International Conference on Machine Learning - ICML
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About this event
International Conference on Machine Learning (ICML) — ICML 2026
ICML (International Conference on Machine Learning) is described as a premier global gathering focused on machine learning and closely related fields such as artificial intelligence, statistics, and data science. ICML 2026 is scheduled as “Forty-Third International Conference on Machine Learning” and will be held in Seoul, South Korea at the COEX Convention & Exhibition Center from July 6th–11th, 2026.
The official schedule framing provided on the website indicates: July 6: Expo/Tutorial Day, July 7–9: Main Conference, and July 10–11: Workshops.
1) Who attends (BUYERS / ATTENDEES)
ICML is not positioned as a narrow “sales-only” trade show. The conference is presented as a research and professional meeting where attendees span academia and industry. The official website states that participants include:
- Academic and industrial researchers
- Entrepreneurs and engineers
- Graduate students and postdocs
For buyer identification (companies with budget and internal decision power), the most relevant “buyer-like” attendees are typically those leading technology adoption, platform procurement, AI productization, applied research programs, and talent hiring—especially in organizations that build or deploy machine learning systems. Based on the conference’s research scope and listed application areas (machine vision, computational biology, speech recognition, robotics), buyer-relevant attendee groups include:
- Research leadership (heads of ML research, applied research directors, lab/program leaders)
- Engineering leadership (engineering directors/managers for ML platforms and infrastructure)
- Product and platform teams (product managers for AI/ML tooling, developer platforms)
- Industry technical leadership (technical program managers and solutions architects for ML deployments)
- Talent acquisition and recruiting (AI/ML hiring managers, recruiting leads tied to research & engineering)
- AI hardware & infrastructure stakeholders (compute, accelerators, networking for ML training/inference)
2) Where the show is happening + attendee geographic origin
Venue (official): COEX Convention & Exhibition Center
City/Country (official): Seoul, South Korea
Dates (official): July 6–11, 2026
Attendee geographic origin (from provided website content): the official page emphasizes “globally renowned” and “participants span a wide range of backgrounds,” but it does not provide a breakdown of attendee country/region origin. Therefore, we cannot reliably quantify attendee origin percentages from the provided source.
3) Audience reach (Local / National / Global)
The official website frames ICML as globally renowned and describes it as the “premier gathering” for professionals advancing machine learning. That positioning strongly indicates global reach rather than local or national-only reach.
4) Sample buyer company names (BUYERS ONLY) + Websites
ICML attracts buyer-fit organizations that support machine learning research, tooling, infrastructure, and talent pipelines. The most buyer-relevant company types include: AI platforms, ML infrastructure providers, cloud and compute providers, semiconductor/hardware ecosystem companies, developer tooling, data/platform vendors, and enterprise AI application companies.
Sample buyer list (15–20 accounts)
| Priority | Company | Website | Best Title to Target | Why This is a Good Buyer Fit |
|---|---|---|---|---|
| 1 | NVIDIA | nvidia.com | Director, AI/ML Platform Strategy / Technical Program Manager, AI Infrastructure | ML training/inference compute and AI platform ecosystem relevance to large-scale research workloads and robotics/vision/speech use cases. |
| 2 | Google Cloud | cloud.google.com | Head of AI Platform Partnerships / Director, Machine Learning Infrastructure | Strong fit for ML infrastructure, developer platforms, and scaling research-to-production for globally used ML stacks. |
| 3 | Amazon Web Services (AWS) | aws.amazon.com | Director, AI/ML Go-To-Market / Head of Applied AI Partnerships | High relevance for compute, managed training, and enterprise ML adoption across academia-to-industry workflows. |
| 4 | Microsoft Azure | azure.microsoft.com | Director, AI Platform Strategy / Principal PM, AI Infrastructure | Buyer-fit for ML platform procurement and platform enablement supporting research engineering and deployment at scale. |
