DES-1721 certification - Specialist - Implementation Engineer, SC Series Updated: 2023

At the recent SAP Sapphire conference in Orlando, SAP made several announcements showcasing its responsible AI, sustainability and “empowering customers to navigate complex challenges and transformations” in the cloud era.

In this article, I examine how SAP says it wants to help customers transform business models in the cloud, achieve greater visibility around sustainability and bolster supply chain resilience.

While AI algorithms have been around since the 1960s, it took decades to make the training and inference of these models affordable for everyone except national security agencies. Today, AI and machine learning permeates many of the applications we use today.

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Foundational models offer new applications for text, programming, numbers, images and videos that results more what everybody thought of when we thought of ”AI.” With tech company fortunes ebbing and flowing on how investors and customers see vendors applying AI, SAP wanted, understandable, to lean into AI more than it naturally would.

AI in the core of business systems is quite different that consumer AI- it’s not poems or planning the best week in Paris, it’s how company’s best deploy and manage human capital and durable assets. It’s corporate reputation, the lifeblood of a business, not playtime. Therefore, when the stakes are high, there’s little room for error. I think SAP knows this and demonstrated this at Sapphire.

Integration of AI capabilities across ERP

The SAP S/4HANA cloud will now include SAP's AI capabilities. The new features include SAP AI Business Services, which will assist finance teams in controlling costs and reducing risk by quickly identifying and responding to changes in customer sentiment. Sales teams could benefit from the automation of sales order functions, streamlining order processing by eliminating manual data entry.

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Spend management software and the SAP Business Network will now leverage AI to better help procurement professionals navigate market dynamics, engage with suppliers and enforce compliance. Features such as intelligent invoice conversion and AI-powered guided buying in SAP Ariba Buying will simplify procurement processes.

The Customer Experience software portfolio will receive an AI-powered companion application to Strengthen conversion rates and operational efficiency for sales, commerce and services teams. SAP S/4HANA Cloud will introduce built-in support for reporting and addressing problems within the software. Users can access tailored and proactive support content in real time and receive alerts for critical product issues.

SAP will embed AI into industry-specific solutions as well. For instance, SAP Predictive Replenishment will use AI to optimize inventory management by automatically calculating and ordering products. Sales teams can also utilize SAP Intelligent Product Recommendations, which suggests optimal product configurations based on AI-driven sales data analysis.

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AI will also enhance SAP's Human Experience Management software portfolio by streamlining human resources management. SAP SuccessFactors will leverage AI to understand the workforce's skills, allowing HR teams to provide continuous development opportunities and align employees to support the company better. The SAP SuccessFactors talent intelligence hub will offer employees a foundation to build and maintain a skills portfolio, with AI assisting them in showcasing skills and providing personalized recommendations for career paths, training projects and mentors.

Additionally, HR teams will gain access to AI-powered workforce management tools that consolidate workforce information, including the skills, cost and availability of employees and contract workers. This capability will enhance resource managers' visibility into available resources for various projects.

An expanded partnership with Microsoft

SAP has expanded its partnership with Microsoft to enhance human productivity and development in the workplace through generative AI. Integrating SAP SuccessFactors solutions with Microsoft 365 Copilot and Copilot in Microsoft Viva Learning aims to address skills gaps and Strengthen recruitment, retention and upskilling of the workforce. (For more on Copilot and its role in Microsoft products, check out our analysis here.) Meanwhile, Microsoft's Azure OpenAI Service provides advanced language models that analyze and generate natural language. This integration aims to revolutionize how organizations attract, retain and develop the workforce, offering new and improved experiences for talent management.

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SAP SuccessFactors has over 248 million users, and combined with Microsoft’s one billion Microsoft Office users and 280 million daily active Microsoft Teams users, this initiative certainly has the reach to transform business at scale.

