Resale Product Listings Across a Broad Network of eCommerce Marketplaces Using Artificial Intelligence and Machine Learning

A glut of online retail returns and a flourishing market for sustainable fashion upcycling and vintage items has created an exploding reCommerce industry. With this financing round, Hammoq will accelerate development of its AI and machine learning platform to automate marketplace listings and open up channels for reseller financing as well as product sourcing.

“The reCommerce market is exploding, with the fashion resale market alone expected to reach $26 billion in 2022,” said Sid Lunawat, CEO and Co-Founder of Hammoq. “There are an abundance of resale marketplaces supporting this economy. Yet, the largest gap is in the labor required to identify product value and push it to the marketplaces. With rising labor costs, the need for automation in the reCommerce industry continues to grow. Using AI and machine learning, Hammoq has put a major dent in this labor gap, empowering its customers to automate the identification and listing process. Our latest funding will accelerate tech development while supporting our sales and marketing strategies to capitalize on this rising opportunity.”

Goodwill of the San Francisco Bay and Hammoq share a deep commitment to sustainability. Hammoq’s AI technology is helping Goodwill to process more material donations, more quickly to help divert more items from landfill. Donated items are given a second life in Goodwill thrift stores.

Another customer of Hammoq is reseller Flip the World. “Before we brought Hammoq on to handle our listings, I would average around 40-50 listings a week. Listing across platforms is time consuming work and after a full day of sourcing, cleaning, prepping and photographing I found that I was falling asleep in bed trying to get my listings up,” said Chris Hatfield, Owner, Flip the World. “Now with Hammoq, we’ve more than doubled our weekly listings and have gone from a 500-item store to over 2,000 in less than two months. It’s been a game changer for our business. Now we can focus on the most important part of reselling, sourcing the items, rather than the listing.”

Hammoq was co-founded by reCommerce industry veterans Sid Lunawat and Ty Blunt, sustainability advocates who believe the 80 billion resalable items dropped in landfills every year have a viable purpose for prolonged use. After successful stints running a reseller business, Lunawat and Blunt understood that identifying and easily listing products for resale was an acute pain point. To solve this challenge, they developed the Hammoq software platform using AI and machine learning to enable listing at scale. Starting with a simple photo, the platform uses product data to automatically enable listings across dozens of resale marketplaces. Hammoq has listed over 600,000 products for sale and ensures 100% compliance with data listing requirements prior to pushing to the marketplaces.

“Hammoq is uniquely serving the exploding reCommerce industry by applying technology to solve sellers’ largest challenge: intelligent, real-time listings,” said Vignesh Ravikumar, Partner, Sierra Ventures. “Their solution and business model are well aligned with our mission to invest early in emerging technology companies that are moving the needle in key market areas. Their SaaS reCommerce solution is delivering precisely what the rapidly transforming market needs as consumers actively seek ways to buy used products that support sustainability and reuse.”

Hammoq is a company that is reimagining reCommerce, digitizing resale goods and automating online listings so customers can list and sell more. Established in 2021 and based in Phoenix, Hammoq has supported the identification and listing automation process for more than 600,000 items. For resellers, liquidators, thrift and retail organizations, Hammoq enables the resale of returns, lost SKUs, overstocks and other pre-loved goods. Built by resellers for resellers Hammoq’s SaaS platform drives customers’ ability to scale reCommerce, while significantly reducing the time and labor needed to get merchandise online.

Sierra Ventures is a Silicon Valley-based early-stage venture firm investing globally with a focus on Core Enterprise and Next-Gen Technologies. With four decades of experience and over $2 billion of assets under management, Sierra has created a vast network of successful entrepreneurs, Global 1000 CXOs, operational executives, and deep domain experts, providing a platform for entrepreneurs around the world.

Learn more about Hammoq at the website here.

Image licensed by unsplash.com

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The post Hammoq Closes $24 Million Round to Build AI Platform to Tackle Used Goods Market appeared first on Digital IT News.

Retail returns topped $761 billion in 2022 and is projected to reach trillions over the next few years. Due to the complexity of processing these items for resale, merchandise is frequently incinerated, exported, liquidated for pennies on the dollar or dumped into landfills. This is not only unsustainable, it’s costly. As online shopping flourishes and the culture of ‘buy and return’ continues to overwhelm retailers, a resulting used goods marketplace has emerged to a tune of $36 billion in 2022. And that’s just apparel, electronics adds $32.5 billion, used furniture another $12 billion, the list goes on -– placing the resale industry sternly in the trillions.

