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About this event
ODSC AI West — Deep Research Buyer & Attendee Intelligence (Global Tech Conference)
Quick Event Snapshot
- Event Title: ODSC AI West
- Format: AI/ML technical conference with industry talks, hands-on learning, workshops, and an exhibitor/sponsor presence
- Typical Location: West Coast / major U.S. city venue (varies by year; commonly San Francisco Bay Area or nearby major markets)
- Expected Audience Type: Data science, ML engineering, AI research practitioners, platform/ML infrastructure teams, applied AI builders, and enterprise technology decision-makers who sponsor or evaluate AI solutions
1) Who attends (BUYERS / ATTENDEES)
ODSC AI West is primarily a technical and practitioner-heavy event, which means the “attendee” universe includes both (a) hands-on builders who actively evaluate tools and platforms, and (b) buyer-influence stakeholders who guide procurement and vendor selection for AI initiatives. For attendee-list quality and buyer fit, the event typically concentrates the following groups:
A) Primary attendee groups (often the largest visible segments)
- Machine Learning Engineers building production or near-production ML systems
- Data Scientists applying statistical learning, NLP, forecasting, personalization, and computer vision
- AI/ML Researchers focused on model development, evaluation, and experimental R&D
- Applied AI Engineers integrating AI capabilities into products and workflows
- DevOps / ML Ops practitioners focused on deployment, observability, pipelines, and model governance
- Software Engineers and platform engineers supporting AI infrastructure and data platforms
B) Buyer / procurement influence groups (where the business value sits)
- VP/Director of Data Science and Head of AI stakeholders who sponsor platform standards
- Engineering Leadership (Director/Manager, ML Platform, Applied AI Engineering, Research Engineering)
- Technical Program Managers and AI Transformation Leaders coordinating AI roadmaps
- Cloud, Data, and Security leaders (responsible for cost, compliance, and safe deployment)
- Product and Solutions architects selecting vendor toolchains for AI productization
- Vendor teams from ML platforms, inference optimization, data tooling, and AI application frameworks (often the most “buyer-legible” contacts)
C) Best buyer fit (practical summary)
The best buyer-fit attendees for ODSC AI West are typically those with responsibilities for: model development-to-deployment lifecycle, AI platform evaluation, ML infrastructure, enterprise AI enablement, and AI product implementation. Even when the role is technical, many of these people influence purchase decisions because AI tooling is frequently bought through engineering evaluation cycles and platform standards.
2) Where the show is happening + attendee geographic origin
ODSC AI West is designed for the Western U.S. market while drawing attendees from other parts of the country and, depending on year, internationally. We typically see an audience mix that includes: local/regional Bay Area and West Coast professionals plus national attendees traveling for the most relevant speakers, workshops, and vendor demos.
A) Venue footprint (how we recommend targeting geography)
- Primary geography: West Coast (California, Oregon, Washington, Nevada, Arizona)
- Secondary geography: Southwest and Mountain region (Texas often appears as a secondary travel origin in tech conferences, depending on year)
- National traveling: Northeast and Midwest frequently send ML/AI teams for major ODSC tracks and sponsor ecosystems
B) Best geographic origin targeting for attendee lists
- Regional deep pull: West Coast cities (San Francisco Bay Area, Los Angeles, San Diego, Seattle, Portland)
- National coverage: large innovation hubs (Austin, Chicago, Boston, New York area, Washington DC corridor)
- International touchpoints: smaller but meaningful share from EMEA/India/Canada, mostly in highly specialized AI communities
If our client product targets enterprise AI platform decisions, we recommend building a list that prioritizes West Coast + major national tech hubs, then expanding with roles that match the buyer persona (ML platform, AI engineering leadership, data platform owners).
3) Audience reach (Local / National / Global)
ODSC AI West should be treated as a Nationally significant U.S. tech conference with strong local concentration. It is rarely “pure local only” because AI teams often travel to attend specific tracks that match their current model development and productionization needs. For global reach, the presence is typically selective but real (international researchers and vendor teams), rather than a conference that is predominantly attended by non-U.S. participants.
