Après avoir suivi ces cours, je me sens confiant dans la création de visualisations et de tableaux de bord professionnels.
Shoebur RahmanSpécialiste des marchés publics, Giz Bangladesh
Description du cours
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 Snowflake Cortex AI functions to accomplish many common AI tasks, from prompting and summarization to embeddings and image inputs, 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.
Prérequis
Programme de formation
Plan du cours
1
Introduction to GenAI on Snowflake
Get started with generative AI on Snowflake. You'll set up your environment, work in Snowflake Notebooks, and build a simple AI app that summarizes customer call transcripts as JSON and displays the results in Streamlit.
- Navigating the Generative AI Revolution with Snowflake50 XP
- What we'll cover in this course50 XP
- How to successfully complete this course50 XP
- Preparing your development environment50 XP
- Download the Snowflake Notebook for the simple AI app50 XP
- Build a simple AI app in Snowflake50 XP
- Chapter 1 review - Question 150 XP
- Chapter 1 review - Question 250 XP
2
Snowflake Cortex's LLM-Based Functions
Explore Snowflake Cortex AI functions for common AI tasks. You'll prompt models with AI_COMPLETE, use task-specific and helper functions, try newer functions for filtering, aggregating, redacting, embeddings, and image inputs, and bring it together in a Streamlit app.
3
Customize LLM responses with Cortex Fine-Tuning
Customize LLM responses with Cortex Fine-Tuning. You'll learn how parameter- efficient fine-tuning keeps costs low, prepare training data, fine-tune Mistral-7b to respond in a specific style, test your model, and build a Streamlit app that drafts custom emails and text messages.
R
Generative AI Essentials with Snowflake
Cours
terminé