The collaboration between SAP and Microsoft introduces three key innovations. First, generative AI capabilities will enhance recruiting and learning processes, providing hyper-personalized experiences within the flow of work. The second innovation is a talent intelligence hub that leverages AI to build and maintain a skills portfolio for each employee, enabling personalized recommendations for learning, mentors and internal jobs. Lastly, enhanced total workforce management functionality brings together SAP SuccessFactors, SAP Fieldglass and SAP S/4HANA Cloud to increase visibility across the organization.

An expanded partnership with Google

SAP has also expanded its partnership with Google to provide customers an open-data offering, enabling data integration across the enterprise landscape using SAP Datasphere and Google's Cloud.

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By combining SAP with Google Cloud's data and analytics technology, customers can create an end-to-end data cloud that provides real-time visibility into entire data estates, empowering organizations to maximize the value of investments in Google Cloud and SAP software, streamlining data integration and accelerating the development of AI models and analytics engines.

Ledger-based accounting for carbon tracking

There is a growing need to track, measure, monitor, and report on carbon emissions. SAP is extending its ERP software's definition of resources to include carbon, thus introducing the green ledger initiative. This initiative allows companies to manage carbon emissions with the same transparency and reliability as financial data. SAP is also updating its Sustainability Footprint Management solution and introducing the Sustainability Data Exchange application to facilitate the exchange of standardized sustainability data with partners and suppliers.

Industry-specific networks to enhance supply chain resilience

SAP also announced the SAP Business Network for Industry, which enables customers in specific industries to Strengthen and extend key business processes with trading partners.

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SAP Business Network for Industry targets common supply chain collaboration use cases in specific industries. These industry-specific use cases are paired with industry best practices and are all prepackaged for quick implementation and ready use. Today's focus sectors are consumer products, high-tech, industrial manufacturing and life sciences—with more to come.

Wrapping up

Considering the booming interest in AI and particularly generative AI applications, SAP's announcements could not be timelier. SAP customers should sleep well knowing that SAP is planning to leverage generative AI across the ERP suite in ways that are relevant to business needs, designed responsibly and aligned with ethical standards.

Finally, SAP has also increased its commitment to recruiting professional developers and announced new programs to meet the growing demand for SAP experts across the ecosystem. That can only be good news for customers embroiled in ongoing business transformations to the cloud.

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Moor Insights & Strategy provides or has provided paid services to technology companies like all research and tech industry analyst firms. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking, and video and speaking sponsorships. The company has had or currently has paid business relationships with 8×8, Accenture, A10 Networks, Advanced Micro Devices, Amazon, Amazon Web Services, Ambient Scientific, Ampere Computing, Anuta Networks, Applied Brain Research, Applied Micro, Apstra, Arm, Aruba Networks (now HPE), Atom Computing, AT&T, Aura, Automation Anywhere, AWS, A-10 Strategies, Bitfusion, Blaize, Box, Broadcom, C3.AI, Calix, Cadence Systems, Campfire, Cisco Systems, Clear Software, Cloudera, Clumio, Cohesity, Cognitive Systems, CompuCom, Cradlepoint, CyberArk, Dell, Dell EMC, Dell Technologies, Diablo Technologies, Dialogue Group, Digital Optics, Dreamium Labs, D-Wave, Echelon, Ericsson, Extreme Networks, Five9, Flex, Foundries.io, Foxconn, Frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Revolve (now Google), Google Cloud, Graphcore, Groq, Hiregenics, Hotwire Global, HP Inc., Hewlett Packard Enterprise, Honeywell, Huawei Technologies, HYCU, IBM, Infinidat, Infoblox, Infosys, Inseego, IonQ, IonVR, Inseego, Infosys, Infiot, Intel, Interdigital, Jabil Circuit, Juniper Networks, Keysight, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation, Lightbits Labs, LogicMonitor, LoRa Alliance, Luminar, MapBox, Marvell Technology, Mavenir, Marseille Inc, Mayfair Equity, Meraki (Cisco), Merck KGaA, Mesophere, Micron Technology, Microsoft, MiTEL, Mojo Networks, MongoDB, Multefire Alliance, National Instruments, Neat, NetApp, Nightwatch, NOKIA, Nortek, Novumind, NVIDIA, Nutanix, Nuvia (now Qualcomm), NXP, onsemi, ONUG, OpenStack Foundation, Oracle, Palo Alto Networks, Panasas, Peraso, Pexip, Pixelworks, Plume Design, PlusAI, Poly (formerly Plantronics), Portworx, Pure Storage, Qualcomm, Quantinuum, Rackspace, Rambus, Rayvolt E-Bikes, Red Hat, Renesas, Residio, Samsung Electronics, Samsung Semi, SAP, SAS, Scale Computing, Schneider Electric, SiFive, Silver Peak (now Aruba-HPE), SkyWorks, SONY Optical Storage, Splunk, Springpath (now Cisco), Spirent, Splunk, Sprint (now T-Mobile), Stratus Technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, Telesign,TE Connectivity, TensTorrent, Tobii Technology, Teradata,T-Mobile, Treasure Data, Twitter, Unity Technologies, UiPath, Verizon Communications, VAST Data, Ventana Micro Systems, Vidyo, VMware, Wave Computing, Wellsmith, Xilinx, Zayo, Zebra, Zededa, Zendesk, Zoho, Zoom, and Zscaler. Moor Insights & Strategy founder, CEO, and Chief Analyst Patrick Moorhead is an investor in dMY Technology Group Inc. VI, Fivestone Partners, Frore Systems, Groq, MemryX, Movandi, and Ventana Micro., MemryX, Movandi, and Ventana Micro.

