BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260818T155452Z UID:248F5BA2-38E1-4756-B820-76475A04E4C4 DTSTART;TZID=America/New_York:20260917T173000 DTEND;TZID=America/New_York:20260917T183000 DESCRIPTION:Artificial intelligence is reshaping cloud infrastructure at a pace that is challenging long-standing assumptions about scale\, performan ce\, operations\, and control. As organizations deploy larger models\, acc elerate inference\, and expand AI-enabled services across production envir onments\, infrastructure teams must support new demands in compute\, stora ge\, networking\, capacity planning\, and reliability. At the same time\, these environments must remain observable\, resilient\, efficient\, and se cure.\nThis session examines how AI workloads are changing cloud infrastru cture design and operations\, and why observability is becoming a foundati onal requirement rather than an optional capability. It will explore the o perational pressures introduced by AI\, including dynamic workload behavio r\, higher infrastructure intensity\, faster failure propagation\, and the need for deeper telemetry across increasingly distributed systems. The pr esentation will also discuss practical strategies for building AI-ready cl oud platforms that improve visibility\, strengthen resilience\, optimize r esource efficiency\, and support long-term scalability in complex enterpri se environments.\n\nSpeaker(s): Sasi\, \n\nAgenda: \n1. Why AI is changing cloud infrastructure now\n\n- How AI workloads differ from traditional en terprise application patterns\n- Why model training\, fine-tuning\, retrie val\, and inference create new operational demands\n- The shift from cloud -native infrastructure to AI-ready infrastructure\n\n2. The core infrastru cture pressures introduced by AI\n\n- Higher demand on compute\, storage\, and network architectures\n- Capacity planning challenges in bursty and h igh-intensity environments\n- Latency\, throughput\, and utilization trade offs in AI-enabled systems\n\n3. Why observability becomes essential in AI -ready environments\n\n- The limits of conventional monitoring for modern AI workloads\n- The growing need for end-to-end telemetry across applicati ons\, platforms\, and infrastructure\n- Detecting performance degradation\ , resource contention\, anomalous behavior\, and failure patterns earlier\ n\n4. Building for resilience\, efficiency\, and operational control\n\n- Designing cloud environments that can absorb dynamic workload shifts\n- Im proving operational response through visibility\, automation\, and priorit ization\n- Balancing scale\, reliability\, and cost efficiency in producti on AI environments\n\n5. Practical architecture and operations strategies\ n\n- Foundational design principles for AI-ready cloud platforms\n- Streng thening observability across distributed services\, data flows\, and infra structure layers\n- Approaches for improving scalability and long-term sus tainability without losing governance and control\n\n6. What comes next\n\ n- Emerging infrastructure patterns for AI-enabled enterprises\n- How infr astructure teams should prepare for future operating models\n- Key takeawa ys for architects\, platform teams\, and technology leaders\n\nRoom: Netwo rking Room\, Bldg: Icon Co Work\, 17415 Northwood Ave\, #201\, Lakewood\, Ohio\, United States\, 44107\, Virtual: https://events.vtools.ieee.org/m/ 568378 LOCATION:Room: Networking Room\, Bldg: Icon Co Work\, 17415 Northwood Ave\, #201\, Lakewood\, Ohio\, United States\, 44107\, Virtual: https://events .vtools.ieee.org/m/568378 ORGANIZER:GFWhite@burnsmcd.com SEQUENCE:25 SUMMARY:Building AI-Ready Cloud Infrastructure: Strategies for Scale\, Obse rvability\, Resilience\, and Efficiency URL;VALUE=URI:https://events.vtools.ieee.org/m/568378 X-ALT-DESC:Description: <br /><p>Artificial intelligence is reshaping cloud infrastructure at a pace that is challenging long-standing assumptions ab out scale\, performance\, operations\, and control. As organizations deplo y larger models\, accelerate inference\, and expand AI-enabled services ac ross production environments\, infrastructure teams must support new deman ds in compute\, storage\, networking\, capacity planning\, and reliability . At the same time\, these environments must remain observable\, resilient \, efficient\, and secure.