| 5 | IBM | ibm.com | VP, AI Research & Partnerships / Director, Data & AI Platform | Fit for enterprise AI tooling and platform programs; aligns with broad ML and applied research interests. |
| 6 | Meta | meta.com | Director, Applied Machine Learning / Technical Program Manager, AI Research Infrastructure | Strong alignment with ML research and applied systems across vision, speech, and robotics-adjacent workloads. |
| 7 | Tencent Cloud | cloud.tencent.com | Head of AI Platform Partnerships / Director, Machine Learning Infrastructure | Buyer-fit for ML cloud platform initiatives and developer ecosystem programs tied to major ML research events. |
| 8 | Alibaba Cloud | alibabacloud.com | Director, AI Platform Product / Head of ML Partnerships | Relevant for ML infrastructure services and enterprise adoption; aligns with large-scale training/inference needs. |
| 9 | Intel | intel.com | Director, AI & Compute Strategy / Product Manager, AI Acceleration | Fit for accelerator/compute ecosystem influence and performance initiatives for ML workloads. |
| 10 | AMD | amd.com | Director, AI Solutions / Technical Marketing Lead, AI Compute | Good fit for AI compute/accelerator strategies and ML performance benchmarking in research-heavy environments. |
| 11 | Hugging Face | huggingface.co | Director, Developer Ecosystem / Head of ML Platform Partnerships | Strong alignment with open ML workflows, model/tooling adoption, and developer community engagement around ML. |
| 12 | Databricks | databricks.com | VP, AI & Data Platform / Director, Machine Learning Partnerships | Buyer-fit for end-to-end ML and data platform capabilities connecting research pipelines to analytics and production. |
| 13 | Snowflake | snowflake.com | Director, AI/ML Platform & Partnerships / VP, Data & AI GTM | Relevance for data-to-ML pathways and ML enablement through managed data platforms used by research and enterprise teams. |
| 14 | Palantir | palantir.com | Director, Applied AI Programs / VP, Data Integration & ML Deployment | Fit for applied ML adoption in real-world use cases, including complex data and domain-specific modeling. |
| 15 | MathWorks | mathworks.com | Director, AI/ML Product Strategy / Solutions Engineering Manager, ML | Strong for engineering-led ML tooling, model development support, and applied ML across engineering and research teams. |
| 16 | Siemens Digital Industries Software | siemens.com/sw | Director, AI for Industrial Solutions / Head of ML Enablement | Good fit for ML adoption in robotics and industrial workflows, mapping to robotics/automation-adjacent research topics. |
| 17 | Schneider Electric | se.com | Director, Data & AI / Head of AI Strategy for Operations | Alignment with applied analytics and ML for operational optimization; potential fit for AI deployment roadmaps. |
| 18 | DoorDash | doordash.com | Director, Applied ML / Head of ML Infrastructure & Platform | Buyer-fit for applied ML and scaling ML infrastructure for high-impact production systems. |
| 19 | Spotify | spotify.com | Head of ML Platform / Director, Recommendation & Applied ML | Relevant to ML applications and large-scale engineering teams working on model training, retrieval, and personalization. |
| 20 | Veritone | veritone.com | Director, Enterprise AI Partnerships / VP, Applied AI Engineering | Fit for speech/vision-adjacent ML applications and enterprise adoption of AI platforms. |
Best top 5 to send first (for outreach prioritization): NVIDIA, Google Cloud, AWS, Microsoft Azure, Hugging Face. This mix covers compute/infrastructure, platform adoption, and developer ecosystem engagement that typically align with machine learning conferences.
5) Job profiles, industries & event type
A) Best job profiles to target
- Research leadership: Director of Machine Learning, Head of ML Research, VP AI Research & Partnerships
- Applied ML & platform leadership: Director Applied Machine Learning, Director AI Platform Strategy, Head of Applied AI
- Infrastructure & scaling: Director Machine Learning Infrastructure, Technical Program Manager (ML Systems), Principal PM AI Infrastructure
- Developer ecosystem / partnerships: Director Developer Ecosystem, Head of ML Partnerships, Partnerships Manager AI/ML
- Product & GTM: VP AI/ML Go-To-Market, AI Product Manager, Solutions Engineering Manager (ML)
- Talent & hiring: Head of ML Hiring, Recruiting Manager (AI/ML), Talent Acquisition Lead (Research Engineering)
B) Industries & event type mapping
Event type: International research and professional conference in machine learning, with a structured program across tutorials, main conference, workshops, and an expo/tutorial day.