- Local reach: High (West Coast professionals attending in person)
- National reach: High (U.S. AI practitioners traveling to workshops and expo demos)
- Global reach: Moderate (international participants, often via vendor ecosystems and advanced technical communities)
4) Sample buyer company names (BUYERS ONLY) + Websites
Below is a curated buyer-style list of companies that commonly align with ODSC-type audiences: applied AI builders, ML infrastructure providers, data tooling vendors, model optimization/inference platforms, MLOps/observability vendors, and cloud/AI ecosystem participants. These are examples we would typically use as “buyer hypotheses” when creating a high-fit outreach list.
Important: For the most accurate buyer list, we recommend aligning the titles and industries with the client’s exact offering (we cover this in Point 8). ODSC AI West attendee lists are best when we filter by AI platform evaluation roles rather than only job seekers or student profiles.
5) Job profiles, industries & event type
A) Best job profiles to target
- Head of AI, Director of AI, VP AI Transformation
- Director/VP, Machine Learning, Director/VP, Data Science
- ML Engineering Manager, Applied AI Engineering Manager
- ML Platform Manager, AI Platform Engineering Lead
- Solutions Architect (AI/Data), Technical Lead (AI)
- MLOps Lead, ML Ops Manager, Model Operations Manager
- Data Platform Manager / Data Engineering Director (AI enablement)
- Cloud Architecture Lead / Infrastructure Lead (AI workload)
- AI Product Manager (if the client sells applied AI products)
- Research Engineering Manager (if the client sells R&D tooling)
- Security / Compliance technologists for safe AI deployment (if relevant to the client)
B) Event type (what it “really” is)
ODSC AI West is best classified as a technical AI/ML conference with: industry track sessions, practical implementation content, and technology vendor engagement. It is not purely a sales expo; it is an education + evaluation environment where technical teams explore solutions. That means buyer fit comes from both role matching and topic matching (MLOps, inference, NLP, LLMOps, AI infrastructure, evaluation/monitoring, etc.).
C) Industry mapping (using our common industry filter approach)
Based on standard industry categorization aligned to ODSC’s audience, the most relevant industry groupings typically include: Computer Software, Information Technology & Services, Computer & Network Security (for responsible AI), Internet, Semiconductors (AI acceleration), Market Research (applied analytics), Professional Training & Coaching (AI enablement and upskilling), Medical Devices / Hospital & Health Care (when tracks include health AI), and Financial Services / Capital Markets (when tracks include trading analytics, risk modeling, and fraud).
6) Estimated attendance (expected total footfall)
ODSC events vary by year, but AI West editions typically attract a mid-to-large professional audience for a technical conference. Because attendance can shift based on the city, year, and workshop volume, we recommend treating attendance as an estimated range for research and planning purposes.
- Expected total footfall: commonly in the several thousand to tens of thousands bracket depending on year configuration
- High-density hours: keynote and expo transitions; workshop overflow periods
- Buyer density considerations: not every attendee is a buyer, but buyer-influence roles are present among leadership, platform owners, and evaluation stakeholders
For best results in attendee-list research, we recommend segmenting by job seniority and function (ML platform, data science leadership, MLOps/infra, and AI solutions ownership). That improves “buyer density” even if total footfall is large.
7) Key focus areas & buyer engagement
ODSC AI West typically emphasizes practical AI adoption topics. Buyers and buyer-influencers engage deeply when the content aligns with what they are implementing next. The most common focus areas include:
A) Core technical themes that drive engagement
- LLMs & Generative AI engineering (use cases, evaluation, safety, and deployment)
- MLOps and LLMOps (pipelines, monitoring, governance, continuous evaluation)
- AI infrastructure and performance (inference optimization, hardware acceleration)
- Data-centric AI (data labeling, quality, retrieval, and knowledge augmentation)
- Model evaluation and observability (quality metrics, drift detection, experiment tracking)
- Responsible AI (privacy, compliance, secure deployment)
B) Buyer engagement behaviors we should expect
- Leadership and technical managers attend sessions to validate vendor claims and roadmap fit
- Engineers attend workshops to understand implementation requirements and integration patterns
- Teams often request demos around the conference’s most relevant engineering bottlenecks (deployment, cost, monitoring, integration)
- Sponsors and exhibitors tend to generate high interest during breaks and hands-on sessions
Outreach angle that typically works best: “We help AI teams accelerate from evaluation to production by improving deployment reliability, cost control, governance, and measurable model performance.” This framing resonates with both technical evaluators and leadership decision-makers.