Dell EMC is waging an all-out assault on the storage market, investing $2 billion in the effort, hiring 1,200 new storage sales certified and offering new, robust, storage compensation incentives for its partners. A trio of executives leading the charge - Scott Millard, Joyce Mullen and Marius Haas - talked to CRN about how Dell EMC is putting the pieces in place to help its partners win big in storage.

'Refuse To Lose': Dell EMC Primes Its Partners For A Storage Revolution
Driving the company's storage sales offensive are new robust storage compensation incentives for solution providers and an influx of Dell EMC sales reps working side by side with those partners.

Dell EMC President Marius Haas On The Company's 'Refuse To Lose Approach' To The Storage Market
Dell EMC's Marius Haas on the Dell EMC's storage push and how channel partners are vital to Dell EMC's success.

Storage Boost: Dell Hires 1,200 Sales certified To Drive Partner-Led Selling
By hiring 1,200 new storage sales specialists, Dell boosts its storage team by upward of 25 percent with an eye on skyrocketing storage revenue through its partners in 2018.

10 Hot Dell EMC Storage Products
CRN looks at 10 key products in the Dell EMC storage lineup, one that offers the broad capabilities to match the company's position as the world’s largest storage vendor.

Tech Tour: Behind The Scenes At Dell EMC's Storage Manufacturing Facility
CRN recently toured Dell EMC's storage manufacturing facility in Massachusetts which assembles, tests and ships converged and hyper-converged products throughout North America.

CRN Interview: Dell EMC Channel Chief Joyce Mullen On Driving More Storage Sales Through Partners And Her Channel Vision For 2018
Joyce Mullen, an 18-year Dell veteran who is now president of global channels, OEM and IoT Solutions, spoke with CRN about enabling partners to drive more storage sales, the potential integration of VMware into the Dell EMC Partner Program, and her channel vision for 2018.

Dell EMC Doubles Down On Storage Services, Unifying Portfolio
Dell EMC executives are making sure partners have the competencies they need to sell the Dell EMC portfolio as well as their own services.

Fistfuls Of Growth: Dell EMC's Expanded Product Portfolio Paves New Paths For Partners
Michael Dell has emphasized that customers want cutting-edge hybrid cloud tech and they want to buy it from fewer vendors. Here's how three solution providers backed up Dell's assertion with skyrocketing sales across Dell EMC's broad portfolio in 2017.