Industries (using the provided industry list as closely as possible):
- Computer Software
- Computer Hardware
- Semiconductors
- Information Technology & Services
- Internet
- Internet (for platform and cloud-native AI services)
- Information Services
- Market Research (for analytics-focused ML commercialization teams, where applicable)
- Research (universities, labs, research institutes; often not a “buyer” in procurement terms, but still a decision-influence community)
- Professional Training & Coaching (indirect, for ML upskilling programs tied to tutorials/workshops)
6) Estimated attendance (expected total footfall)
The provided official page content does not state an attendance or footfall number for ICML 2026. Therefore, we cannot reliably fill this field from the supplied source.
7) Key focus areas & buyer engagement
The official website lists ICML’s scope as covering machine learning and closely related areas, and it calls out application domains such as: machine vision, computational biology, speech recognition, and robotics.
A) Key focus areas likely to matter to buyer organizations
- Core ML algorithms and systems (methods, training strategies, evaluation)
- Applied ML in specialized domains (vision, speech, biology, robotics)
- Scalable ML infrastructure (compute acceleration, distributed training, deployment constraints)
- Data-centric workflows (from datasets to pipelines to model readiness)
- Research-to-product transition (how breakthroughs become engineering roadmaps)
- Talent and community building (recruiting and ecosystem partnerships)
B) Buyer engagement angles we recommend
- Positioning around ML research enablement: we align messaging to research workflows, not only “marketing attendance.”
- Infrastructure relevance: we focus on how your solution reduces training/inference friction, improves reproducibility, and accelerates experimentation.
- Domain-specific credibility: we tailor outreach to vision/speech/robotics/cross-domain teams depending on buyer type.
- Partnership mechanics: we emphasize co-development, workshops/tutorial collaborations, and technical collaboration pathways.
8) Client-product fit note (must match your product; pending website review)
We will identify the best buyer-fit accounts based on your product requirements, but we need your information first. Please share your client product website (URL) and we will review it and return a refined buyer shortlist aligned to your exact use case (e.g., compute, tooling, data platform, model deployment, education/training, compliance, staffing, etc.).
What we need from your side:
- Your client website URL
- Target customer type (enterprise / developers / research labs / universities / telecom / healthcare, etc.)
- Primary product category (software platform, services, hardware, training, etc.)
- Geography focus (if any)
Provisional best-fit direction (until we review your website): For ICML, buyer-fit typically clusters into the categories below. Once we see your client website, we will choose the most relevant subset and prioritize titles accordingly.
- ML infrastructure & accelerators: compute/accelerator strategy and ML infrastructure leaders
- ML platforms & developer tooling: platform strategy, partnerships, and ML engineering productivity teams
- Data-to-ML pipelines: AI platform and data platform leadership
- Applied AI deployments: applied ML directors and production ML engineering leaders
- AI talent & hiring ecosystems: recruiting leaders aligned to research engineering growth
9) Final recommendation (what we should do next)
ICML 2026 is positioned as a top global ML research and professional conference, with a clear time and venue structure (July 6–11, 2026 at COEX Convention & Exhibition Center, Seoul). The attendee mix spans academia, industry researchers, entrepreneurs, engineers, and advanced students—so buyer-value depends on targeting the right “decision-influence” titles.
Immediate next step: Share your client website, and we will refine: (1) the best buyer segments, (2) the most relevant job titles, (3) and an improved prioritized company list (over the sample table) tailored to your product fit for ICML 2026.
