BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Business Development Institute (BDI) - ECPv6.15.20//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://bdionline.com X-WR-CALDESC:Events for Business Development Institute (BDI) REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20250309T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20251102T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20260308T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20261101T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20270314T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20271107T060000 END:STANDARD END:VTIMEZONE BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 TZNAME:CDT DTSTART:20250309T080000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 TZNAME:CST DTSTART:20251102T070000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 TZNAME:CDT DTSTART:20260308T080000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 TZNAME:CST DTSTART:20261101T070000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 TZNAME:CDT DTSTART:20270314T080000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 TZNAME:CST DTSTART:20271107T070000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/New_York:20260519T120000 DTEND;TZID=America/New_York:20260519T140000 DTSTAMP:20260416T054657 CREATED:20260330T195659Z LAST-MODIFIED:20260415T160905Z UID:121176-1779192000-1779199200@bdionline.com SUMMARY:Managing Risk Beyond the Traditional Perimeter: Building Consistent Controls Across Physical and Digital Environments DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 2026   \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake URL:https://bdionline.com/event/051926/ LOCATION:Noa\, 77 S 7th St\, Minneapolis\, MN\, 55402\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/03/Envoy-Minnepolis.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260520T080000 DTEND;TZID=America/New_York:20260520T170000 DTSTAMP:20260416T054657 CREATED:20260319T172049Z LAST-MODIFIED:20260413T224622Z UID:120978-1779264000-1779296400@bdionline.com SUMMARY:Operationalizing AI at Scale: Moving Enterprise AI from Pilot to Production DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 2026   \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake URL:https://bdionline.com/event/052026/ LOCATION:Harry Caray’s\, 33 W. KINZIE STREET\, CHICAGO\, IL\, 60654\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/03/hpe-nvidia-chicago2.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Chicago:20260602T120000 DTEND;TZID=America/Chicago:20260602T140000 DTSTAMP:20260416T054657 CREATED:20260320T182535Z LAST-MODIFIED:20260415T183053Z UID:121008-1780401600-1780408800@bdionline.com SUMMARY:Ignoring Culture in the Age of AI: The Hidden Cost During Times of Uncertainty DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 2026   \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake URL:https://bdionline.com/event/060226/ LOCATION:Remington’s\, 20 N Michigan Ave\,\, Chicago\, IL\, 60602\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/webp:https://bdionline.com/wp-content/uploads/2026/03/Untitled-1-20-03-2026-20-31-43.webp END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260603T173000 DTEND;TZID=America/New_York:20260603T200000 DTSTAMP:20260416T054657 CREATED:20260325T193949Z LAST-MODIFIED:20260414T165746Z UID:121062-1780507800-1780516800@bdionline.com SUMMARY:Managing Risk Beyond the Traditional Perimeter: Building Consistent Controls Across Physical and Digital Environments DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 2026   \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake URL:https://bdionline.com/event/060326/ LOCATION:Davios Back Bay\, 75 Arlington Street\, Boston\, MA\, 02116\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/03/envoy-boston.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260604T173000 DTEND;TZID=America/New_York:20260604T200000 DTSTAMP:20260416T054657 CREATED:20260414T184927Z LAST-MODIFIED:20260415T162233Z UID:121390-1780594200-1780603200@bdionline.com SUMMARY:The Rise of Agentic Financial Operations DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 2026   \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake URL:https://bdionline.com/event/060426/ LOCATION:Capital Grille (New York City)\, 120 W 51st St\, New York\, NY\, 10020\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/04/Blackline-nyc.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260611T173000 DTEND;TZID=America/New_York:20260611T200000 DTSTAMP:20260416T054657 CREATED:20260413T194503Z LAST-MODIFIED:20260413T201847Z UID:121347-1781199000-1781208000@bdionline.com SUMMARY:Building the Enterprise AI Factory: From Experimentation to Execution DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 2026   \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake URL:https://bdionline.com/event/061126/ LOCATION:The Sea by Alexander’s Steakhouse\, 4269 W El Camino Real\, Palo Alto\, CA\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/04/Andela-Palo-Alto.png END:VEVENT END:VCALENDAR