While there is an abundance of product and a variety of resale marketplaces where these items could be sold, the barrier is in the processing and listing of these products. This has traditionally been a labor-intensive process where products must be manually identified, valued, and characterized before listing for sale. With artificial intelligence (AI) and machine learning, all this is changing.

How AI is Changing the Product Resale Market

Retailers, along with used product resellers, non-profits, thrifters and liquidators, want to capitalize on this thriving reCommerce market to cut the billions of dollars going to waste. AI and machine learning has become the answer. Here are the key ways AI is enabling the reCommerce industry to scale.

1. Attribute Identification. Using AI and machine vision, resellers are able to quickly take what was once a very manual process of identifying and assigning product attributes and automate this identification process. Product characteristics such as color, style and even brand can be quickly assessed, automatically, using simple photographs. This can cut hundreds of hours off product processing when used for large batches of items, such as returns, donations and overstocks.
2. Product Valuation. Returned, donated and used items typically don’t have designated tags or SKUs that can identify the value or assign a price. Using AI technology, identified attributes can be used to scour data sets across resale markets to properly identify current item value and set a market price. This too takes hours off of the traditionally manual product pricing process and is very effective at optimizing resale revenue.
3. Marketplace Listing Automation. Product marketplaces from Poshmark, threadUP and The RealReal to eBay, OfferUp and Facebook Marketplace all require specific adjustments and characterizations to effectively list on their sites. AI technology and automation is being used to produce fully compliant, SEO optimized listings for each marketplace so that products are showcased effectively, no matter where they are being sold.
4. Empowering Listing Volume. Listing products for resale is a numbers game. And with so much product volume to move, retailers specifically need to optimize their listing abilities to recoup lost profits from returned goods. AI empowers scalability and incredible volume, enabling sellers to move from a 5,000-item turn a month to exponentially increase overnight.
5. Lowers Labor Costs. The cost of human labor has been the number one barrier to increasing reCommerce scale. Through AI and machine vision technology, resellers are closing the labor gap and optimizing profitability without the costs, challenge and difficulty of recruiting workers or shipping product offshore to perform processing. This further reduces costs and enhances rapid growth through product processing scale.

There are many uses for AI and machine learning technology, but perhaps none are as market changing as how AI can transform reCommerce. AI bridges the gap between sustainability, labor shortages, and the increasing consumer demand for recycled goods. It will indeed change the game for today’s retailers as they move quickly to compete in today’s competitive retail industry and maximize the losses from high volumes of returns.

For more information please visit the HAMMOQ website.

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The global retail industry, which has grappled with the shift to online shopping for a decade, is now facing one of its most unpredictable periods to date, as businesses adapt to COVID-19. As more shopping accelerates online, retailers are facing pressure to prioritize ecommerce improvements that enhance the entire consumer shopping journey, while also driving bottom-line sales for their businesses.

“As the shift to online continues, smarter and more personalized shopping experiences will be even more critical for retailers to rise above their competition,” said Carrie Tharp, vice president of retail and consumer at Google Cloud. “Retailers are in dire need of agile operating models powered by cloud infrastructure and technologies like artificial intelligence and machine learning (AI/ML) to meet today’s industry demands. We’re proud to partner with retailers around the world, and bring forward our Product Discovery offerings to help them succeed.”

In collaboration with some of the world’s leading retailers—and leveraging Google’s semantic understanding of online search and user intent—Google Cloud’s Product Discovery Solutions for Retail bring together state-of-the-art AI algorithms with the highly scalable infrastructure of Google Cloud. Google Cloud’s Product Discovery Solutions for Retail include:

• Now Generally Available, Recommendations AI enables retailers to deliver highly personalized product recommendations at scale and across all channels. The solution is able to piece together the history of a customer’s shopping journey and serve them with customized product recommendations. Recommendations AI uses Google Cloud’s latest machine-learning architectures, so retailers can dynamically adapt to real-time user behavior and changes, accounting for variables like assortment and pricing, to ensure an unfractured shopping experience.
• Vision API Product Search allows retailers to adapt to how today’s consumers are finding product inspiration and discovery whether that happens from retailer specific websites or on any social media platforms. Vision API Product Search allows shoppers to search for products with an image and receive a ranked list of visually and semantically similar items. Vision Product Search uses ML-powered object recognition and lookup to provide real-time results of similar, or complementary, items from retailers’ product catalog. Google Cloud Vision API Product Search is Generally Available to all companies.
• Google Cloud Search for Retail, available in Private Preview, pulls from Google’s deep understanding of user intent and context to provide retailers with high-quality product search functionality that can be embedded into their websites and mobile applications. Retailers benefit from Google’s advanced search capabilities to provide intuitive search functionalities that are configurable to meet the specific needs of each retail organization. With Google Cloud Search for Retail, retailers can ensure their shoppers find exactly what they’re looking for at every step of their shopping journeys.

