Name: Generative AI Essentials with Snowflake
Rating: 4.666666666666667 (24 reviews)

Generative AI Essentials with Snowflake

Средний4.6+отзывов: 24Обновлено 10/2026

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

SnowflakeArtificial Intelligence3 чСвидетельство о достижении

R3 чСвидетельство о достижении

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Описание курса

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.

Предварительные требования

Intro to Snowflake for Devs, Data Scientists, Data Engineers

Программа

План курса

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.

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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.

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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.