Classical machine learning (ML) is a powerful subset of artificial intelligence. Machine learning has advanced from simple pattern recognition in the 1960s to today's advanced use of massive datasets for training and the generation of highly accurate predictions.

Meanwhile, between 2010 and 2020, global data usage increased from 1.2 trillion gigabytes to almost 60 trillion gigabytes. At some point, quantum systems will more easily handle the ongoing exponential growth in data compared to classical computers, which may struggle to keep up. Theoretically, at some point in the not-too-distant future, only quantum computers can handle such massive scale and complexity. Applying this same insight to the realm of ML, it only makes sense that at some point, the real breakthroughs will be coming from quantum machine learning (QML) rather than classical approaches.

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IonQ

Although other quantum computing companies are exploring QML, there are several reasons I have focused on advanced QML research being done at IonQ ($IONQ).

One, IonQ's CEO, Peter Chapman, has a rich background in machine learning when he worked with Ray Kurzweil at Kurzweil Technologies. Chapman played a crucial role in developing a pioneering character recognition system that generated text characters from scanned images.urzweil Technologies eventually used that approach to build a comprehensive digital library for the blind and visually impaired.

Two, Chapman is optimistic about the future of QML. He believes that QML will eventually be as significant as the large language models used by OpenAI's ChatGPT and other generative AI systems. For that reason,QML is built into IonQ's long-term quantum product roadmap.

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And three, IonQ collaborates with leading companies in the field of AI and machine learning, such as Amazon, Dell, Microsoft, and NVIDIA. These partnerships combine IonQ's expertise in quantum technology with the partner's AI knowledge of their partners.

IonQ hardware and #AQ

IonQ's primary focus is not just on qubit quantity but more comprehensively on the quality of the qubits and how they operate as a system. This quality—also called qubit fidelity—is a critical differentiator for efficiently completing quantum computations, one that IonQ measures with an application-oriented benchmark that it calls algorithmic qubits or #AQ.

#AQ is based on work pioneered by the Quantum Economic Development Consortium, an independent industry group that evaluates quantum computer utility in real-world settings. Here is how #AQ is computed.

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IonQ quantum processors

IonQ has created three trapped-ion quantum computers: IonQ Harmony, IonQ Aria and its latest model, a software-defined quantum computer called IonQ Forte.

There are two Arias online. According to Chapman, the second Aria machine was needed to handle increased customer demand and to Strengthen the company's redundancy, capacity and order processing speed.

Additionally, IonQ is working hard to make the IonQ Forte commercially available

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IonQ Aria and IonQ Harmony are cloud accessible via Google, Amazon Braket, Microsoft Azure and IonQ Quantum Cloud. According to the company, cloud access for IonQ Forte will be announced later. Let's take a deeper look at the different quantum computers that IonQ has built:

• IonQ Harmony was the first commercial quantum computer introduced by IonQ. It is dynamically reconfigurable in software to use up to 11 qubits with an #AQ of 9. All qubits are fully connected, meaning you can run a two-qubit gate between any pair of them. It is an excellent processor with an efficient backend for small-scale proof-of-concept work and compatible with most quantum SDKs.
• IonQ Aria is IonQ's fifth-generation quantum machine. It is available on the IonQ Quantum Cloud and all public clouds. With an #AQ of 25, a higher qubit count and high gate fidelities, IonQ Aria enables computation for more complex problems. Its #AQ of 25 also means less noise in the quantum system. With less noise, even the most complex problems take fewer iterations, saving valuable time and money.
• IonQ Forte is IonQ's latest quantum computer; it boasts enhanced flexibility, precision and performance. IonQ Forte is equipped with highly specialized acousto-optic deflectors (AODs) to direct laser beams at individual qubits in the ion chain to apply logic gates among the qubits. The processor has a capacity of up to 32 qubits like the IonQ Aria, and it is further expandable in software.

