Get introduced to the core generative AI concepts and the Snowflake capabilities that bring them to life. You'll set up your Snowflake environment, work in Snowflake Notebooks, and build a simple AI app that loads unstructured call-transcript data from an S3 bucket, prompts a foundation model to summarize it as JSON, and surfaces the results in a Streamlit UI.

Navigating the Generative AI Revolution with Snowflake

What we'll cover in this course

How to successfully complete this course

Preparing your development environment

Download the Snowflake Notebook for the simple AI app

Build a simple AI app in Snowflake

Chapter 1 review - Question 1

Chapter 1 review - Question 2

Introduction to GenAI on Snowflake

Dive into Snowflake Cortex's LLM-based functions to accomplish a wide range of AI tasks. You'll implement common use cases like summarization, translation, sentiment analysis, and text classification with task-specific functions, run open-ended prompts through the Cortex COMPLETE function with Llama, Mistral, and Anthropic models, choose the right LLM for the job, and use helper functions to estimate token count and cost before you spend it.

Course Introduction

Setting up your environment

Note: Update to LLM Functions

Introduction to LLM Functions and Cortex COMPLETE

Snowflake Services Consumption Table

Task-specific LLM functions & Helper functions

Using the Cortex COMPLETE function

Using task-specific Cortex LLM functions

Using Helper Functions

Download source code for the Streamlit app

Getting ready to put it all together: Build a Streamlit app

Module recap

Chapter 2 review - Question 1

Chapter 2 review - Question 2

Chapter 2 review - Question 3

Chapter 2 review - Question 4

Chapter 2 review - Question 5

Chapter 2 review - Question 6

Chapter 2 review - Question 7

Snowflake Cortex's LLM-Based Functions

Customize LLM responses for your use case with Cortex Fine-Tuning. You'll learn how Parameter Efficient Fine-Tuning lowers data requirements and cost, generate and split training data, fine-tune Mistral-7b to respond in a specific style using the Cortex FINETUNE function and the no-code AI/ML Studio, test your fine-tuned model with COMPLETE, and build a Streamlit app that auto-generates custom emails and text messages.

What is Cortex Fine-tuning?

Analyzing Customer Support Tickets

Preparing your Training Data

Starting the fine-tuning job

Inference using fine-tuned model

Streamlit application to auto-generate custom emails and text messages

Continue your Generative AI education

Chapter 3 review - Question 1

Chapter 3 review - Question 2

Chapter 3 review - Question 3

Chapter 3 review - Question 4

Chapter 3 review - Question 5

Chapter 3 review - Question 6

Chapter 3 review - Question 7

Chapter 3 review - Question 8

Chapter 3 review - Question 9

Customize LLM responses with Cortex Fine-Tuning

This course introduces learners to generative AI and how to implement common AI use cases using Snowflake. The course starts with an introduction to important concepts, the setup of the learner environment, and the building of a simple application. It’s followed by learning how to use the Cortex LLM functions to accomplish many common AI tasks, and ends with learning how to fine-tune foundation models to perform specific tasks. This course is for anyone looking to skill up on AI, but is particularly suited for data scientists, ML/AI engineers, and data analytics professionals. To be successful in this course, you should have a background in Python, GenAI, and LLMs.

Build generative AI apps on Snowflake with Cortex LLM functions, prompt engineering, and fine-tuning.


Generative AI Essentials with Snowflake

Instructor

Let's practice!

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Customize LLM responses with Cortex Fine-Tuning - Preparing your Training Data

Preparing your Training Data

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