Data Science Salon lands — where insights meet innovation in SoMa.

About Data Science Salon

Data Science Salon is a boutique conference series where senior data leaders share hard-won lessons on shipping AI and machine learning to production—without the fluff. The San Francisco edition leans into the Bay Area’s strengths in LLMs, MLOps, and data platform engineering, delivering deeply technical talks and candid peer exchange in an intimate, practitioner-first setting.

What to Expect

Content is tightly curated around real-world use cases rather than vendor roadmaps. You’ll hear case studies that unpack end-to-end pipelines, evaluation strategies, and cost/performance tradeoffs; postmortems on what broke and why; and frameworks for governance, risk, and compliance that actually scale. Sessions typically combine focused talks, panels, fireside chats, and live Q&A so you can interrogate details—architectures, tooling choices, incident response, and org design—directly with speakers. Expect a single-track or dual‑track flow that keeps the signal high, plus ample breaks designed to fuel hallway-track conversations.

Who It’s For

Built for senior data scientists, ML engineers, data platform and MLOps leaders, analytics managers, and product owners accountable for AI features and business outcomes. It also attracts CTOs, heads of data/AI, and founders pressure-testing strategy, governance, LLMOps, and total cost of ownership alongside peers facing similar scale, latency, and reliability constraints.

Program Focus and Format

Each edition is industry-aware and use‑case led. In San Francisco, expect depth on LLMOps and retrieval, evaluation and observability, feature platforms and experimentation, responsible AI and privacy, and managing inference cost at scale for search, recommendations, risk/fraud, ads, trust & safety, and forecasting. The experience is intentionally right‑sized—plenty of time for Q&A, curated roundtables, and a networking reception—often hosted at venues in SoMa. Check the official site for the confirmed location, agenda, and registration tiers.

Key Highlights

• Practitioner playbooks: Concrete architectures, rollout patterns, and SLAs from teams running models in production.
• LLM reality checks: Retrieval strategies, evaluation harnesses, guardrails, and observability that survive real traffic.
• MLOps depth: CI/CD for models, feature stores, data contracts, lineage, and incident management.
• Use‑case tracks: Sessions clustered around search, recommendations, risk, personalization, and experimentation.
• Interactive format: Extended Q&A, live demos, and candid postmortems over polished marketing.
• High‑intent networking: Roundtables and receptions that make it easy to meet peers, mentors, and hiring managers.

Practical Tips

Arrive early for badge pickup and seating—popular single‑track sessions fill fast. Bring a laptop for workshops or demos, plus a short list of questions to make Q&A count. San Francisco venues can run cool; a light layer helps. If you’re evaluating tools, plan vendor walk‑throughs between sessions to avoid missing content. For current dates, speakers, and pricing, confirm details on the official Data Science Salon website.