<br>This session examines how AI workloads are c hanging cloud infrastructure design and operations\, and why observability is becoming a foundational requirement rather than an optional capability . It will explore the operational pressures introduced by AI\, including d ynamic workload behavior\, higher infrastructure intensity\, faster failur e propagation\, and the need for deeper telemetry across increasingly dist ributed systems. The presentation will also discuss practical strategies f or building AI-ready cloud platforms that improve visibility\, strengthen resilience\, optimize resource efficiency\, and support long-term scalabil ity in complex enterprise environments.</p><br /><br />Agenda: <br /><p><s trong><span style="font-family: 'Aptos'\,sans-serif\; mso-bidi-font-family : Aptos\;">1. Why AI is changing cloud infrastructure now</span></strong>< /p>\n<ul type="disc">\n<li style="mso-list: l4 level1 lfo1\; tab-stops: li st .5in\;">How AI workloads differ from traditional enterprise application patterns</li>\n<li style="mso-list: l4 level1 lfo1\; tab-stops: list .5in \;">Why model training\, fine-tuning\, retrieval\, and inference create ne w operational demands</li>\n<li style="mso-list: l4 level1 lfo1\; tab-stop s: list .5in\;">The shift from cloud-native infrastructure to AI-ready inf rastructure</li>\n</ul>\n<p><strong><span style="font-family: 'Aptos'\,san s-serif\; mso-bidi-font-family: Aptos\;">2. The core infrastructure pressu res introduced by AI</span></strong></p>\n<ul type="disc">\n<li style="mso -list: l5 level1 lfo2\; tab-stops: list .5in\;">Higher demand on compute\, storage\, and network architectures</li>\n<li style="mso-list: l5 level1 lfo2\; tab-stops: list .5in\;">Capacity planning challenges in bursty and high-intensity environments</li>\n<li style="mso-list: l5 level1 lfo2\; ta b-stops: list .5in\;">Latency\, throughput\, and utilization tradeoffs in AI-enabled systems</li>\n</ul>\n<p><strong><span style="font-family: 'Apto s'\,sans-serif\; mso-bidi-font-family: Aptos\;">3. Why observability becom es essential in AI-ready environments</span></strong></p>\n<ul type="disc" >\n<li style="mso-list: l3 level1 lfo3\; tab-stops: list .5in\;">The limit s of conventional monitoring for modern AI workloads</li>\n<li style="mso- list: l3 level1 lfo3\; tab-stops: list .5in\;">The growing need for end-to -end telemetry across applications\, platforms\, and infrastructure</li>\n <li style="mso-list: l3 level1 lfo3\; tab-stops: list .5in\;">Detecting pe rformance degradation\, resource contention\, anomalous behavior\, and fai lure patterns earlier</li>\n</ul>\n<p><strong><span style="font-family: 'A ptos'\,sans-serif\; mso-bidi-font-family: Aptos\;">4. Building for resilie nce\, efficiency\, and operational control</span></strong></p>\n<ul type=" disc">\n<li style="mso-list: l2 level1 lfo4\; tab-stops: list .5in\;">Desi gning cloud environments that can absorb dynamic workload shifts</li>\n<li style="mso-list: l2 level1 lfo4\; tab-stops: list .5in\;">Improving opera tional response through visibility\, automation\, and prioritization</li>\ n<li style="mso-list: l2 level1 lfo4\; tab-stops: list .5in\;">Balancing s cale\, reliability\, and cost efficiency in production AI environments</li >\n</ul>\n<p><strong><span style="font-family: 'Aptos'\,sans-serif\; mso-b idi-font-family: Aptos\;">5. Practical architecture and operations strateg ies</span></strong></p>\n<ul type="disc">\n<li style="mso-list: l1 level1 lfo5\; tab-stops: list .5in\;">Foundational design principles for AI-ready cloud platforms</li>\n<li style="mso-list: l1 level1 lfo5\; tab-stops: li st .5in\;">Strengthening observability across distributed services\, data flows\, and infrastructure layers</li>\n<li style="mso-list: l1 level1 lfo 5\; tab-stops: list .5in\;">Approaches for improving scalability and long- term sustainability without losing governance and control</li>\n</ul>\n<p> <strong><span style="font-family: 'Aptos'\,sans-serif\; mso-bidi-font-fami ly: Aptos\;">6. What comes next</span></strong></p>\n<ul type="disc">\n<li style="mso-list: l0 level1 lfo6\; tab-stops: list .5in\;">Emerging infras tructure patterns for AI-enabled enterprises</li>\n<li style="mso-list: l0 level1 lfo6\; tab-stops: list .5in\;">How infrastructure teams should pre pare for future operating models</li>\n<li style="mso-list: l0 level1 lfo6 \; tab-stops: list .5in\;">Key takeaways for architects\, platform teams\, and technology leaders</li>\n</ul> END:VEVENT END:VCALENDAR