8) Client-product fit: seller-led recommendation + buyer shortlist logic (We need your website)
To produce a best-fit buyer list for ODSC AI West, we need the client’s official website URL (and ideally a 3–5 line description of what the client sells). The correct buyer titles and industries change significantly depending on whether the client offers: AI software platform, infrastructure, data tooling, MLOps, consulting/training, security for AI, or vertical AI solutions.
What we will do once we have the website
- Map the client’s core value proposition to the ODSC AI West theme areas (LLM engineering, MLOps/LLMOps, inference/performance, governance, evaluation)
- Select the best functional buyer segments (AI platform owners, MLOps leads, data platform directors, research engineering leaders, solutions architects)
- Choose the best industries consistent with your buyers (commonly Computer Software, IT Services, Semiconductors, Internet, Security, Healthcare/Fintech where relevant)
- Generate a prioritized attendee/buyer shortlist based on likely buying authority and evaluation influence
- Provide a “top send first” subset (highest relevance) plus supporting accounts
Interim guidance (until we review the client website)
If the client product is any of the following, then these buyer profiles are typically the highest probability fit at ODSC AI West:
- AI/ML software platform, MLOps tooling, LLMOps tooling: Target ML Platform Manager, MLOps Lead, Head of Data Science, Director AI Engineering, Solutions Architect (AI/Data).
- AI infrastructure/performance optimization: Target Infrastructure Lead (AI workloads), Cloud Architecture Lead, ML Systems Engineering Manager, and Tech Ops leaders.
- AI security / responsible AI / compliance tooling: Target Security Engineering Manager, Privacy/Compliance technologists, Responsible AI leads, and Platform governance owners.
- AI training / upskilling / enterprise enablement: Target Director of AI Enablement, Learning & Development for Engineering, Professional Training Program leads, and Head of Data Science education.
- Vertical AI solutions (healthcare, finance, retail): Target vertical analytics leaders plus data science leadership in those verticals.
Please share the client website so we can confirm the best-fit buyer segment and produce a ranked shortlist tailored to the product.
9) Final recommendation (How we should approach ODSC AI West for best buyer results)
ODSC AI West is a strong event for AI tooling, AI infrastructure, applied AI programs, and technical enablement offerings because: (1) the attendee universe includes both practitioners and buyer-influencers, (2) technical interest converts into evaluation conversations, and (3) sponsor/exhibitor engagement usually clusters around actionable implementation themes.
We recommend positioning outreach around these outcomes
- Faster path from model evaluation to production deployment
- Reduced cost and improved performance for inference and pipelines
- Better monitoring, evaluation, and governance for reliable AI outcomes
- Improved developer productivity with integration-ready tooling
Quality targets (what a “good buyer fit” looks like)
- Senior role OR technical lead role with ownership of AI platform decisions
- Company size where budgets exist for tooling (mid-market to enterprise preferred)
- Function alignment: AI platform, MLOps/LLMOps, data science leadership, solutions architecture
- Topic alignment: LLM engineering, evaluation, deployment, observability, performance
Sample Buyer List Table (15–20 examples with outreach-ready fields)
Below are sample buyer-style accounts we would typically use when assembling a prioritized buyer outreach list for ODSC AI West. These are not guaranteed exhibitors for every year, but they are representative of companies that frequently match the AI/ML evaluation ecosystem. We prioritize job titles that have a high likelihood of evaluating AI tooling and influencing buying decisions.
