{"id":10977,"date":"2026-01-22T14:51:00","date_gmt":"2026-01-22T13:51:00","guid":{"rendered":"https:\/\/euroccitaly.it\/?p=10977"},"modified":"2026-01-23T16:18:32","modified_gmt":"2026-01-23T15:18:32","slug":"a-test-before-invest-approach-for-scalable-and-controllable-ai","status":"publish","type":"post","link":"https:\/\/euroccitaly.it\/en\/news\/a-test-before-invest-approach-for-scalable-and-controllable-ai\/","title":{"rendered":"A test-before-invest approach for scalable and controllable AI"},"content":{"rendered":"<p data-start=\"75\" data-end=\"313\">The emergence of new technologies\u2014particularly Artificial Intelligence\u2014within the business landscape brings significant opportunities and benefits for organizational ecosystems, while also introducing a set of <strong>important and non-trivial challenges<\/strong>. For example, for many SMEs, the challenge of adopting Artificial Intelligence is not access to GPUs or advanced infrastructures, but <strong>the risk of investing time and budget<\/strong> into AI initiatives that never move beyond experimentation.<\/p>\n<p data-start=\"75\" data-end=\"313\">For this reason, EuroCC Italy is organizing a new training course aimed at introducing a test-before-invest approach to the design and operation of AI workloads in HPC and cloud environments: \u201c<strong>A test-before-invest approach for scalable and controllable AI<\/strong>\u201d, scheduled for 26 February at the BI-REX premises in Bologna.<\/p>\n<h4>A test-before-invest approach for scalable and controllable AI: a new EuroCC Italy course<\/h4>\n<p><strong>When:<span>\u00a0<\/span><\/strong>26nd February 2026, h 9.00 a.m.\u00a0 \u2013 1.00 p.m.\u00a0<strong><br \/>\nWhere:<span>\u00a0<\/span><\/strong>BI-REX,<span>\u00a0<\/span><span>Via Paolo Nanni Costa 14 (Bologna)<\/span><\/p>\n<p>The objective of the course\u2014designed and developed within the EuroCC Italy project\u2014is not to focus on specific platforms or tools, but rather to provide <strong>practical design patterns and operational principles<\/strong>. In doing so, organizations will be able to:<\/p>\n<ul>\n<li>validate AI workloads at an early stage<\/li>\n<li>keep execution costs under control<\/li>\n<li>make informed decisions before scaling their investments<\/li>\n<\/ul>\n<p>Participants will learn how to structure AI workloads so they can be <strong>tested under real conditions <\/strong>&#8211;\u00a0long-running jobs, growing datasets, and limited computational budgets &#8211; while remaining reproducible and observable. A <strong>guided design exercise<\/strong> will help participants translate these concepts into their own use cases, identifying technical and operational trade-offs and defining clear criteria to decide when an AI workload is ready to scale\u2014and when it is not.<\/p>\n<h5>Why attends<\/h5>\n<p data-start=\"1662\" data-end=\"1728\">By the end of the training, participants will be able to:<\/p>\n<ul data-start=\"1729\" data-end=\"2599\">\n<li data-start=\"1729\" data-end=\"1862\">\n<p data-start=\"1731\" data-end=\"1862\"><strong>Understand<\/strong> why many AI workloads fail to scale beyond the proof-of-concept phase in HPC and cloud environments<\/p>\n<\/li>\n<li data-start=\"1863\" data-end=\"2016\">\n<p data-start=\"1865\" data-end=\"2016\"><strong>Distinguish<\/strong> between different types of AI workloads (exploratory, batch, production-like) and select appropriate execution environments<\/p>\n<\/li>\n<li data-start=\"2017\" data-end=\"2131\">\n<p data-start=\"2019\" data-end=\"2131\"><strong>Design<\/strong> hybrid HPC\u2013cloud architectures using reproducible and container-based execution models<\/p>\n<\/li>\n<li data-start=\"2132\" data-end=\"2256\">\n<p data-start=\"2134\" data-end=\"2256\"><strong>Execute<\/strong> and <strong>manage<\/strong> long-running AI jobs, including fault tolerance and resource management considerations<\/p>\n<\/li>\n<li data-start=\"2257\" data-end=\"2379\">\n<p data-start=\"2259\" data-end=\"2379\"><strong>Identify<\/strong> key observability signals for AI workloads, covering jobs, data, models, and outputs<\/p>\n<\/li>\n<li data-start=\"2380\" data-end=\"2523\">\n<p data-start=\"2382\" data-end=\"2523\"><strong>Understand<\/strong> the principles of AI model lifecycle management, including versioning, monitoring, and re-training strategies<\/p>\n<\/li>\n<li data-start=\"2524\" data-end=\"2599\">\n<p data-start=\"2526\" data-end=\"2599\"><strong>Evaluate<\/strong> architectural and operational trade-offs in real-world AI workflows<\/p>\n<\/li>\n<\/ul>\n<h5>Topic<\/h5>\n<div class=\"flex flex-col text-sm