Forte recently demonstrated a record 29 AQ, which puts it seven months ahead of IonQ's original AQ goal for 2023.

Note: IonQ's next major technical milestone is achieving 35 AQ. At the 35 AQ level, using classical hardware to simulate quantum algorithms can become very challenging and costly. At that point, IonQ believes it will be easier and less expensive for some customers to run models on actual quantum machines rather than attempting to simulate them classically.

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ML + QC = QML

Even though quantum computing is still being carried out by mid-stage prototypes, it has the potential, perhaps within this decade, to solve problems far beyond the capability of classical supercomputers. Meanwhile, as quantum computing prototypes are getting closer to becoming operationally sound, scaled versions of classical ML models are already being used in hundreds of thousands of applications across almost every industry. These range from personalized recommendations on shopping sites to critical healthcare diagnostics, such as analyzing X-rays and MRI scans to detect diseases more accurately than humans can.

QML is a still-developing field that uses quantum computers for challenging ML tasks, even though at this point quantum machines are less practical than classical computers. Combining ML and quantum computing (QC) to produce QML creates a technology that should soon be even more powerful than classical machine learning.

According to Peter Chapman, much of today's QML is created by converting classical machine learning algorithms into quantum algorithms. QML is not without challenges. It has many of the same problems as those associated with current quantum computers, the most prevalent being susceptibility to errors caused by environmental noise and decoherence due to prototype hardware limitations.

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"Look at the past research we've done with Fidelity, GE, Hyundai and a few others," Chapman said. "All those projects started with regular machine learning algorithms before we converted them to quantum algorithms."

He explained, however, that IonQ's research has shown QML performance to be superior to many of its classical ML counterparts. "Our QML versions beat comparable classical ML versions," he said. "Sometimes the results show that the QML model did a better job capturing the signal in the data, or sometimes the number of iterations needed to go through the data was substantially less. And sometimes, as our most recent research indicates, the data needed for QML was about 8,000 times less than a classical model needs."

Why QML performs better than classical ML

QML uses superposition and entanglement, two principles of quantum mechanics, to develop new machine learning algorithms. Quantum superposition allows qubits to be in multiple states simultaneously, whereas quantum entanglement allows many qubits to share the same state. This is in contrast to classical physics, where a bit can be in only one state at a time, and where connectivity between bits is only possible by physical means. The relevant quantum properties allow developers to create QML algorithms to solve problems that are intractable using classical computers.

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It is important to note that QML is still in its early stages of development. It is not yet powerful enough to solve very large and very complex machine learning problems. Still, QML has the potential to revolutionize classical machine learning by training models faster, providing greater accuracy and opening the door for newer and even more powerful algorithms.

Quantum artificial intelligence

Quantum AI is even newer than QML. About a year ago, IonQ started exploring quantum AI. Its first research effort produced a paper on modeling human cognition that was published in the peer-reviewed scientific journal Entropy. The paper shows that human decision making can be tested on quantum computers. Since the 1960s, researchers have found that people don't always follow the rules of classical probability when making decisions. For instance, the sequence in which people are asked questions can influence their answers. Quantum probability helps clarify that oddity.

The research paper doesn't say that the brain explicitly operates on using quantum mechanics. Instead, it applies the same mathematical structures to both fields, which adds to the intrigue of using quantum computers to simulate human cognition.

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"We are excited by the potential for quantum to not only add power to machine learning but to artificial general intelligence or AGI as well," Chapman said. "AGI is the point at which AI is strong enough to accomplish any task that a human can. Some things are almost impossible to model on a classical computer but are possible on a quantum computer. And, I think that AGI will likely be where these kinds of problem sets will be done."

Wrapping up

Quantum Machine Learning is still an emerging field. It is the intersection where techniques from quantum information processing, machine learning and optimization come together to solve problems faster and more accurately than classical machine learning.

It is possible to use classical machine learning algorithms and convert them to quantum machine learning. IonQ has done this successfully several times. These QML models often outperform the original ML models.