| Priority | Company | Website | Best Title to Target | Why This is a Good Buyer Fit |
|---|---|---|---|---|
| 1 | Google Cloud | https://cloud.google.com | Director, Machine Learning / Head of AI Platform | Strong alignment with enterprise AI platform adoption, model deployment patterns, and AI infrastructure strategy. |
| 2 | Amazon Web Services (AWS) | https://aws.amazon.com | Senior Product Manager, AI/ML Services / ML Platform Director | Typically supports multiple AI workload deployment stacks; buyer-fit roles evaluate tooling, orchestration, and governance. |
| 3 | Microsoft | https://www.microsoft.com | Director, Data & AI / Head of Azure AI Engineering | Enterprise AI adoption and platform enablement connect directly to MLOps/LLMOps and governance initiatives. |
| 4 | Databricks | https://www.databricks.com | Head of Applied AI / Director, Machine Learning Platform | Data-to-ML pipeline leadership roles frequently evaluate end-to-end AI workflow tooling. |
| 5 | Snowflake | https://www.snowflake.com | VP/Director, AI & Data Platform / Solutions Architect, AI | Buyer-fit for data platform + AI enablement; evaluation teams often attend technical conferences for integration clarity. |
| 6 | NVIDIA | https://www.nvidia.com | Director, AI Infrastructure / ML Systems Engineering Lead | High alignment with acceleration, inference performance, and AI compute ecosystems. |
| 7 | AMD | https://www.amd.com | Senior Director, AI/ML Solutions / Infrastructure Product Lead | Strong interest in AI workloads, performance benchmarking, and enterprise adoption discussions. |
| 8 | OpenAI (enterprise ecosystem presence) | https://openai.com | Enterprise Solutions Architect / Director, AI Platform Partnerships | Buyer-fit for enterprise deployment considerations and evaluation of LLM application workflows. |
| 9 | Anthropic | https://www.anthropic.com | Enterprise Solutions Lead, AI / Director, Customer Solutions (AI) | Strong relevance for applied generative AI evaluation and enterprise integration requirements. |
| 10 | Hugging Face | https://huggingface.co | Director, Enterprise ML / Solutions Architect, LLM Ops | Aligns with model ecosystems, deployment pipelines, and practical AI tooling needs. |
| 11 | Datadog | https://www.datadoghq.com | Director, ML Observability / Head of AI Monitoring | Monitoring and evaluation observability are core themes; strong fit for AI production reliability needs. |
| 12 | Weights & Biases | https://wandb.ai | Director, MLOps / Head of Developer Experience (AI) | Highly aligned with experiment tracking, evaluation, and end-to-end AI development workflows. |
| 13 | PagerDuty | https://www.pagerduty.com | Director, Incident Response Platforms / Reliability Engineering Lead | AI systems still require operational reliability; buyer-fit for production monitoring and incident workflows. |
| 14 | Splunk | https://www.splunk.com | Director, Data Engineering & AI / Solutions Architect, ML Analytics | Aligns with operational analytics and AI governance by turning telemetry into measurable outcomes. |
| 15 | Palantir | https://www.palantir.com | Director, AI Engineering / VP, Data & AI Systems | Strong match for applied AI programs where deployment and operational integration matter. |
| 16 | UiPath (Automation + AI angle) | https://www.uipath.com | Director, AI Automation / Head of Intelligent Process Automation | Buyer-fit for applied AI in enterprise workflows; ODSC audiences overlap with applied automation builders. |
| 17 | ServiceNow | https://www.servicenow.com | Director, AI for Enterprise / Head of AI Platform Engineering | Strong alignment with enterprise AI adoption and workflow integration where AI must be measurable and governed. |
| 18 | Snowplow (example for event analytics ecosystem) | https://snowplow.io | Head of Data Partnerships / Director, Data Engineering (AI-ready) | If the client product targets data instrumentation or event-driven analytics used for ML, this type of buyer fits well. |
| 19 | Datadog (second angle: incident + AI ops) | https://www.datadoghq.com | Head of Platform Observability / ML Platform Lead | AI Ops is increasingly important; roles here are often directly connected to buying monitoring and governance tooling. |
| 20 | Stripe (risk/ML analytics) | https://stripe.com | Director, ML Engineering / Head of Risk ML Systems | Fintech ML teams often attend AI technical events to optimize model performance and operational safety at scale. |
Top 5 “best to send first” (for a generic AI tooling buyer fit)
- Databricks
- AWS
- Google Cloud
- NVIDIA
- Weights & Biases
We choose these as early targets because they frequently intersect with AI platform evaluation, ML engineering workflows, and MLOps/observability themes that match ODSC AI West content. After we review your client website, we will refine this to a precise top list that matches your product category.
What we need from you to finalize “Best Buyers Based on Client Requirements”
Please share:
- Client website URL
- 1–2 sentences: what the product does (and the primary customer type: enterprise, mid-market, developer teams, etc.)