pb-25\">\n<article class=\"text-token-text-primary w-full focus:outline-none [--shadow-height:45px] has-data-writing-block:pointer-events-none has-data-writing-block:-mt-(--shadow-height) has-data-writing-block:pt-(--shadow-height) [&amp;:has([data-writing-block])&gt;*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]\" dir=\"auto\" tabindex=\"-1\" data-turn-id=\"request-WEB:d4c3d8c2-7127-425f-ad8d-56247f81e86a-4\" data-testid=\"conversation-turn-10\" data-scroll-anchor=\"true\" data-turn=\"assistant\">\n<div class=\"text-base my-auto mx-auto pb-10 [--thread-content-margin:--spacing(4)] @w-sm\/main:[--thread-content-margin:--spacing(6)] @w-lg\/main:[--thread-content-margin:--spacing(16)] px-(--thread-content-margin)\">\n<div class=\"[--thread-content-max-width:40rem] @w-lg\/main:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group\/turn-messages focus-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn\" tabindex=\"-1\">\n<div class=\"flex max-w-full flex-col grow\">\n<div class=\"min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal [.text-message+&amp;]:mt-1\" dir=\"auto\" data-message-author-role=\"assistant\" data-message-id=\"e3f44bae-11a4-44ad-9d6e-7e8ab23b3f59\" data-message-model-slug=\"gpt-5-2\">\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[1px]\">\n<div class=\"markdown prose dark:prose-invert w-full wrap-break-word dark markdown-new-styling\">\n<p data-start=\"0\" data-end=\"81\" data-is-last-node=\"\" data-is-only-node=\"\">The training programme covered by the course focuses on the following key topics:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<\/div>\n<ul>\n<li>Hybrid HPC\u2013cloud architectures<\/li>\n<li>Execution strategies for GPU-intensive workloads<\/li>\n<li>The fundamentals of observability and model lifecycle management, enabling teams to understand not only how models run, but how well they perform over time.<\/li>\n<li>A guided design exercise to translate these concepts into use cases<\/li>\n<\/ul>\n<h5>Target<\/h5>\n<p data-start=\"2623\" data-end=\"2711\">This training is designed for a <strong>mixed technical and applied audience<\/strong>, including:<\/p>\n<ul data-start=\"2712\" data-end=\"3310\">\n<li data-start=\"2712\" data-end=\"2812\">\n<p data-start=\"2714\" data-end=\"2812\"><strong>Researchers<\/strong> and <strong>PhD students<\/strong> working on AI-driven scientific workflows on HPC infrastructures<\/p>\n<\/li>\n<li data-start=\"2813\" data-end=\"2924\">\n<p data-start=\"2815\" data-end=\"2924\">AI and data science <strong>practitioners<\/strong> who need to operationalize models beyond experimentation<\/p>\n<\/li>\n<li data-start=\"2925\" data-end=\"3044\">\n<p data-start=\"2927\" data-end=\"3044\"><strong>Technical staff<\/strong> and <strong>system engineers<\/strong> involved in supporting AI workloads on HPC or hybrid infrastructures<\/p>\n<\/li>\n<li data-start=\"3045\" data-end=\"3182\">\n<p data-start=\"3047\" data-end=\"3182\"><strong>Innovation managers<\/strong> and <strong>R&amp;D coordinators<\/strong> seeking to better understand architectural and operational implications of AI projects<\/p>\n<\/li>\n<li data-start=\"3183\" data-end=\"3310\">\n<p data-start=\"3185\" data-end=\"3310\"><strong>SMEs<\/strong> and applied <strong>research teams<\/strong> using EuroHPC or national HPC resources for AI and data-intensive workloads<\/p>\n<\/li>\n<\/ul>\n<h5>Prerequisites<\/h5>\n<div class=\"flex flex-col text-sm pb-25\">\n<article class=\"text-token-text-primary w-full focus:outline-none [--shadow-height:45px] has-data-writing-block:pointer-events-none has-data-writing-block:-mt-(--shadow-height) has-data-writing-block:pt-(--shadow-height) [&amp;:has([data-writing-block])&gt;*]:pointer-events-auto [content-visibility:auto] supports-[content-visibility:auto]:[contain-intrinsic-size:auto_100lvh] scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]\" dir=\"auto\" tabindex=\"-1\" data-turn-id=\"request-WEB:d4c3d8c2-7127-425f-ad8d-56247f81e86a-0\" data-testid=\"conversation-turn-2\" data-scroll-anchor=\"true\" data-turn=\"assistant\">\n<div class=\"text-base my-auto mx-auto pb-10 [--thread-content-margin:--spacing(4)] @w-sm\/main:[--thread-content-margin:--spacing(6)] @w-lg\/main:[--thread-content-margin:--spacing(16)] px-(--thread-content-margin)\">\n<div class=\"[--thread-content-max-width:40rem] @w-lg\/main:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group\/turn-messages focus-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn\" tabindex=\"-1\">\n<div class=\"flex max-w-full flex-col grow\">\n<div class=\"min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal [.text-message+&amp;]:mt-1\" dir=\"auto\" data-message-author-role=\"assistant\" data-message-id=\"994fc5e9-d45a-4ed9-87ad-d79e91c39d4c\" data-message-model-slug=\"gpt-5-2\">\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[1px]\">\n<div class=\"markdown prose dark:prose-invert w-full wrap-break-word dark markdown-new-styling\">\n<p>A basic familiarity with AI or data-driven workflows is recommended; deep expertise in HPC systems is not required.