After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
课程介绍
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.
先修要求
本课程无先修要求
课程大纲
课程大纲
1
Introduction to GenAI on Snowflake
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 Snowflake50 经验值
- What we'll cover in this course50 经验值
- How to successfully complete this course50 经验值
- Preparing your development environment50 经验值
- Download the Snowflake Notebook for the simple AI app50 经验值
- Build a simple AI app in Snowflake50 经验值
- Chapter 1 review - Question 150 经验值
- Chapter 1 review - Question 250 经验值
2
Snowflake Cortex's LLM-Based Functions
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.
3
Customize LLM responses with Cortex Fine-Tuning
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.
R
Generative AI Essentials with Snowflake
课程完成