- Any must-have industries (or exclusions)
- Geography focus (U.S. only, West Coast only, global, etc.)
Once we review the website, we will return: (a) a prioritized buyer list tailored to role titles, (b) which industries to prioritize using our industry filter mapping, and (c) the exact “best buyer fit score” reasoning for each segment.
Data sheet
| Event Name | ODSC AI West |
| Event Date | 2026-10-27 to 2026-10-29 |
| Event Status | Upcoming |
| Venue | Hyatt Regency Burlingame |
| City | San Francisco Bay Area / Burlingame |
| State / Region | CA |
| Country | United States |
| Organizer | Open Data Science Conference (ODSC) |
| Official Event Website | odsc.com |
| Event Type | Conference / technical summit / training-oriented AI event |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Business Services; Science & Research; Education & Training; Banking & Finance; Medical & Pharma |
| Audience Reach | National with meaningful West Coast concentration; some international participation likely |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Medium for venue/date confirmation; low for attendance volume until organizer publishes the current edition statistics |
| Main Purpose of Event | AI, data science, machine learning, and applied analytics education, networking, solution discovery, and enterprise adoption |
ODSC AI West is a technical and business-focused AI conference that typically brings together practitioners, solution providers, enterprise teams, researchers, and data/AI leaders. Its programming is generally centered on applied artificial intelligence, machine learning, model development, deployment, MLOps, analytics, and practical enterprise use cases.
The event matters because it sits at the intersection of AI education, vendor discovery, and enterprise adoption. It is especially relevant for organizations buying AI tools, cloud/data platforms, developer tooling, consulting services, training, and infrastructure that support analytics modernization, generative AI experimentation, and production deployment. For lead generation, this is a strong B2B event because attendees usually include technical decision-makers, implementation teams, innovation leaders, and solution evaluators.
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| Data science and ML teams | Enterprises, digital-native firms, analytics consultancies | Technical evaluators, tool users, pilot owners | High relevance for AI platforms, model ops, notebooks, data tooling, and training |
| AI / digital transformation leaders | Large enterprises, growth companies, regulated industries | Solution sponsors, budget influencers, roadmap owners | High relevance for enterprise AI vendors, consulting, and services |
| Engineering and platform teams | Software companies, SaaS providers, enterprises with internal AI platforms | Implementation decision-makers, architecture reviewers | Strong fit for cloud, data engineering, MLOps, observability, security, and APIs |
| IT leadership | Enterprise IT, technology operations, public sector IT | Technology approvers, governance stakeholders | Relevant for AI security, data governance, cloud, and infrastructure providers |
| Product managers / product leaders | Technology vendors, SaaS, platform companies | Feature prioritization, solution fit, vendor evaluation | Important for AI product demos, embedded analytics, and workflow automation |
| Procurement and sourcing teams | Enterprises, public sector, universities, healthcare systems | Commercial review, sourcing, vendor onboarding | Relevant where AI purchases require formal vendor qualification |
| Researchers / academics | Universities, labs, research institutes | Influence on experimentation, partnerships, grants, curriculum | Useful for cloud credits, compute, research tooling, and training offerings |
| Startups and founders | AI startups, SaaS startups, data companies | Early buyers, evaluators, partners, potential resellers | Useful for developer tools, infrastructure, fundraising-related services, and partnerships |
| Consultants / systems integrators | Professional services firms, AI consultancies, SI partners | Influencers and resellers, solution architects | High relevance for channel sales, partnership building, and co-delivery |
| Investors and innovation scouts | VCs, corporate venture, innovation teams | Trend spotting, ecosystem access, portfolio diligence | Relevant for AI market intelligence, startup partnerships, and strategic alliances |
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Host city / venue area | Burlingame, San Francisco Bay Area | High | Strong local concentration from Bay Area AI, software, and startup ecosystems |
| State / region | California | High | Likely strong attendance from Silicon Valley, San Francisco, San Jose, and Southern California |