Availability & Getting Started 

Today, Recommendations AI and Vision API Product Search are Generally Available to all companies. Google Cloud Search for Retail is in Private Preview. If companies are interested in Google Cloud Search for Retail, please contact a sales representative. To learn more about these solutions, visit Google Cloud’s Product Discovery Solutions for Retail page here.

Image licensed by Google.com

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When Amazon DevOps Guru identifies anomalous application behavior (e.g. increased latency, error rates, resource constraints, etc.) that could cause potential outages or service disruptions, it alerts developers with issue details (e.g. resources involved, issue timeline, related events, etc.) via Amazon Simple Notification Service (SNS) and partner integrations like Atlassian Opsgenie and PagerDuty to help them quickly understand the potential impact and likely causes of the issue with specific recommendations for remediation. Developers can use remediation suggestions from Amazon DevOps Guru to reduce time to resolution when issues arise and improve application availability and reliability with no manual setup or machine learning expertise required. There are no upfront costs or commitments with Amazon DevOps Guru, and customers pay only for the data Amazon DevOps Guru analyzes. To get started with Amazon DevOps Guru, visit https://aws.amazon.com/devops-guru

As more organizations move to cloud-based application deployment and microservice architectures to globally scale their businesses and operations without the limitations of on-premises deployments, applications have become increasingly distributed to meet customer needs, and developers need more automated practices to maintain application availability and reduce the time and effort spent detecting, debugging, and resolving operational issues. Application downtime events caused by faulty code or config changes, unbalanced container clusters, or resource exhaustion (e.g. CPU, memory, disk, etc.) inevitably lead to bad customer experiences and lost revenue. Companies invest considerable money and developer time to deploy multiple monitoring tools, often managed separately, and then have to develop and maintain custom alerts for common issues like spikes in load balancer errors or drops in application request rates. Setting thresholds to identify and alert when application resources are behaving abnormally is difficult to get right, involves manual setup, and requires thresholds that must be continually updated as application usage changes (e.g. an unusually large numbers of requests during holiday shopping season).

If a threshold is set too high, developers don’t see alarms until operational performance is severely impacted. When a threshold is set too low, developers get too many false positives, which ultimately get ignored. Even if developers get alerted to a potential operational issue, the process of identifying the root cause can still prove difficult. Using existing tools, developers often have difficulty triangulating the root cause of an operational issue from graphs and alarms, and even when they are able to find the root cause, they are often left without a means to fix it. Each troubleshooting attempt is a cold start where teams must spend hours or days to identify problems, and this leads to time consuming, tedious work that slows down the time to resolve an operational failure and can prolong application disruptions.

Amazon DevOps Guru’s machine learning models leverage over 20 years of operational expertise in building, scaling, and maintaining highly available applications for Amazon.com. This gives Amazon DevOps Guru the ability to automatically detect operational issues (e.g. missing or misconfigured alarms, early warning of resource exhaustion, config changes that could lead to outages, etc.), provide context on resources involved and related events, and recommend remediation actions – with no machine learning experience required. With just a few clicks in the Amazon DevOps Guru console, historical application and infrastructure metrics like latency, error rates, and request rates for all resources are automatically ingested and analyzed to establish normal operating bounds, and Amazon DevOps Guru then uses a pre-trained machine learning model to identify deviations from the established baseline.

When Amazon DevOps Guru analyzes system and application data to automatically detect anomalies, it also groups this data into operational insights that include anomalous metrics, visualizations of application behavior over time, and recommendations on actions for remediation. Amazon DevOps Guru also correlates and groups related application and infrastructure metrics (e.g. web application latency spikes, running out of disk space, bad code deployments, memory leaks etc.) to reduce redundant alarms and help focus users on high-severity issues. Customers can see configuration change histories and deployment events, along with system and user activity, to generate a prioritized list of likely causes for an operational issue in the Amazon DevOps Guru console.

To help customers resolve issues quickly, Amazon DevOps Guru provides intelligent recommendations with remediation steps and integrates with AWS Systems Manager for runbook and collaboration tooling, giving customers the ability to more effectively maintain applications and manage infrastructure for their deployments. Together with Amazon CodeGuru – a developer tool powered by machine learning that provides intelligent recommendations for improving code quality and identifying an application’s most expensive lines of code – Amazon DevOps Guru provides customers the automated benefits of machine learning for their operational data so that developers can more easily improve application availability and reliability.

