What factors impact the cost of fine-tuning?
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理論を実践に変える、インタラクティブな演習のひとつをお試しください
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.
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.
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.
現在の演習