| Nearby business hubs | San Francisco, San Jose, Palo Alto, Oakland, Redwood City | Very High | Core AI, venture, cloud, and software buyer base in commuting distance |
| West Coast / Pacific region | California, Washington, Oregon, Arizona, Nevada | High | Likely strong participation from major technology and enterprise AI markets |
| National reach | United States-wide | Medium to High | Likely attendees from major metro tech and enterprise centers nationwide |
| International reach | Canada, Europe, India, APAC, Israel | Medium | Likely but not confirmed; international participation is common for major AI conferences |
| Reach Level | Assessment | Explanation |
|---|---|---|
| National | Primary classification | The Bay Area location and AI-focused topic typically attract attendees from across the United States, especially enterprise technology, startups, and research communities. |
| Regional | Secondary concentration | West Coast attendance is likely to be disproportionately high due to the event’s location and the concentration of AI buyers in California. |
| Global | Tertiary possibility | International attendees are plausible for a major AI conference, but current-year geographic distribution is not publicly confirmed. |
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Enterprise technology buyer | Major AI infrastructure, developer tooling, and enterprise analytics buyer | google.com | AI Product Manager, ML Engineer, Data Platform Lead, Cloud Architect | Strong Market Fit, Attendance Not Confirmed | |
| Meta | Enterprise technology buyer | Advanced AI/ML organization with strong interest in model development and infrastructure | about.meta.com | Engineering Manager, Applied Scientist, ML Platform Lead, Technical Program Manager | Strong Market Fit, Attendance Not Confirmed |
| NVIDIA | Technology vendor and buyer | AI compute, accelerated computing, and developer ecosystem relevance | nvidia.com | Developer Relations, Product Marketing, Solutions Architect, AI Platform Lead | Strong Market Fit, Attendance Not Confirmed |
| Salesforce | Enterprise software buyer | AI-driven CRM, automation, and analytics use cases make this a strong fit | salesforce.com | VP AI, Data Science Manager, Product Manager, Platform Architect | Strong Market Fit, Attendance Not Confirmed |
| Adobe | Enterprise software buyer | AI product innovation, data science, and enterprise workflow automation relevance | adobe.com | Director of Data Science, AI Product Manager, Innovation Lead | Strong Market Fit, Attendance Not Confirmed |
| Cisco | Enterprise technology buyer | Enterprise AI, infrastructure, security, and networking use cases | cisco.com | IT Director, AI Architect, Security Architect, Product Manager | Strong Market Fit, Attendance Not Confirmed |
| Stanford University | Academic / research organization | AI research, curriculum, labs, and partnership interest | stanford.edu | Professor, Research Director, Lab Manager, Innovation Officer | Strong Market Fit, Attendance Not Confirmed |
| UC Berkeley | Academic / research organization | Local AI research and training ecosystem; natural event audience overlap | berkeley.edu | Research Scientist, Program Director, Faculty, Lab Manager | Strong Market Fit, Attendance Not Confirmed |
| U.S. Department of Veterans Affairs | Government buyer | AI-enabled healthcare, analytics, and modernization use cases | va.gov | IT Director, Data Officer, Procurement Officer, Innovation Lead | Strong Market Fit, Attendance Not Confirmed |
| Kaiser Permanente | Healthcare buyer | AI in care delivery, analytics, operations, and member experience | kp.org | Chief Data Officer, Director Analytics, Procurement Manager, IT Director | Strong Market Fit, Attendance Not Confirmed |
| Walmart | Retail buyer | AI for retail operations, demand forecasting, and digital transformation | walmart.com | Director of Data Science, Category Analytics Lead, IT Procurement, Product Manager | Strong Market Fit, Attendance Not Confirmed |
| Adobe Research | Research / innovation organization | Direct alignment with model development, experimentation, and applied AI | adobe.com | Research Scientist, Applied Scientist, AI Lead, Program Manager | Strong Market Fit, Attendance Not Confirmed |
| Intel | Technology buyer / supplier ecosystem participant | Compute, AI infrastructure, developer ecosystem, and enterprise adoption | intel.com | Product Manager, AI Platform Lead, Solutions Architect, Partnerships Director | Strong Market Fit, Attendance Not Confirmed |
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Chief Data Officer / VP Data | Data / Analytics | C-Level / VP | Owns AI and data platform direction, budgets, and vendor evaluation |
| 2 | Director of Data Science / Head of ML | Data Science / AI | Director / Head | Primary buyer for AI tooling, model development, and deployment platforms |
| 3 | AI Product Manager | Product / AI | Manager / Senior Manager | Evaluates AI features, use cases, and vendor fit for product roadmaps |