Quick reference summary (ICML 2026)
- Event: International Conference on Machine Learning (ICML 2026)
- Venue: COEX Convention & Exhibition Center
- City/Country: Seoul, South Korea
- Dates: July 6–11, 2026 (July 6 Expo/Tutorial Day; July 7–9 Main; July 10–11 Workshops)
- Audience reach: Global (conference described as globally renowned)
- Attendance number: Not specified on provided official page content
Data sheet
International Conference on Machine Learning - ICML – Event Attendee & Buyer Profile Analysis
Event Date: July 6–11, 2026
Location: COEX, Seoul, South Korea
Event Status: Upcoming
Research Date: July 1, 2026
Event Overview
| Event Name | International Conference on Machine Learning (ICML) 2026 |
| Event Date | July 6–11, 2026 |
| Event Status | Upcoming |
| Venue | COEX Convention & Exhibition Center |
| City | Seoul |
| State / Region | Seoul Capital Area |
| Country | South Korea |
| Organizer | International Machine Learning Society (ICML) |
| Official Event Website | icml.cc |
| Event Type | Academic and industry conference; research, networking, recruiting, sponsorship, and expo presentations |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research, Education & Training, Business Services |
| Audience Reach | Global |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | High for dates, venue, and location; low for attendance volume because no public count is confirmed on the official page content provided. |
| Main Purpose of Event | To present and discuss cutting-edge machine learning research, foster collaboration across academia and industry, and support technical networking, recruiting, sponsorship, and knowledge exchange. |
About the Event
The International Conference on Machine Learning (ICML) is one of the world’s most prominent gatherings for machine learning research and applied artificial intelligence. The 2026 edition is confirmed for July 6–11, 2026 at COEX in Seoul, South Korea, with programming that includes tutorials, the main conference, workshops, expo presentations, and related conference activities.
ICML matters because it sits at the intersection of foundational research and commercial adoption. It attracts academic researchers, industrial scientists, AI engineers, startup teams, product leaders, and technical executives who influence technology purchasing, research partnerships, hiring, and innovation roadmaps. For B2B outreach, the event is especially relevant for AI infrastructure, machine learning platforms, data tools, compute, cloud, developer tools, and research services.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| AI / ML Research Teams | Technology companies, research labs, universities, applied science groups | Evaluates research tools, platforms, compute, and data infrastructure | High-value audience for cloud, AI tooling, MLOps, data, and model-serving vendors |
| CIO / CTO / VP Engineering | Enterprise tech firms, digital platforms, scale-ups, public sector innovation teams | Approves strategic technology and platform investments | Important for enterprise AI, infrastructure, security, analytics, and cloud suppliers |
| Data Science and Analytics Leaders | Banks, insurers, retailers, manufacturers, internet firms, logistics companies | Influences tool selection, pilot programs, and AI adoption | Strong fit for analytics, data governance, visualization, and experimentation platforms |
| Procurement / Strategic Sourcing | Large enterprises, government labs, universities, research institutes | Manages vendor selection, contracting, and renewals | Relevant for software, cloud, consulting, events, and professional services suppliers |
| Product Management | AI software vendors, SaaS firms, platform companies, device makers | Defines roadmap, feature priorities, and user requirements | Useful for developer tools, API platforms, and model lifecycle products |
| Research Procurement / Lab Managers | Universities, labs, institutes, advanced research centers | Buys research software, compute, data subscriptions, and lab services | Strong for academic software, compute credits, and research data providers |
| Startup Founders / CEOs | AI startups, software startups, venture-backed technology firms | Direct buying authority for tools, services, and partnerships | Valuable for SaaS, cloud credits, recruiting, and GTM services |
| Investors and Corporate Venture Teams | VC funds, corporate venture capital, strategic investors | Looks for technical differentiation and partnership opportunities | Relevant for startup ecosystems, AI infrastructure, and strategic partnerships |
| Public Sector / National AI Programs | Government digital agencies, research ministries, public labs | Shapes funding priorities and procurement for AI initiatives | Relevant for policy, AI governance, cloud, and training vendors |