“Customers have asked us to continue adding services around areas where we can apply our own expertise on how to improve application availability and learn from the years of operational experience that we have acquired running Amazon.com,” said Swami Sivasubramanian, Vice President, Amazon Machine Learning, Amazon Web Services, Inc. “With Amazon DevOps Guru, we have taken our experience and built specialized machine learning models that help customers detect, troubleshoot, and prevent operational issues while providing intelligent recommendations when issues do arise. This enables teams to immediately benefit from operational best practices Amazon has learned from running Amazon.com, saving customers the time and effort that would otherwise be spent configuring and managing multiple monitoring systems.”

With a few clicks in the AWS Management Console, customers can enable Amazon DevOps Guru to begin analyzing account and application activity within minutes to provide operational insights. Amazon DevOps Guru gives customers a single-console experience to visualize their operational data by summarizing relevant data across multiple sources (e.g. AWS CloudTrail, Amazon CloudWatch, AWS Config, AWS CloudFormation, AWS X-Ray) and reduces the need to switch between multiple tools. Customers can also view correlated operational events and contextual data for operational insights within the Amazon DevOps Guru console and receive alerts via Amazon SNS. Additionally, Amazon DevOps Guru supports API endpoints through the AWS SDK, making it easy for partners and customers to integrate Amazon DevOps Guru into their existing solutions for ticketing, paging, and automatic notification of engineers for high-severity issues. PagerDuty and Atlassian are among the partners that have integrated Amazon DevOps Guru into their operations monitoring and incident management platforms, and customers who use their solutions can now benefit from operational insights provided by Amazon DevOps Guru. Amazon DevOps Guru is available in preview today in US East (N. Virginia), US East (Ohio), and US West (Oregon), Asia Pacific (Singapore), and Europe (Ireland) with availability in additional regions in the coming months.

Teams at more than 170,000 companies rely on Atlassian products to make teamwork easier, and help them organize, discuss, and complete their work. “Atlassian is proud to partner with AWS on the launch of Amazon DevOps Guru and help empower teams to deploy code and operate services with confidence,” said Emel Dogrusoz, Head of Product, Opsgenie. “With our new Opsgenie and Jira Service Management integration, the right teams can be immediately notified the instant Amazon DevOps Guru predicts a potential issue, or determines an incident has occurred. Amazon DevOps Guru provides a new dimension of insight, and Atlassian ensures the fastest response.”

“PagerDuty was built to drive the move to a DevOps culture by automating the entire incident response lifecycle with resolution,” said Jonathan Rende, SVP of Product at PagerDuty. “We’re excited to continue this commitment to DevOps with our latest integration with Amazon DevOps Guru. Leveraging Amazon’s decades of operational excellence and Amazon DevOps Guru’s machine learning capabilities, PagerDuty provides even more real-time signal-to-action capabilities to our joint customers. Through PagerDuty’s ingestion of Amazon DevOps Guru’s Amazon SNS, AWS customers can take real-time action on operational issues before they become customer-impacting outages.”

Thomson Reuters is one of the world’s most trusted providers of answers, helping professionals make confident decisions and run better businesses. “Customer experience is vital to us. Dealing with multiple sources of alerts for availability, performance, and change requests can be a challenge when trying to prevent and mitigate incidents impacting our customers,” said Steve Thoennes, Director of Infrastructure Hosting Portfolio at Thomson Reuters. “We are excited to use Amazon DevOps Guru and leverage its machine learning insights to provide clear paths for action, allowing us to mitigate issues quickly and avoid customer impacting events. The integration with PagerDuty is a bonus, as we can have recommendations delivered to the right people timely and efficiently.”

SmugMug facilitates the sale of digital and print media for amateur and professional photographers. “My team follows an ops-for-life motto, and we are always on the lookout for ways to automate our manual activities,” said Andrew Shieh, Operations Director at SmugMug. “With Amazon DevOps Guru, we hope to realize that goal and let AIOps take over many of our day-to-day tasks and make our workday composed of a single George-Jetson-style Easy Button, so my team can focus on IT innovation. We are now not only meeting the needs of the business but able to exceed them since we have more time to focus on what matters most – delivering value for our organization and our customers.”

NextRoll helps marketplaces and marketing platforms grow revenue by empowering them to build and enhance their marketing solutions. “We run thousands of Amazon Elastic Compute Cloud (Amazon EC2) instances, and we are looking for ways to reduce my team’s time spent on resolving operational issues,” said Valentino Volonghi, CTO at NextRoll. “We are excited to use Amazon DevOps Guru and leverage its machine learning-powered insights to help us identify, correlate, and remediate operational issues with recommendations. This will help my team save hours and reduce our mean time to recovery.”