<\/p>\n<h5><span>Instructors:<\/span><\/h5>\n<div class=\"flex flex-col text-sm pb-25\">\n<article class=\"text-token-text-primary w-full focus:outline-none [--shadow-height:45px] has-data-writing-block:pointer-events-none has-data-writing-block:-mt-(--shadow-height) has-data-writing-block:pt-(--shadow-height) [&amp;:has([data-writing-block])&gt;*]:pointer-events-auto [content-visibility:auto] supports-[content-visibility:auto]:[contain-intrinsic-size:auto_100lvh] scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]\" dir=\"auto\" tabindex=\"-1\" data-turn-id=\"request-WEB:d4c3d8c2-7127-425f-ad8d-56247f81e86a-0\" data-testid=\"conversation-turn-2\" data-scroll-anchor=\"true\" data-turn=\"assistant\">\n<div class=\"text-base my-auto mx-auto pb-10 [--thread-content-margin:--spacing(4)] @w-sm\/main:[--thread-content-margin:--spacing(6)] @w-lg\/main:[--thread-content-margin:--spacing(16)] px-(--thread-content-margin)\">\n<div class=\"[--thread-content-max-width:40rem] @w-lg\/main:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group\/turn-messages focus-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn\" tabindex=\"-1\">\n<div class=\"flex max-w-full flex-col grow\">\n<div class=\"min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal [.text-message+&amp;]:mt-1\" dir=\"auto\" data-message-author-role=\"assistant\" data-message-id=\"994fc5e9-d45a-4ed9-87ad-d79e91c39d4c\" data-message-model-slug=\"gpt-5-2\">\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[1px]\">\n<div class=\"markdown prose dark:prose-invert w-full wrap-break-word dark markdown-new-styling\">\n<p data-start=\"3312\" data-end=\"3444\" data-is-last-node=\"\" data-is-only-node=\"\">The course will be delivered by <strong>Alessandro Chiarini,<\/strong> Senior Consultant &#8211; Life Science.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<h5 class=\"z-0 flex min-h-[46px] justify-start\"><span>Registration<\/span><\/h5>\n<p>Register now: complete the registration form at the <strong><a href=\"https:\/\/forms.office.com\/Pages\/ResponsePage.aspx?id=1wgZMMnyPkKZcThS0JwHiH2Jk_sJX85BpgaT1QnrFrhUOFZJV1BXNEVGVjNXUFRIWks2OElUUFdBUS4u\" target=\"_blank\" rel=\"noopener\">dedicated link<\/a><\/strong>.<\/p>\n<\/div>\n<\/div>\n<\/article>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>The emergence of new technologies\u2014particularly Artificial Intelligence\u2014within the business landscape brings significant opportunities and benefits for organizational ecosystems, while also introducing a set of important and non-trivial challenges. For example, for many SMEs, the challenge of adopting Artificial Intelligence is not access to GPUs or advanced infrastructures, but the risk of investing time and budget&#8230;<\/p>\n","protected":false},"author":3,"featured_media":10983,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_members_access_role":[],"_members_access_error":""},"categories":[14,15],"tags":[],"class_list":["post-10977","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","category-training"],"_links":{"self":[{"href":"https:\/\/euroccitaly.it\/en\/wp-json\/wp\/v2\/posts\/10977","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/euroccitaly.it\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/euroccitaly.it\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/euroccitaly.it\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/euroccitaly.it\/en\/wp-json\/wp\/v2\/comments?post=10977"}],"version-history":[{"count":4,"href":"https:\/\/euroccitaly.it\/en\/wp-json\/wp\/v2\/posts\/10977\/revisions"}],"predecessor-version":[{"id":10993,"href":"https:\/\/euroccitaly.it\/en\/wp-json\/wp\/v2\/posts\/10977\/revisions\/10993"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/euroccitaly.it\/en\/wp-json\/wp\/v2\/media\/10983"}],"wp:attachment":[{"href":"https:\/\/euroccitaly.it\/en\/wp-json\/wp\/v2\/media?parent=10977"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/euroccitaly.it\/en\/wp-json\/wp\/v2\/categories?post=10977"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/euroccitaly.it\/en\/wp-json\/wp\/v2\/tags?post=10977"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}