| Media and Industry Analysts | Tech press, research analysts, AI publications | Influences market visibility and category awareness | Useful for thought leadership, PR, and category-building campaigns |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Host city: Seoul | Local universities, technology firms, startups, and research institutes | High | Strong concentration of AI, semiconductor, electronics, and platform companies |
| Host region: Seoul Capital Area | Corporates, government agencies, universities, and R&D organizations | High | Major enterprise and public-sector demand center for AI and digital transformation |
| Nearby business hubs in South Korea | Busan, Daejeon, Incheon, Pangyo, Suwon | Medium to High | Relevant for tech companies, labs, and industrial AI adopters |
| National reach: South Korea | National universities, conglomerates, ministries, and AI startups | High | ICML in Seoul is likely to attract a strong domestic attendance base |
| International reach | Global academic and enterprise AI community | Very High | ICML is a globally recognized conference with cross-border research and commercial attendance |
| Key APAC origin markets | Japan, China, Singapore, Taiwan, India, Australia | High | Strong regional draw for AI research, cloud, chips, and enterprise software stakeholders |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | ICML is internationally recognized and draws a worldwide mix of academic, research, startup, and enterprise attendees. |
| Secondary reach | Asia-Pacific-heavy in location-driven participation | Seoul location is likely to increase participation from South Korea and nearby APAC markets while maintaining strong global visibility. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Enterprise AI buyer | Large-scale ML, cloud, data, and research tooling needs | google.com | VP Engineering, Director of AI, ML Platform Lead | Strong Market Fit, Attendance Not Confirmed | |
| Microsoft | Enterprise AI buyer | Cloud AI, developer tools, and applied research investments | microsoft.com | CIO, CTO, AI Product Director | Strong Market Fit, Attendance Not Confirmed |
| Amazon Web Services | Cloud / infrastructure buyer | Compute, storage, MLOps, and model deployment demand | aws.amazon.com | Head of AI Infrastructure, Cloud Partnerships, Solutions Architect Leader | Strong Market Fit, Attendance Not Confirmed |
| Meta | AI research buyer | Research models, infra, and experimentation platforms | meta.com | Research Scientist Lead, ML Engineering Manager | Strong Market Fit, Attendance Not Confirmed |
| NVIDIA | Compute / AI platform buyer | GPU acceleration, AI software stack, and research collaboration | nvidia.com | Director AI Platform, Product Marketing, Technical Partnerships | Strong Market Fit, Attendance Not Confirmed |
| OpenAI | AI lab buyer | Frontier model, data, infrastructure, and talent ecosystem relevance | openai.com | Research Operations, Infrastructure Lead, Partnerships | Strong Market Fit, Attendance Not Confirmed |
| Anthropic | AI lab buyer | Model research, safety, compute, and vendor ecosystem | anthropic.com | Research Lead, Infrastructure, Technical Operations | Strong Market Fit, Attendance Not Confirmed |
| Samsung Electronics | Enterprise innovation buyer | Device AI, chip-level ML, and R&D procurement | samsung.com | VP R&D, AI Strategy Director, Procurement Lead | Strong Market Fit, Attendance Not Confirmed |
| SK Telecom | Telecom AI buyer | Network intelligence, AI services, and platform partnerships | sktelecom.com | Chief Data Officer, AI Transformation Lead | Strong Market Fit, Attendance Not Confirmed |
| Kakao | Platform buyer | Consumer platform AI, data, and product experimentation | kakaocorp.com | CTO, Head of AI Product, Data Science Director | Strong Market Fit, Attendance Not Confirmed |
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Chief AI Officer / CTO / VP Engineering | Technology | C-level / VP | Owns strategic technology adoption, platform selection, and innovation priorities. |
| 2 | Director of AI / ML Platform Lead | AI / Data / Engineering | Director / Head | Evaluates MLOps, model deployment, governance, and research-to-production tooling. |
| 3 | Data Science Director / Head of Analytics | Data / Analytics | Director / Head | Consumes ML tools, analytics stacks, experimentation platforms, and data services. |
| 4 | Procurement Manager / Strategic Sourcing Manager | Procurement | Manager / Senior Manager | Manages software, cloud, research services, and vendor contracting. |
| 5 | Research Scientist / Applied Scientist | R&D | Senior IC / Lead | Evaluates research datasets, compute, simulation, and publication tools. |
| 6 | Product Manager, AI Platform | Product | Manager / Director | Translates technical requirements into commercial product decisions. |