Imaged licensed by Pixabay.com

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2021 Most Important Technologies and Challenges 
Which will be the most important technologies in 2021?  Among total respondents, nearly one-third (32%) say AI and machine learning, followed by 5G (20%) and IoT (14%).

Manufacturing (19%), healthcare (18%), financial services (15%) and education (13%) are the industries that most believe will be impacted by technology in 2021, according to CIOs and CTOS surveyed. At the same time, more than half (52%) of CIOs and CTOs see their biggest challenge in 2021 as dealing with aspects of COVID-19 recovery in relation to business operations. These challenges include a permanent hybrid remote and office work structure (22%), office and facilities reopenings and return (17%), and managing permanent remote working (13%).  However, 11% said the agility to stop and start IT initiatives as this unpredictable environment continues will be their biggest challenge. Another 11% cited online security threats, including those related to remote workers, as the biggest challenge they see in 2021.

Technology Adoption, Acceleration and Disaster Preparedness due to COVID-19
CIOs and CTOs surveyed have sped up adopting some technologies due to the pandemic:

• More than half (55%) of respondents have accelerated adoption of cloud computing
• 52% have accelerated 5G adoption
• 51% have accelerated AI and machine learning

The adoption of IoT (42%), augmented and virtual reality (35%) and video conferencing (35%) technologies have also been accelerated due to the global pandemic.

Compared to a year ago, CIOs and CTOs overwhelmingly (92%) believe their company is better prepared to respond to a potentially catastrophic interruption such as a data breach or natural disaster. What’s more, of those who say they are better prepared, 58% strongly agree that COVID-19 accelerated their preparedness.

When asked which technologies will have the greatest impact on global COVID-19 recovery, one in four (25%) of those surveyed said AI and machine learning,

Cybersecurity 
The top two concerns for CIOs and CTOs when it comes to the cybersecurity of their organization are security issues related to the mobile workforce including employees bringing their own devices to work (37%) and ensuring the Internet of Things (IoT) is secure (35%). This is not surprising, since the number of connected devices such as smartphones, tablets, sensors, robots and drones is increasing dramatically.

Slightly more than one-third (34%) of CIO and CTO respondents said they can track and manage 26-50% of devices connected to their business, while 20% of those surveyed said they could track and manage 51-75% of connected devices.

Image licensed by Upsplash.com

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“Radical simplicity is critical for customers to be successful with unstructured data. Our software-driven file data platform offers enterprise-level capabilities that radically simplify the process of today’s digital transformation,” said Ben Gitenstein, VP of Product, Qumulo. “With the data services we announced today, Qumulo’s customers can simplify the complexity of their infrastructure, accelerate innovation, and unleash the power of their data, wherever it resides.”

NVMe Cached Performance: Significantly lowering the cost of performance

Although many workloads, such as data analytics, research computing, and rich media content creation, benefit from low latency and massive throughput, access to the most performant leading-edge technologies has historically been accessible only to specialized workloads with large budgets.

Qumulo has broken this paradigm, introducing the file industry’s first software to provide machine learning optimized read and write cache leveraging NVMe. The intelligent cache manages data on the optimal storage media to get both high performance and cost-effective capacity. With the introduction of the latest release of Qumulo Core software and two new qualified hardware options (C-192T and C-432T), Qumulo now offers NVMe performance at the price of disk.

“The new Qumulo Core capability to use NVMe as cache enables us to meet our performance and budget needs by providing great economics from very dense hard drives combined with its great caching capabilities on extremely fast NVMe drives,” said Serkan Yalcin, Director of IT, Infrastructure Dev/Ops, Institute for Health Metrics and Evaluation. “Qumulo’s intelligent caching, without managing policies, has been great and provides even more value for us with NVMe as the caching layer. These features, combined with their great customer success experience, really empower us to focus on our mission of bringing population health data to the world.”

Qumulo Dynamic Scale: Leverage new processor, storage and memory innovation to scale existing deployments

Data is growing more rapidly every year. Users need access to new technology to keep pace. Historically, organizations have had to worry about when to invest in new technology, concerned they may miss the window to access new advancements that may be imminently released. The introduction of Qumulo Dynamic Scale enables administrators to leverage newly qualified platforms with the latest processors, memory and storage devices without the need for forklift upgrades, data migrations, or complex storage pool management. Qumulo users now can add new qualified platforms into existing environments with no need to manage different storage pools or perform a data migration. The new platforms are simply added to the existing environment, data is automatically redistributed, and the increased performance and capacity are automatically made available to users and applications.

“Everyone from the marketing group to our dot-com and interactive groups is relying on it. We needed the storage equivalent of a reliable Swiss army knife, and unfortunately, our old system wasn’t cutting it anymore,” said Raoul Edwards, Director, Network Systems Engineering and Field Ops at MSG Networks.

