Shelby Heinecke
Employers on the program
- 2024–2025Salesforce6 talks
Appearances by year
- MLOps World (Machine Learning in Production)
- AI Engineer (World's Fair, Summit and regional editions)
- Data Outpost
- OpenXData
Talks
| Date | Event | Conference | Role | Title | Links |
|---|---|---|---|---|---|
| Nov 4, 2025 | Small Data SF 2025 | Data Outpost | speaker | Better Data, Smaller Models, Bigger Impact as Salesforce | Video |
| Nov 4, 2025 | Small Data SF 2025 | Data Outpost | panelist | Panel: Is the Future Small? as Salesforce | Video |
| Oct 8, 2025 | 6th Annual MLOps World | GenAI Summit 2025 | MLOps World (Machine Learning in Production) | speaker | What’s Next in the Agent Stack as Salesforce What does it take to go from promising prototype to production-ready AI agent? | |
| May 21, 2025 | OpenXData 2025 | OpenXData | speaker | An Intro to Trajectory Data for AI Agents as Salesforce AI agents need more than just language - they need to act. This talk introduces trajectory data - an emerging class of data used to training LLM agents. | |
| Nov 7, 2024 | 5th Annual MLOps World and Generative AI World Conference 2024 | MLOps World (Machine Learning in Production) | speaker | A Practical Guide to Efficient AI as Salesforce In the past two years, we’ve witnessed a whirlwind of AI breakthroughs powered by extremely large and resource-demanding models. And as engineers and…Show lessIn the past two years, we’ve witnessed a whirlwind of AI breakthroughs powered by extremely large and resource-demanding models. And as engineers and practitioners, we are now faced with deploying these AI models at scale in resource constrained environments, from cloud to on-device. | |
| Jun 25, 2024 | AI Engineer World's Fair 2024 | AI Engineer (World's Fair, Summit and regional editions) | speaker | A Practical Guide to Efficient AI as Salesforce In the past years, we’ve witnessed a whirlwind of AI breakthroughs powered by extremely large and resource-demanding models. And now, faced with actually…Show lessIn the past years, we’ve witnessed a whirlwind of AI breakthroughs powered by extremely large and resource-demanding models. And now, faced with actually deploying these models at scale, AI engineers and builders are left to pick up the pieces on how to improve latency and resource consumption practically. | Video |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Aishwarya Naresh Reganti | 2 | — | 2025 |
| Anish Shah | 2 | — | 2025 |
| Eero Laaksonen | 2 | — | 2025 |
| Hudson Buzby | 2 | — | 2025 |
| Jason Liu | 2 | — | 2025 |
| Kshetrajna Raghavan | 2 | — | 2025 |
| Kyle Corbitt | 2 | — | 2025 |
| Niels Bantilan | 2 | — | 2025 |
| Pablo Salvador Lopez | 2 | — | 2025 |
| Paco Nathan | 2 | — | 2025 |
| Qingyun Wu | 2 | — | 2025 |
| Ravi Chandu Ummadisetti | 2 | — | 2025 |
| Zachary Carrico | 2 | — | 2025 |
| Zain Hasan | 2 | — | 2025 |
| Alison Cossette | 2 | — | 2024 |
Speakers with the same name are merged into one entry, so this page may combine more than one person. Affiliations are shown as each conference listed them.