Intermediate Level

Snowflake Cortex AI

★ 4.8 (300 learners) 60 hrs total 2 modules

Snowflake Salesforce LangChain LangGraph ChatGPT

Only 4 seats left · Next batch starts 22 Aug 2026

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60 hrs: of content
2 modules: in curriculum
16 projects: hands-on builds
Intermediate: difficulty

Curriculum covers

Frameworks you'll master

5 tools & frameworks

Snowflake Salesforce LangChain LangGraph ChatGPT

Course Curriculum

2 modules · 60 hours · 24 live sessions

Snowflake Core Concepts 20 sessions

Introduction to Snowflake Cortex
Overview of Snowflake Cortex
Snowflake Create Agent using Cortex Search & Analyst
Agent Creation via Cortex Analyst (Structured Sources)
Agent Creation via Cortex Analyst (Unstructured Sources)
Agent to run a SQL query against the table using Cortex Analyst.
Snowflake REST API’s
Snowflake Intelligence: Integrate the agent with Snowflake Intelligence and have the orchestration and UI built by Snowflake for you.
Cortex Search: How to leverage hybrid search combining semantic and keyword approaches for more accurate results
Cortex Agents: How to integrate and use the stateless REST API for combining search and analysis capabilities
Cortex Analyst: How to convert natural language to SQL using semantic models for highaccuracy analytics
Integration: How to combine these capabilities into a cohesive application using Streamlit
Automating Document Processing Workflows Document AI
What is Document AI
Upload Documents
Setup Snowflake Cortex Search
create a Document AI model to extract values from documents
create a document extraction pipeline
create a Streamlit application in Snowflake to verify extracted values
Best Practices for Creating Semantic Views for Cortex Analyst
Core semantic view design principles
How to scope, organize, and route semantic views by business domain
How to improve accuracy with descriptions, metrics, filters, verified queries, and custom instructions
How to test and iterate using evaluation sets and feedback loops
Common pitfalls and how to avoid them
Build an Image Analysis App with Streamlit and Snowflake Cortex
Snowflake Cortex with visioncapable models
Create stages with proper encryption for image analysis
Upload images and analyze them with AI_COMPLETE
Build different analysis modes (OCR, object detection, etc.)
Build an AI Assistant for Telco using AI SQL and Snowflake Intelligence
Extract structured data from unstructured documents
Build and configure Cortex Search Services for RAG applications
Create Cortex Analyst semantic models for business intelligence
Use Snowflake Intelligence agents with multiple tools
Deploy productionready Streamlit applications in Snowflake
Build and Evaluate MultiAgent Systems with Snowflake and LangGraph
Set up Snowflake Cortex Agents with specialized tools
Create Cortex Search services for semantic search
Build Semantic Views for Cortex Analyst texttoSQL
Build a multiagent supervisor workflow using LangGraph
Run and debug your workflow with LangGraph Studio
Evaluate agent performance using TruLens with Snowflake
Build a Chatbot App with Streamlit and Snowflake Cortex
Build a productionready chatbot using Streamlit's chat components and Snowflake Cortex LLM functions
Use Streamlit's chat UI elements (st.chat_message, st.chat_input)
Implement session state for conversation memory
Build a chatbot with conversation history context
Implement customizable system prompts for Bot personalities
Build Customer Facing Applications Using Sigma and Snowflake
Ingest data into Snowflake & connect to Sigma
Leverage Sigma functions for data prep
Build a workbook and visualizations
Embed a workbook into your application
End users of the application can explore data and generate new insights in a selfserve fashion with Sigma
Build and Evaluate RAG with LangChain and Snowflake
set up a Snowflake environment for RAG applications
create a retriever using Snowflake Cortex Search
build a complete RAG chain with LangChain and Snowflake
Embed a workbook into your application
evaluate RAG performance using TruLens
analyze evaluation results in Snowflake
Call Centre Analytics with Snowflake Cortex LLM and Snowpark Container Services
AudiotoText Conversion: Utilizes open AI Whisper running in Snowpark Containers to transcribe insurance call center a...
Insight Capture: Extracts and compiles information such as Customer details, Agent interactions, Sentiment analysis, Summary, Resolution, and Next Steps, Duration, Intent for every call using Cortex LLM functions.