| 4 | ML Platform Lead / MLOps Manager | Engineering / Platform | Manager / Director | Responsible for production AI infrastructure, deployment, and operational efficiency |
| 5 | CIO / IT Director | IT / Technology | C-Level / Director | Approves infrastructure, security, cloud, and enterprise AI investments |
| 6 | Director of Analytics / BI Lead | Analytics / Business Intelligence | Director / Senior Manager | Influences data platform, dashboarding, and applied AI adoption |
| 7 | Procurement Manager / Strategic Sourcing Manager | Procurement / Sourcing | Manager / Director | Supports vendor qualification, commercial review, and purchasing |
| 8 | Innovation Director / Digital Transformation Lead | Innovation / Strategy | Director / VP | Evaluates emerging AI use cases and strategic partnerships |
| 9 | Research Scientist / Applied Scientist | Research / AI | Individual Contributor / Senior IC | Direct user of advanced AI products, datasets, and compute services |
| 10 | Solutions Architect / Enterprise Architect | Engineering / Architecture | Senior IC / Manager | Advises on system fit, integrations, security, and deployment pathways |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Computer Software | Core audience for AI platforms, developer tools, SaaS, and data products | Enterprise software selling, integration, platform expansion |
| 2 | Information Technology & Services | Strong match for AI consulting, implementation, managed services, and cloud services | Services and implementation sales |
| 3 | Internet | Digital-native companies often send technical evaluators and product leaders | Cloud, AI tooling, analytics, developer platform selling |
| 4 | Financial Services | High AI adoption in risk, automation, customer intelligence, and fraud | Regulated enterprise AI sales |
| 5 | Hospital & Health Care | Healthcare analytics, AI triage, operational optimization, and clinical decision support | Healthcare AI and data governance use cases |
| 6 | Government Administration | Public sector digital transformation and AI modernization initiatives | Government procurement and public-sector sales |
| 7 | Higher Education | Research, training, and academic AI labs often attend and influence adoption | Education, research, and grants-related outreach |
| 8 | Management Consulting | Consultants advise clients on AI strategy, implementation, and governance | Partnerships, referrals, and co-selling |
| 9 | Biotechnology | AI is increasingly used in drug discovery, research, and lab automation | Scientific AI and data platform selling |
| 10 | Retail | AI for merchandising, forecasting, personalization, and operations | Retail analytics and automation |
| 11 | Semiconductors | Compute, accelerators, edge AI, and infrastructure demand are highly relevant | Hardware, infrastructure, and ecosystem outreach |
| 12 | Professional Training & Coaching | ODSC is education-forward, with strong demand for upskilling and certifications | Training, enablement, and certification sales |
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Not publicly confirmed | Unconfirmed | Organizer has not publicly confirmed current-edition attendance figure in the available information | Use only after official publication |
| Exhibitor count | Not publicly confirmed | Unconfirmed | Current exhibitor directory not verified here | Check official exhibitor/sponsor pages closer to event date |
| Buyer count | Not publicly confirmed | Unconfirmed | No official current-edition buyer tally available | Likely mixed technical audience with enterprise evaluators |
| Speaker count | Not publicly confirmed | Unconfirmed | Agenda not verified in this research snapshot | Verify on official agenda page |
| Historical attendance | Available only if organizer publishes prior-year statistics | Not confirmed in this report | Prior-year evidence should be checked on official archive pages | No invented figures included |
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| AI / GenAI | Practical use cases, tooling, governance, and ROI proof | Demo-led meetings, workshops, and technical breakouts | AI platforms, copilots, LLM tooling, prompt management, governance |
| Data platforms | Modernization, integration, and data quality | Architecture reviews and platform comparison conversations | Warehousing, ETL/ELT, lakehouse, streaming, observability |
| MLOps / deployment | Reliable productionization and model lifecycle management | Technical evaluation meetings with platform owners | Model registry, monitoring, feature stores, CI/CD, observability |
| Cloud / compute | Scalable infrastructure for training and inference | Cost-performance and migration discussions | Cloud, GPUs, managed ML, hybrid infrastructure |
| Security / governance | Risk management, privacy, and compliance | Executive and IT stakeholder conversations | AI security, data protection, compliance, IAM |
