Snowflake Cortex AI: Build Production AI Apps

Cortex AI is already inside your Snowflake account. Most teams use one function, hit a wall, and never learn the other six — or worse, ship code built on a feature that doesn't actually exist. This course fixes both problems.

Across 22 modules and 87 lessons you build production AI applications directly inside Snowflake, using every real Cortex AI surface: task functions (AI_SENTIMENT, AI_TRANSLATE, AI_EXTRACT), AI_COMPLETE, Cortex Search, Cortex Analyst, the ML Functions (forecasting, anomaly detection, contribution analysis), the document-parsing pipeline, fine-tuning, and multi-tool agents — all wired to one running case study: a CPG company's product-review, POS, and shipment data, escalating module by module into a governed, cost-monitored intelligence platform.

What makes this course different:

• Every function call is fact-audited against live Snowflake documentation. Not paraphrased from a blog post — every SQL statement, every model id, every class name on screen was independently web-verified in two full passes (before AND after the slides were built), and multiple real fabrications from other AI-course content were caught and corrected before publish: a Cortex ML class name that doesn't exist, a semantic-model YAML structure that was nested wrong, invalid model ids copied from stale sources.
• The tool-selection matrix, not just a feature tour. The single most common Cortex mistake is reaching for AI_COMPLETE when a purpose-built task function is cheaper and more reliable — this course teaches you the decision framework, not just seven disconnected demos.
• Cost engineering as a first-class topic. What actually gets metered (tokens x model tier, not "calls"), how to estimate a workload's cost before running it at scale, and the real monitoring query against CORTEX_AISQL_USAGE_HISTORY — not an afterthought slide.
• Production discipline, not demo code. RBAC and the exact privilege chain, cross-region model availability, PII redaction before AI calls, prompt/model drift monitoring, A/B testing a prompt change safely, and a full production failure lab covering the exact ways Cortex pipelines break in the real world.
• A real, escalating capstone. One CPG company's data — reviews, POS, shipments, product documents — used consistently from module 1 through the capstone, where you assemble document ingestion, search, a semantic model, forecasting, an orchestrating agent, and a governed Streamlit front end into one platform.

What you'll build, module by module: your first production AI_COMPLETE call and the tool-selection matrix, the task-function family (sentiment, translate, summarize, extract), embeddings and vector search fundamentals, Cortex Search-powered retrieval, Cortex Analyst semantic models (with the real YAML nesting rules and the "no declared edge means refuse, never fabricate a join" behavior), the document-parsing pipeline (AI_PARSE_DOCUMENT/AI_EXTRACT), demand forecasting and anomaly detection with dollar-quantified business impact, fine-tuning (and when NOT to reach for it), multi-tool agent orchestration, governance and cost controls, the app layer (Streamlit-in-Snowflake vs. container runtime), packaging and distribution, production operations — and the capstone that ties every module into one CPG intelligence platform.

The capstone — One CPG Intelligence Platform, End to End: you assemble document ingestion, a search index, a semantic model, forecasting and anomaly detection, an orchestrating agent, and a governed Streamlit app into a single working platform on one consistent dataset — then trace the full arc back to the very first problem the course opened with.

By the end of this course, you will be able to design, build, cost-control, and govern production Cortex AI applications — and know exactly which tool to reach for, and which claims about Cortex to trust.

Enrol now. Cortex AI is already in your account — build the judgment to use it correctly, not just the syntax to turn it on.