| 7 | Partnerships Director / Business Development Director | Partnerships / BD | Director / VP | Looks for co-development, channel, research, and ecosystem deals. |
| 8 | CISO / Security Architect | Security | Director / VP | Important where AI platforms, data access, and governance are buying criteria. |
| 9 | Program Manager / Research Operations | Operations / Research Ops | Manager / Senior Manager | Coordinates events, research workflows, and operational vendor needs. |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Information Technology & Services | Core industry for cloud, software, data, AI, and enterprise tech buyers | AI platforms, cloud services, MLOps, analytics, security, developer tools |
| 2 | Computer Software | Many attendees and sponsors build or buy software products | AI APIs, enterprise software, data platforms, workflow tools |
| 3 | Internet | Platform and product companies are major ICML participants | Product analytics, AI inference, personalization, and experimentation |
| 4 | Semiconductors | Compute, chips, and acceleration are essential to ML workloads | GPU/AI hardware, edge AI, optimization, and systems tooling |
| 5 | Aviation & Aerospace | Advanced R&D organizations often attend technical conferences for AI use cases | Computer vision, predictive maintenance, simulation, autonomy |
| 6 | Electrical/Electronic Manufacturing | Device makers increasingly adopt AI in products and operations | Embedded AI, edge inference, quality control, vision systems |
| 7 | Financial Services | Banks and fintech firms buy ML and analytics solutions | Risk models, fraud detection, automation, data governance |
| 8 | Pharmaceuticals | ML is central to drug discovery and research workflows | Bioinformatics, research compute, data science platforms |
| 9 | Telecommunications | Telecom operators use AI for network intelligence and customer operations | AI operations, forecasting, network automation, customer analytics |
| 10 | Government Administration | Public-sector AI, digital, and research organizations are relevant | AI governance, procurement, public research, and digital transformation |
| 11 | Education Management | Universities and schools contribute research attendees and buyers | Academic software, research tools, training, and conference services |
| 12 | Research | Directly aligned with ICML’s core research audience | Research infrastructure, publication tools, datasets, collaboration software |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Not publicly confirmed | Unconfirmed | Official website content provided | No organizer-published attendance number found in the provided source text. |
| Exhibitor count | Not publicly confirmed | Unconfirmed | Official website content provided | The site references exhibitors, but no count is shown in the provided content. |
| Buyer count | Not publicly confirmed | Unconfirmed | Official website content provided | ICML is not a buyer-show format with a public buyer total. |
| Speaker count | Not publicly confirmed | Unconfirmed | Official website content provided | Invited talks and program tracks are referenced, but no total count is shown. |
| Sponsor count | Not publicly confirmed | Unconfirmed | Official website content provided | The site notes sponsors and applications closed, but no count is shown in the provided content. |
| Historical attendance | Not included here | Not verified in provided source text | No verified prior-year figure supplied | Use organizer archives if available for year-over-year benchmarking. |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Machine Learning Research | Access to new methods, papers, and collaboration | Sponsor sessions, demos, and academic partnerships | Research tools, publishing services, academic software |
| AI Infrastructure | Scalable compute and deployment capacity | Target technical leaders with performance and cost messaging | Cloud, GPUs, storage, orchestration, MLOps |
| Data Management | Reliable pipelines, governance, and quality control | Offer discovery calls around data readiness and governance | Data platforms, cataloging, governance, quality tools |
| AI Productization | Move models into production | Engage product and engineering stakeholders | APIs, deployment tooling, monitoring, feature stores |
| Talent and Recruiting | Hire researchers and engineers | Use career-site and networking touchpoints | Recruiting services, employer branding, ATS integrations |
| AI Governance / Safety | Responsible AI frameworks and controls | Speak to risk, compliance, and trust concerns | Governance software, audit, policy, security consulting |