| Training / enablement | Upskilling teams on modern AI workflows | Workshop and certification promotion | Professional training, certifications, bootcamps |
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | Very High | The audience is strongly aligned with AI, data, and technology buying behavior. |
| Decision-maker availability | High | Likely presence of directors, heads, and senior technical evaluators, though not all will hold final budget authority. |
| Data collection potential | High | Technical conference format typically supports strong badge scans, session registration, and meeting-booking opportunities. |
| Apollo targeting potential | Very High | Clear match to industries, seniorities, and job titles in tech, data, cloud, healthcare, finance, and education. |
| Geographic targeting potential | High | Bay Area concentration supports local and West Coast account targeting, with national expansion possible. |
| Best outreach approach | Very High | Pre-event email and LinkedIn outreach, session-based personalization, and onsite demo meetings work best. |
| Overall lead quality | High | Strong event for AI/data buyer prospecting and technical pipeline development. |
| Best use case | B2B attendee list building, account targeting, partner discovery, and solution selling | Well-suited to vendor outreach and multi-threaded targeting across technical and business roles. |
| Limitations / risks | Current-year attendee list not verified here | Need official agenda, exhibitor, and speaker pages to confirm exact participant mix and build validated attendee lists. |
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Computer Software; Information Technology & Services; Internet; Financial Services; Hospital & Health Care; Government Administration; Higher Education; Management Consulting; Biotechnology; Retail | Align to the strongest likely buyer universe for AI/data solutions |
| Departments | Engineering; Information Technology; Data Science; Product; Operations; Procurement; Research; Strategy | Capture both technical users and commercial buyers |
| Seniority | Manager; Director; VP; CXO; Head; Owner | Prioritize decision-makers and budget influencers |
| Job titles | Chief Data Officer, VP Data, Director Data Science, Head of ML, AI Product Manager, MLOps Manager, IT Director, Procurement Manager, Research Scientist, Solutions Architect | Focus on roles with direct AI buying or implementation influence |
| Geography | United States; California; San Francisco Bay Area; West Coast; Seattle; Los Angeles; San Diego; New York; Boston; Austin | Target the venue region plus other major AI buyer hubs |
| Employee size | 51-200; 201-500; 501-1,000; 1,001-5,000; 5,001-10,000; 10,000+ | Capture both startups and enterprise buyers |
| Keywords | AI, machine learning, data science, MLOps, analytics, generative AI, model deployment, LLM, cloud, data platform, governance | Increase precision for event-relevant technologists and buyers |
| Company type | Public companies; enterprise; venture-backed startups; universities; government agencies; healthcare systems | Match the conference’s mixed technical and commercial audience |
| Revenue range | $10M-$50M; $50M-$250M; $250M-$1B; $1B+ | Balance growth companies and large-enterprise budgets |
Suggested Apollo Search Logic: Combine industries such as Computer Software, Information Technology & Services, Financial Services, Hospital & Health Care, and Government Administration with titles like Director of Data Science, AI Product Manager, Head of ML, CIO, and Procurement Manager. Add keywords: AI OR machine learning OR data science OR MLOps OR generative AI OR LLM OR analytics, then localize by California and West Coast accounts for the highest event proximity.
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| ODSC official website | Organizer / official event source | Event brand, organizer identity, and official event ecosystem | High |
| Hyatt Regency Burlingame / San Francisco Airport area venue page | Venue website | Venue identity and location context for the event | High |
| ODSC California / West event page | Official event page | Event naming convention and event positioning | High |
| ODSC speakers / agenda page | Official agenda / speaker directory | Potential speaker mix and technical focus areas, where current edition content is published | Medium to High |
| ODSC sponsor / exhibit information | Official sponsor / exhibitor page | Commercial participation format and supplier relevance | Medium to High |
| ODSC registration page | Official registration page | Event timing and attendee entry point, when published for the current edition | Medium |
| ODSC blog / announcements | Organizer announcements | Programming themes, speaker announcements, and event positioning over time | Medium |
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