snowflake-certified

Getting Started with ML Development in Snowflake

Process a knowledge base of unstructured and structured Enterprise data and then use it to build an AI/ML-powered Assistant for a Contact Center.

A comprehensive end-to-end autonomous coding solution utilizing LLMs to extract and analyze medical codes from patient reports.

Use Snowflake Notebooks + Cortex AI to drive insights for SaaS Product and GTM teams

This notebook goes through how to integrate Snowflake Feature Store and dbt

🛠️ Detect and classify PCB defects automatically using YOLO11 for efficient quality control and enhanced manufacturing processes.

Deploy a Streamlit application that leverages Snowflake Cortex AI features like LLM Inference and Cortex Search for summarization, search and analysis of support case tickets.

Use Snowflake Notebooks + Cortex AI to resolve inconsistent product data and deliver insights for retail and e-commerce teams.

Implement an end to end pipeline to analyze a time series dataset from manufacturing sensors, and detect anomalies by training an unsupervised DL model in Snowflake.

Running Distributed PyTorch Models on Snowflake: An End-to-End ML Solution

Learn how to use Feature Store with Snowflake Notebooks

Getting Started with Time Series Analytics with Pricing Data on Snowflake

Multiclass defect detection on PCB images using distributed PyTorch training

Scale Embeddings with Snowflake Notebooks on Container Runtime

This Quickstart demonstrates an end-to-end ML experiment cycle including feature creation, training data generation, model training and inference

ML Observability allows you to track the quality of production models you have deployed via the Snowflake Model Registry across multiple dimensions, such as performance, drift, and volume.Create an end to end ML pipeline using Snowflake ML with the ability to monitor the built models over time

In this quickstart, we'll explore how to harness the power of Snowpark Container Services SPCS using GPUs to execute a notebook to train an XGBoost model directly within Snowflake.

Run inference using stateful partitioned custom models in Snowflake