| Research Collaboration | Partnerships between academia and industry | Build co-development and pilot programs | Sponsored research, grants, lab partnerships, workshops |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | Very High | ICML attracts technical and procurement-influencing audiences that buy AI, data, cloud, research, and engineering solutions. |
| Decision-maker availability | High | Senior technical leaders, researchers, and partnership heads are common, though not always procurement-final decision makers. |
| Data collection potential | High | Strong prospecting value for attendee list sales, sponsorship outreach, and lead enrichment workflows. |
| Apollo targeting potential | Very High | Clear keyword and title segmentation exists across AI, software, cloud, research, product, and procurement roles. |
| Geographic targeting potential | High | Seoul location enables APAC targeting while still supporting global outreach. |
| Best outreach approach | Technical thought leadership | Lead with use cases, benchmarks, integration value, and conference-specific relevance rather than generic sales copy. |
| Overall lead quality | Very High | Excellent for B2B attendee list building, AI ecosystem prospecting, and technical enterprise outreach. |
| Best use case | Prospecting and partnerships | Ideal for lead generation, account-based marketing, AI vendor outreach, and conference sponsorship sales. |
| Limitations / risks | Attendance uncertainty | No organizer-published attendance figure was confirmed in the provided official content; attendee/company lists may be incomplete before the event. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Information Technology & Services; Computer Software; Internet; Semiconductors; Research; Financial Services; Telecommunications; Government Administration; Education Management; Pharmaceuticals | Capture the most likely buyer and influencer segments around AI, data, compute, and research. |
| Departments | Engineering; Data; Product; Procurement; Research; IT; Partnerships; Operations; Security | Focus outreach on technical and commercial stakeholders with buying influence. |
| Seniority | Director; VP; Head; C-level; Manager; Senior Manager; Individual Contributor for research roles | Prioritize decision-makers and technical evaluators who can act on conference outreach. |
| Job titles | CTO, CIO, VP Engineering, Head of AI, Director of ML, Data Science Director, Procurement Manager, Strategic Sourcing Manager, Product Manager, Research Scientist, Partnerships Director | Align title filters with conference roles and buying influence. |
| Geography | South Korea; Seoul Capital Area; APAC; United States; Canada; United Kingdom; Japan; Singapore; China; India | Cover host-market and global attendance footprint. |
| Employee size | 51–200; 201–500; 501–1,000; 1,001–5,000; 5,001–10,000; 10,000+ | Match startup, scale-up, enterprise, and public-sector buying profiles. |
| Keywords | machine learning, AI, deep learning, MLOps, data science, model deployment, LLM, foundation model, research, GPU, cloud, inference, governance | Enable intent-style searches aligned with conference themes. |
| Company type | Enterprise; startup; university; research institute; government agency; public lab; vendor; system integrator | Broaden coverage across attendee and sponsor ecosystems. |
| Revenue range | Select by client offering; generally mid-market to enterprise for infrastructure and software vendors | Focus on organizations with budget and technical buying power. |
Suggested Apollo Search Logic: (machine learning OR AI OR “data science” OR MLOps OR “deep learning” OR “model deployment”) AND (CTO OR “VP Engineering” OR “Head of AI” OR “Data Science Director” OR Procurement OR “Strategic Sourcing” OR “Product Manager”) AND geography filters for South Korea, APAC, and global enterprise tech markets.
Client Fit Review Required
Please share the client website or product/service details. I will review the client offering and identify the highest-fit buyer companies, Apollo industries, seniority levels, departments, and job titles from this event.
Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| ICML Official Website | Official organizer source | Confirmed ICML 2026 dates, Seoul location, COEX venue, conference programming, and organizer framing | Very High |
| ICML 2026 official pages referenced in site content | Organizer portal / event pages | Confirmed that tutorials, main conference, and workshops are part of the 2026 program; attendance figure not publicly confirmed | Very High |
| COEX Convention & Exhibition Center | Venue reference | Venue identification for the conference location in Seoul | High |
| ICML official announcements on the 2026 page | Organizer announcement | Verified 2026 dates and venue statement, plus program structure and registration status | Very High |
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