Qumulo Secure: Automated data encryption, for free

To help make data encryption easy and cost effective, today Qumulo is introducing AES 256-bit software encryption at-rest as part of the Qumulo Secure set of data services. For new deployments, Qumulo now encrypts all data, automatically. No additional third-party applications or key managers are needed, and there is no added cost. Encryption now comes standard, as do all Qumulo features as part of the standard software subscription.

Qumulo Secure provides a wide range of security features, including role-based authentication (RBAC), audit, and encryption in-flight. And with today’s announcement, the Qumulo software will now encrypt all data automatically, on any deployment type, at no additional cost. “With Qumulo making industry-standard AES-256 encryption a standard in their solution, I never need to worry about if my data is at risk,” said Hanoz Elavia, Storage Administrator at Atomic Cartoons.

Instant Upgrade to Qumulo Core: No downtime to users or applications, no maintenance windows

Software upgrades to IT infrastructure historically required time-consuming planning, maintenance windows, and scheduled downtime. When using Instant Upgrade, the system starts the new version in a container, changes the system to point to the new version, and stops the old version in under 20 seconds, making upgrades fast and consistent across cluster sizes and different underlying hardware. And when OS updates are needed, Instant Upgrade automates those as well, applying updates and rebooting nodes as needed to achieve a fully upgraded environment with ease.

“Managing data with Qumulo is so simple that it’s hard to describe the impact. It has given us tremendous ROI in terms of time saved and problems eliminated. Having such reliable storage makes us eager to use it more broadly throughout the company,” said John Beck, IT Manager at Hyundai Mobis.

Qumulo Shift: Simplify transformation of data from file to object now with a visual interface

Data is typically created in a file format, but applications and developers often want to leverage capabilities and services connected to cloud object stores such as AWS S3. Qumulo Shift makes it simple to copy data from a file solution into Amazon S3. The new visual interface makes it even easier to leverage data in the location and format that makes innovation fastest. When data needs to be transformed from file to object, customers can simply select their choice Amazon S3 target buckets and initiate a data copy with the click of a button.

With Qumulo Shift, customers can:

• Leverage legacy and cloud-native applications without having to re-build their architecture
• Retain S3-native and file-native properties to maintain full data control and ownership
• Avoid having to refactor applications or use third-party data movement packages

The simplicity of Qumulo’s file data platform makes it easy and affordable for organizations to leverage the value of massive data sets distributed across on-prem and multi-clouds and ensures visibility into the data with uncompromising security and data protection.

Learn more about leveraging the Qumulo file data platform and start innovating faster with Qumulo today; contact us for a demo.

Availability

• NVMe Cached Performance
  • Software optimizations for NVMe as cache – available today in software release 3.0.2
  • New qualified platforms
    • C-192T – available to order today, general availability November 25, 2020
    • C-432T – available to order today, general availability November 10, 2020
• Qumulo Dynamic Scale – available December 15, 2020
• Qumulo Secure – available today in software release 3.1.5
• Instant software upgrade for Qumulo Core – available November 25, 2020, in software release 3.3.3
• Qumulo Shift included in a visual interface – available today in software release 3.3.0

Image Licensed by pixabay.com

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The IDC MarketScape report is the latest recognition from top industry analyst firms for SAS^® artificial intelligence (AI), machine learning and advanced analytics capabilities.

“Organizations with large amounts of data – which today is most organizations – value machine learning because it helps them quickly discover insights in their data and improve decision making,” said Susan Kahler, SAS Global Marketing Manager for AI and Machine Learning. “SAS machine learning technologies and the larger SAS^® Viya^® analytics platform help people at all skill levels – executives, data scientists, business analysts and more – transform data into decisions and bottom-line results through a powerful, collaborative and cloud-native environment.”

According to David Schubmehl, Research Director for AI Software Platforms at IDC, “Success in the rapidly evolving AI software platforms market requires advanced machine learning software platform vendors to continue to innovate and provide tools to help customers accelerate development and deployment and monitoring of machine learning models.”

“SAS has strengths in both product and business strategies and capabilities, especially in its R&D pace and productivity, delivery, and capabilities; product functionality; and offering as well as its customer satisfaction.”

The report evaluated SAS Viya. “SAS Viya enables an end-to-end data mining and machine learning process with a comprehensive visual – and programming – interface,” it says, noting SAS’ strengths in AI applications that require the building of models using both structured data and text analytics or computer vision.

Referencing the capabilities of the reengineered SAS Viya platform, IDC reports: “SAS has embraced and incorporated open source languages and tools and integrated them into the SAS Viya product so that AI/ML developers and data scientists can utilize their learning and skills.”

