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Generative AI Essentials with Snowflake

Streamlit application to auto-generate custom emails and text messages

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Cet exercice fait partie du cours

<cours>Generative AI Essentials with Snowflake</cours>

IntermédiaireNiveau de compétence

4.4+

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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.

Exercise 1: Navigating the Generative AI Revolution with Snowflake Exercise 2: What we'll cover in this course Exercise 3: How to successfully complete this course Exercise 4: Preparing your development environment Exercise 5: Download the Snowflake Notebook for the simple AI app Exercise 6: Build a simple AI app in Snowflake Exercise 7: Chapter 1 review - Question 1 Exercise 8: Chapter 1 review - Question 2

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.

Exercise 1: Course Introduction Exercise 2: Setting up your environment Exercise 3: Note: Update to LLM Functions Exercise 4: Introduction to LLM Functions and Cortex COMPLETE Exercise 5: Snowflake Services Consumption Table Exercise 6: Task-specific LLM functions & Helper functions Exercise 7: Using the Cortex COMPLETE function Exercise 8: Using task-specific Cortex LLM functions Exercise 9: Using Helper Functions Exercise 10: Download source code for the Streamlit app Exercise 11: Getting ready to put it all together: Build a Streamlit app Exercise 12: Module recap Exercise 13: Chapter 2 review - Question 1 Exercise 14: Chapter 2 review - Question 2 Exercise 15: Chapter 2 review - Question 3 Exercise 16: Chapter 2 review - Question 4 Exercise 17: Chapter 2 review - Question 5 Exercise 18: Chapter 2 review - Question 6 Exercise 19: Chapter 2 review - Question 7

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.

Exercise 1: What is Cortex Fine-tuning?Exercise 2: Setting up your environment Exercise 3: Setting up your environment Exercise 4: Analyzing Customer Support Tickets Exercise 5: Preparing your Training Data Exercise 6: Starting the fine-tuning job Exercise 7: Inference using fine-tuned model Exercise 8: Download source code for the Streamlit app Exercise 9: Streamlit application to auto-generate custom emails and text messages

Exercice actuel

Exercise 10: Module recap Exercise 11: Continue your Generative AI education Exercise 12: Chapter 3 review - Question 1 Exercise 13: Chapter 3 review - Question 2 Exercise 14: Chapter 3 review - Question 3 Exercise 15: Chapter 3 review - Question 4 Exercise 16: Chapter 3 review - Question 5 Exercise 17: Chapter 3 review - Question 6 Exercise 18: Chapter 3 review - Question 7 Exercise 19: Chapter 3 review - Question 8 Exercise 20: Chapter 3 review - Question 9