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QML offers several advantages over traditional machine learning thanks to quantum mechanics in the form of superposition and entanglement. QML can complement the growing trend of using ML models for many classification tasks, from image recognition to NLP.

Analyst's notes:

Here are a few IonQ QML-related research papers I found interesting:

January 2023 — Quantum natural language processing (QNLP) is a subfield of machine learning that focuses on developing algorithms that can process and understand natural language (i.e., the languages spoken by humans). IonQ researchers demonstrated that statistically meaningful results can be obtained using real datasets, even though it is much more difficult to predict than with easier artificial language examples used previously in developing quantum NLP systems. Other approaches to quantum NLP are compared, partly with respect to contemporary issues including informal language, fluency and truthfulness.

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January 2023 — Research by IonQ focused on text classification with QNLP. This research demonstrated that an amplitude encoded feature map combined with a quantum support vector machine can achieve 62% average accuracy predicting sentiment using a dataset of 50 actual movie reviews. This is small, but considerably larger than previously-reported results using quantum NLP.

November 2022 — This joint research by IonQ, the Fidelity Center for Applied Technology (FCAT) and Fidelity Investments focuses on generative quantum learning of joint probability distribution functions—GANs, QGANs and QCBMs—all of which use machine learning to learn from data and make predictions. The research demonstrates that a relationship between two or more variables can be represented by a quantum state of multiple particles. This is important because it shows that quantum computers can be used to model and understand complex relationships between variables.

November 2021 — IonQ and Zapata Computing developed the first practical and experimental implementation of a hybrid quantum-classical QML algorithm that can generate high-resolution images of handwritten digits. The results outperformed comparable classical generative adversarial networks (GANs) trained on the same database. GAN is a machine learning model with two neural networks that compete against each other to produce the most accurate prediction.

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September 2021 — Researchers from IonQ and FCAT developed a proof-of-concept QML model to analyze numerical relationships in the daily returns of Apple and Microsoft stock from 2010 to 2018. Daily returns are the price of a stock at the daily closure compared to its price at the previous day's closure. The metric measures daily stock performance. The model demonstrated that quantum computers can be used to generate correlations which cannot be efficiently reproduced by classical means, such as probability distribution.

December 2020 — In a partnership between IonQ and QC Ware, classical data was loaded onto quantum states to allow efficient and robust QML applications. Machine learning achieved the same level of accuracy and ran faster than on classical computers. The project used QC Ware's Forge Data Loader technology to transform classical data onto quantum states. The quantum algorithm, running on IonQ's hardware, performed at the same level as the classical algorithm, identifying the correct digits eight out of 10 times on average.

Paul Smith-Goodson is the Vice President and Principal Analyst covering AI and quantum for Moor Insights & Strategy. He is currently working on several personal research projects, one of which is a unique method of using machine learning and ionospheric data collected from a national network of HF transceivers for highly accurate prediction of real-time and future global propagation of HF radio signals.

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For current information on these subjects, you can follow him on Twitter.