The upcoming release of SAS Viya with new features (available next month) is designed to be delivered and updated continuously via the cloud. The updated SAS Viya platform is one result of SAS’ $1 billion investment in AI, announced 18 months ago.

The post SAS a Leader in IDC MarketScape for Advanced Machine Learning appeared first on Digital IT News.

NGP led both funding rounds with $6M in combined investment. Since its launch in November 2018, the utility industry’s first Silicon Valley-based investment and innovation firm now has put $175 million to work in emerging technology companies and specialty venture funds. These innovators share National Grid’s commitment to developing a smarter, more renewable energy future.

NGP’s newest portfolio additions are:

• Boston-based Aperio Systems, which uses AI and machine learning (ML) to analyze and monitor industrial sensor data in real time. Aperio’s data integrity platform enables customers in industries such as energy, mining and manufacturing to make better-informed decisions, reduce downtime and boost safety and security.

• Silicon Valley’s AiDash uses high-resolution satellite imagery coupled with AI to help utility and energy customers transform operations and maintenance activities like vegetation management, remote monitoring and disaster management. Its technology helps protect distribution grids from overgrown plant life that can spark disruptions or fires.

“National Grid’s ambition is to become the most intelligent transmission network in the world,” said Lisa Lambert, the company’s Chief Technology and Innovation Officer and the founder and president of National Grid Partners. “We are investing in and deploying technologies across our networks to enhance resilience and reliability, while more easily integrating clean energy.”

NGP Director Andre Turenne will join both companies’ boards of directors.

Founded to help National Grid disrupt and future-proof itself, NGP invests in early and expansion-stage companies from its $300M initial funding allocation. Its focus areas include the Internet of Things, grid modernization, security, cloud, AI, mobility, and analytics, among others. NGP also convenes the NextGrid Alliance, a network of global utility companies that share innovation and investment best practices to solve common problems and benefit customers.

National Grid plc is one of the largest investor-owned energy companies in the world. National Grid and its affiliates play a vital role in delivering gas and electricity to millions of people across Great Britain and the northeastern U.S. The company is transforming its electricity and natural gas networks with smarter, cleaner, and more resilient energy solutions to reduce its greenhouse gas emissions to net zero by 2050. National Grid also is working to accelerate decarbonization through its diverse portfolio of low-carbon and renewable energy businesses.

Image credit: Unsplash.

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“The future of work, where working from anywhere on any device is the new normal, means that proactively managing the ever-increasing security risks and asset spend is top of mind for every enterprise,” said Nayaki Nayyar, executive vice president and chief product officer, Ivanti. “Our latest additions to the Ivanti Neurons Platform for Patch and Spend Intelligence leverage our strength in patching to assess patch reliability and risk-based patch prioritization using supervised and unsupervised machine learning algorithms to automate vulnerability remediation and proactively manage software spend.”

Ivanti Neurons enables the self-healing autonomous edge with adaptive security and a contextualized, personalized experience for today’s remote workforce. Customers of Ivanti Neurons are realizing over 50 percent reductions in support call times, eliminating duplicate work between IT operations and security teams, reducing the number of vulnerable devices by up to 50 percent.*

Jesse Miller, information technology specialist for SouthStar Bank recalls his conversation with his compliance auditor about Ivanti Neurons for Patch Intelligence, “I am blown away by how Ivanti has built in a community response directly into their remediation system. I have never found anything like this! Instead of talking about security and patching for hours with a compliance auditor, we spent only minutes on it and moved onto other things. I have been impressed with the entire solution.”

The new Ivanti Neurons capabilities help enable users to reduce their time-to-patch for an overall improved security posture. They also dramatically improve the asset deployment and reclamation process for IT operations and security teams to take action and collaboratively work. New Ivanti Neurons solutions and capabilities include:

• Ivanti Neurons for Patch Intelligence helps enable organizations to achieve faster SLAs for their vulnerability remediation efforts via supervised and unsupervised machine learning algorithms. Drawing on the Ivanti patch management expertise, which deploys over 1.2 billion patch updates annually, Ivanti Neurons for Patch Intelligence helps users easily research, prioritize and receive better insights for patch management processes in one central location. Patch reliability data is automatically delivered with actionable intelligence pulled from thousands of public and crowdsourced sentiment data. This information provides improved patch reliability so security teams can act on threats faster and reduce their time-to-patch. A precise picture of the organization’s threat landscape is provided through prioritized, risk-based metrics and detailed compliance reporting. Ivanti Neurons for Patch Intelligence delivers highly accurate data that reduces the time it takes to respond to threats.
• Ivanti Neurons for Spend Intelligence which provides insights into an organization’s software landscape and application spend for on-premises, cloud and edge environments to help improve operational speed and asset visibility, improve utilization and manage costs. Unlike complex software licensing tools, Ivanti Neurons for Spend Intelligence is easy and intuitive to use, with faster time-to-value. Within minutes, a detailed analysis of usage, licenses types, purchases, subscriptions, renewals and instances are presented in engaging dashboards to help users more effectively track usage, purchase history, upcoming renewals, contract expirations, and ongoing overall spend. Potential under and overspend issues and opportunities for automated reclamation are also highlighted, managing cost and reducing risk.