Supervisor Dashboard: Builds dashboards in Streamlit that showcase diverse metrics, enabling users to gain a holistic view of various metrics and user experiences.
RAGBased Chatbot: Integrates an innovative chatbot using RAG approach, ensuring contextual responses for enhanced user engagement.
Text2SQL Functionality: Empowers users with a personalized copilot like feature, allowing for natural language queries and receiving output tailored to tables in context, enhancing user experience and analytical capabilities.
LLM Functions and Extensions in Cortex
SENTIMENT LLM Function
Introduction to LLM Functions: Overview
Introduction to LLM Functions: Quick Demo
Introduction to Data Science: Important Milestones
Introduction to Data Science: Deep Learning Review
Introduction to Data Science: Generative AI Review
ChatGPT Integrations: Local Applications
ChatGPT Integrations: Snowflake Applications
ChatGPT Integrations: Overview
COMPLETE LLM Functions
EXTRACT_ANSWER LLM Function
SENTIMENT LLM Function
SUMMARIZE LLM Functions
TRANSLATE LLM Function
Applications with Cortex LLM Functions
Access Rights to LLM Functions
Cost of LLM Functions
Quick Tips: MistralLarge Cost
Quick Checkpoint: About Mistral Large
LLM Extensions in Snowsight
Universal Search: Overview
Snowflake Copilot: Quick Demo
Snowflake Copilot: Overview
Snowflake Copilot: SQL Query Generation with LangChain and ChatGPT
Quick Checkpoint: Is Snowflake Copilot Reliable Enough?
Document AI: Overview
Document AI: Private Data Access with LlamaIndex and ChatGPT
ML Pipelines with Snowpark ML (in Cortex)
Introduction: Snowpark ML APIs
Data Collection: FileSystem
Data Collection: FileSet and Framework Connectors
Data Collection: SnowflakeFile
Distributed Preprocessing: Sklearn vs Snowpark ML
Distributed Preprocessing: Snowpark vs Snowpark ML
Distributed Preprocessing: Notebook Experiments
Model Training: Sklearn vs Snowpark ML
Model Training: Snowpark vs Snowpark ML
Model Training: Overview
Distributed HPO: Sklearn vs Snowpark ML
Distributed HPO: Snowpark vs Snowpark ML
Distributed HPO: Overview
Distributed Metrics: Sklearn vs Snowpark ML
Distributed Metrics: Snowpark vs Snowpark ML
Distributed Metrics: Overview
Snowflake MLOps: Overview
Snowflake MLOps: Logging a Model
Snowflake MLOps: The Model Registry
Snowflake MLOps: Model Predictions from Registered Models
Snowflake MLOps: Model Types and Providers
Cost of Snowpark ML
ML Functions (in Cortex)
Introduction: ML Classes
Introduction: ML Class Methods
Introduction: Snowflake SQL Classes
Introduction: Snowflake SQL Class Instances
Quick Checkpoint: About the MLPowered Functions
Classification: Binary Classifier
Classification: Multiclass Classifier
Classification: Bank Classifier
Classification: Overview
Quick Tips: Confusion Heatmap for Classification ML Class
Forecasting: Time Series Data
Forecasting: Prepare Sales Data
Forecasting: Train Model and Predict Sales
Forecasting: Train Model and Predict Temperatures
Forecasting: Overview
Anomaly Detection: Overview
Anomaly Detection: Detect Outliers in Sales
Anomaly Detection: Automation with Tasks and Alerts
Anomaly Detection: Detect Outliers in Temperatures
Gradient Boosting: Algorithm
Gradient Boosting: Classifier & Regressor
Contribution Explorer: Overview
Contribution Explorer: What Led to a Change in Sales
Contribution Explorer: What Makes a Customer Take to a Loan
Contribution Explorer: How to Survive on Titanic
Access Rights: Introduction to Roles
Access Rights: Classification
Access Rights: Forecasting and Anomaly Detection
Cost of ML Functions

Capstone Projects 1 sessions

Automated document generation → Creates product manuals, release notes, and FAQs from technical specs or code changes.
Content summarization → Converts long technical details into simplified, user
friendly explanations.
Version management → Tracks changes and updates documentation automatically with each release.
Multiformat publishing → Generates content for web, PDF, knowledge bases, and chatbots.
Search & retrieval → Makes documentation easily searchable with semantic understanding.
Localization support → Translates documentation into multiple languages while preserving context.

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Batch Schedule

⚡ Seats filling fast — only 4 remaining across all batches

Available Batches
Batch Type	Schedule	Duration	Next Start Date	Availability
Weekend	Sat & Sun, 10am–1pm	10 weeks	22 Aug 2026	Available
Weekday	Mon–Fri, 7–9pm	8 weeks	24 Aug 2026	Available
Fast-track	Mon–Sun, 6 hours/day	2 weeks	31 Aug 2026	Only 4 seats left

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