Moor Insights & Strategy provides or has provided paid services to technology companies like all research and tech industry analyst firms. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking, and video and speaking sponsorships. The company has had or currently has paid business relationships with 8×8, Accenture, A10 Networks, Advanced Micro Devices, Amazon, Amazon Web Services, Ambient Scientific, Ampere Computing, Anuta Networks, Applied Brain Research, Applied Micro, Apstra, Arm, Aruba Networks (now HPE), Atom Computing, AT&T, Aura, Automation Anywhere, AWS, A-10 Strategies, Bitfusion, Blaize, Box, Broadcom, C3.AI, Calix, Cadence Systems, Campfire, Cisco Systems, Clear Software, Cloudera, Clumio, Cohesity, Cognitive Systems, CompuCom, Cradlepoint, CyberArk, Dell, Dell EMC, Dell Technologies, Diablo Technologies, Dialogue Group, Digital Optics, Dreamium Labs, D-Wave, Echelon, Ericsson, Extreme Networks, Five9, Flex, Foundries.io, Foxconn, Frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Revolve (now Google), Google Cloud, Graphcore, Groq, Hiregenics, Hotwire Global, HP Inc., Hewlett Packard Enterprise, Honeywell, Huawei Technologies, HYCU, IBM, Infinidat, Infoblox, Infosys, Inseego, IonQ, IonVR, Inseego, Infosys, Infiot, Intel, Interdigital, Jabil Circuit, Juniper Networks, Keysight, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation, Lightbits Labs, LogicMonitor, LoRa Alliance, Luminar, MapBox, Marvell Technology, Mavenir, Marseille Inc, Mayfair Equity, Meraki (Cisco), Merck KGaA, Mesophere, Micron Technology, Microsoft, MiTEL, Mojo Networks, MongoDB, Multefire Alliance, National Instruments, Neat, NetApp, Nightwatch, NOKIA, Nortek, Novumind, NVIDIA, Nutanix, Nuvia (now Qualcomm), NXP, onsemi, ONUG, OpenStack Foundation, Oracle, Palo Alto Networks, Panasas, Peraso, Pexip, Pixelworks, Plume Design, PlusAI, Poly (formerly Plantronics), Portworx, Pure Storage, Qualcomm, Quantinuum, Rackspace, Rambus, Rayvolt E-Bikes, Red Hat, Renesas, Residio, Samsung Electronics, Samsung Semi, SAP, SAS, Scale Computing, Schneider Electric, SiFive, Silver Peak (now Aruba-HPE), SkyWorks, SONY Optical Storage, Splunk, Springpath (now Cisco), Spirent, Splunk, Sprint (now T-Mobile), Stratus Technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, Telesign,TE Connectivity, TensTorrent, Tobii Technology, Teradata,T-Mobile, Treasure Data, Twitter, Unity Technologies, UiPath, Verizon Communications, VAST Data, Ventana Micro Systems, Vidyo, VMware, Wave Computing, Wellsmith, Xilinx, Zayo, Zebra, Zededa, Zendesk, Zoho, Zoom, and Zscaler. Moor Insights & Strategy founder, CEO, and Chief Analyst Patrick Moorhead is an investor in dMY Technology Group Inc. VI, Fivestone Partners, Frore Systems, Groq, MemryX, Movandi, and Ventana Micro., MemryX, Movandi, and Ventana Micro.

Don Granato offered insight into the Buffalo Sabres' goalie competition after the team held its intrasquad scrimmage on Sunday, giving the early edge to veteran newcomers Craig Anderson and Aaron Dell on merit of experience. 

"If it started tomorrow, those two guys would be one and two," Granato said. 

The Sabres signed Anderson and Dell on the opening night of free agency to replace the departed tandem of Linus Ullmark and Carter Hutton. They joined an organizational depth chart that includes Ukko-Pekka Luukkonen and Dustin Tokarski, both of whom played games for Buffalo last season. 

Anderson, 40, joins Buffalo for his 19th NHL season. His 652 regular-season games rank fourth among active netminders. 

Dell, 31, went undrafted and climbed the ladder in various pro leagues before making his debut with San Jose in 2016. He played four years with the Sharks prior to last season, which he split between Toronto and New Jersey. 

"They've got, really, the first opportunity," Granato said. "They've got the better track record at this level. So, we'll be watching those guys, I guess, because they should play. Their resume says they're the guys who can handle this."

Video: TRAINING CAMP: Dell

Both goaltenders enter training camp with plenty to prove. Anderson settled into a new role with Washington last season after 10 years in Ottawa, serving as a veteran third option behind the Capitals' young tandem of Ilya Samsonov and Vitek Vanecek. He posted a .915 save percentage in four regular-season appearances and a .929 mark during two playoff outings. 