Ivanti Neurons for Patch Intelligence and Ivanti Neurons for Spend Intelligence are available now as part of the Ivanti Neurons hyper-automation platform. In addition, enhancements across the Ivanti Neurons platform include broader out-of-the-box automation queries and actions to detect, diagnose and resolve issues, provide powerful data filtering to help IT make better decisions, and increase visibility through additional IT and non-IT devices and connector support.

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Our Michigan based IoT software development company, SpinDance, and Edge Impulse, the leading provider of embedded machine learning technology, have recently partnered to allow for the simplification of embedding machine learning technologies into existing smart devices and newly manufactured IoT products.

The partnership makes us the first Edge Impulse Solution Partner in the United States using the Edge Impulse platform to create and optimize machine learning models specifically for running on edge devices. Using the platform provides our experienced engineering team additional tools to bring machine learning to reality for our clients with positive impacts on the bottom line. We believe that combining this technology with SpinDance’s holistic, full-stack approach that has been part of our practice in software development for over twenty years will create powerful new opportunities for our clients.

Applications for Machine Learning in IoT
Use cases span a variety of industries, products, and applications. Advances are rapidly being made in areas such as agricultural, consumer, medical, and industrial spaces. One of the reasons for this is the ability of IoT devices to process sensor data like vibration, audio, vision, temperature, humidity, etc. in real-time to provide insights where and when they are needed.

In many cases today, IoT devices will collect the data, send it to the cloud, make machine learning inferences in the cloud, and then send the data back to the device. While this may be the right solution in some cases, it can be prohibitively slow, unreliable, costly, and/or present security concerns. The power of embedded machine learning is that it enables us to have the model run on the device which eliminates many of these obstacles. It minimizes battery consumption by massively reducing data communication needs and enables completely new applications for devices on low-bandwidth networks such as LPWAN or satellite. It can save costs on data transfers to the cloud and the power usage related to making these transfers. Depending on network latency and infrastructure design, we have seen latency reduced from a 1-second round-trip on inference to under 50ms. This can be a complete game-changer in time-critical applications. Also, distributing this processing load across devices in the field can save thousands of dollars in cloud computing and transfer costs. In the IoT space, data security is always something we need to be very cognizant of. If we can keep sensitive data on the device rather than sending it out, we reduce the security risks that inherently come with multiple network data transfers.

One simple example of the power of audio data and machine learning is with appliances. When the human brain hears unique sounds coming from an appliance it can promptly detect an issue, like a bearing going bad, a motor that is struggling, something out of balance, or a filter being clogged.

During a hot summer, my family enjoys the relief that a window air conditioner unit brings to our home. Living around a lot of trees means that debris can easily clog the drain in a window unit causing water to back up and spray around inside the unit. One day while sitting at my dinner table I heard a noise from the air conditioner – the likely sound of a clogged drain hole. Fortunately, I was home to unclog it and forgo possible damage to the unit. If I wasn’t home, wouldn’t it be better if the appliance was smart enough to know to shut off or at least notify me that there is a problem? This situation could be addressed with a water sensor, however, those come with their problems such as dealing with rain outside. I decided to test a theory that Edge Impulse could enable creating a solution for a smart air conditioner just using audio. Using Edge Impulses data collection API, I quickly built a small data set of three air conditioner states: Off, Normal, and Clogged. Within an hour, I had a model trained that was able to detect these differences with 95.5% accuracy. Without the platform, this model would’ve taken me several days to build.

I find that Edge Impulse reduces the time developers spend on the busy work of machine learning and allows them to focus their time on adding greater value to the project.

Streamlining Development for Better Business Outcomes
Advances in AI and machine learning technologies give companies the ability to gather, analyze, and leverage data to utilize for business advantages. This data can have impactful business outcomes including enhanced customer experiences. When coupled with streamlined IoT development in connected devices the potential to increase revenue can be significant.

Want to explore how to embed machine learning into your connected device(s)? Contact SpinDance for a technical discovery session.

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