Dell posted a .908 save percentage during his four seasons in San Jose prior to signing with Toronto last offseason, though he never did appear in a game for the Maple Leafs. By the time he started his seven-game stint with the Devils - who claimed him off waivers in February - it had been nearly a full year since his last outing.

"It was tough to get to get into a groove at all," he said. 

Sabres general manager Kevyn Adams made it clear that the organization is willing to be patient with the 22-year-old Luukkonen, who had a four-game stint with the NHL club to end last season. Though the opportunity exists for Luukkonen to earn a roster spot over the next two weeks, his performance will need to indicate that he is ready. 

Anderson and Dell have both expressed willingness to serve as veteran mentors, both for Luukkonen and the other young players who project to comprise the bulk of this year's roster. In the meantime, they will seek to earn their own share of the crease. Both goalies posted shutout periods during the intrasquad scrimmage on Sunday. 

"I think it's gone pretty well so far," Dell said. "Everyone's played pretty well so far in the scrimmage and in practice and stuff like that. I'm just going to do my work and I'm excited to see where it goes."

Hagg's fresh start

Robert Hagg joked when asked to describe his playing style after practice on Monday. 

"Power-play quarterback, putting up a lot of points," he deadpanned. "No, not really. Strong defensively. That's what I want to be. Hard to play against. Physical. Makes simple passes out of the D zone and follow up. But I would say the defensive side is probably my strongest game."

Still, the 26-year-old defenseman believes he has untapped offensive upside that he hopes to develop this season. Hagg said he was excited to learn about the July trade from Philadelphia, with whom he spent his first four NHL seasons. Granato and general manager Kevyn Adams both reached out to discuss their plan for the season. 

"They said they really wanted me here," Hagg said. "I found a lot of energy, came in here early to get to know everybody and I'm really excited to get this going. It's a fresh start for me with new coaches, new teammates."

Video: TRAINING CAMP: Hagg
 
Hagg earned a reputation as a physical defenseman during his tenure in Philadelphia. He ranks sixth among NHL defensemen in hits since his first full season of 2017-18, with 732. He hopes being evaluated by a new set of eyes will lead for an opportunity to do more with the puck on his stick. 

"I want to help the team out more offensively, not only defensively," Hagg said. "But at the same time, I know my role. I know what type of player I am and I'm not going to try and be someone else."

Monday's practice sessions

Video: TRAINING CAMP REPORT

The Sabres trimmed their camp roster to 55 players, returning 2021 draft picks Josh Bloom, Viljami Marjala, and Olivier Nadeau to their junior clubs. They once again split into two practice groups:

Gold Group

53 Jeff Skinner - 37 Casey Mittelstadt - 71 Victor Olofsson
91 Drake Caggiula - 74 Rasmus Asplund - 72 Tage Thompson
42 Brendan Warren - 20 Cody Eakin - 21 Kyle Okposo
73 Matej Pekar - 43 Sean Malone - 36 Ryan Scarfo / 51 Dominic Franco 

26 Rasmus Dahlin - 10 Henri Jokiharju
8 Robert Hagg - 33 Colin Miller
75 Josh Teves - 13 Mark Pysyk
56 Matthew Cairns - 38 Nick Boka

41 Craig Anderson
1 Ukko-Pekka Luukkonen
32 Michael Houser

Blue Group

77 JJ Peterka - 24 Dylan Cozens - 22 Jack Quinn
96 Anders Bjork - 28 Zemgus Girgensons - 29 Vinnie Hinostroza
57 Brett Murray - 25 Arttu Ruotsalainen - 15 John Hayden
65 Linus Weissbach - 49 Ryan MacInnis - 27 Michael Mersch

78 Jacob Bryson - 64 Oskari Laaksonen
4 Will Butcher - 6 Ethan Prow
67 Peter Tischke - Mitch Eliot

80 Aaron Dell
41 Dustin Tokarski

Buffalo opens its preseason exhibition schedule at Columbus on Tuesday, with puck drop set for 7 p.m.