Anomaly Detection with Snowflake ML Functions
Snowflake 2024
| Expert
- 12 videos | 1h 36m 13s
- Includes Assessment
- Earns a Badge
Snowflake ML functions offer powerful SQL functionality for several common use cases, including anomaly detection, time series forecasting, and classification. In this course, learn about the types of models available in Snowflake ML functions, when to use functions for different machine learning Snowflake tasks, and the required data formats for input into anomaly detection and forecasting models. Next, examine how to use Snowflake ML functions to implement anomaly detection, interpret the output of the anomaly detection model, and tune model sensitivity and save model results. Finally, discover how to add exogenous variables for anomaly detection model enhancement and extend a model to work with multi-series data. After completing this course, you will be able to implement anomaly detection with Snowflake ML functions.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this course
List the types of models available in snowflake ml functions such as anomaly detection, forecasting, and contribution explorer
Identify the form of data required for input to anomaly detection and forecasting models and differentiate single and multi time series data
Outline the steps for training an anomaly detection model and how to use it for prediction
Recognize how to analyze each column in the output of the anomaly detection model
Use snowflake ml functions to train a single-series unsupervised anomaly detection model -
Invoke the anomaly detection function, interpret the results, and save them to a table
Create an anomaly detection model for single-series data with no exogenous variables using snowflake ml functions and a filtered query on a multi-series dataset
Save model results to a table using sqlid and the result_scan function, then calibrate model sensitivity using prediction_interval
Extend the single-series anomaly detection model by adding exogenous variables and observe changes in model sensitivity and feature importance scores
Extend the anomaly detection model to work with multi-series data and verify that model feature scores are now reported for each series
Summarize the key concepts covered in this course
IN THIS COURSE
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1m 57sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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9m 49sAfter completing this video, you will be able to list the types of models available in Snowflake ML functions such as anomaly detection, forecasting, and Contribution Explorer. FREE ACCESS
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3. Single and Multi Time Series Data7m 24sUpon completion of this video, you will be able to identify the form of data required for input to anomaly detection and forecasting models and differentiate single and multi time series data. FREE ACCESS
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4. Implement Anomaly Detection and Forecasting with ML Functions7m 38sIn this video, we will outline the steps for training an anomaly detection model and how to use it for prediction. FREE ACCESS
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5. Analysis of Anomaly Detection Output10m 9sThrough this video, you will be able to recognize how to analyze each column in the output of the anomaly detection model. FREE ACCESS
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6. Creating a Single-series Unsupervised Anomaly Detection ML Function8m 54sLearn how to use Snowflake ML functions to train a single-series unsupervised anomaly detection model. FREE ACCESS
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7. Invoking an Anomaly Detection Function12m 44sIn this video, find out how to invoke the anomaly detection function, interpret the results, and save them to a table. FREE ACCESS
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8. Creating an Anomaly Detection Model Using Snowflake ML Functions9m 22sDiscover how to create an anomaly detection model for single-series data with no exogenous variables using Snowflake ML functions and a filtered query on a multi-series dataset. FREE ACCESS
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9. Tuning Model Sensitivity with the Prediction Interval11m 22sIn this video, you will learn how to save model results to a table using SQLID and the RESULT_SCAN function, then calibrate model sensitivity using prediction_interval. FREE ACCESS
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10. Adding Exogenous Variables to an Anomaly Detection Model9m 12sDuring this video, discover how to extend the single-series anomaly detection model by adding exogenous variables and observe changes in model sensitivity and feature importance scores. FREE ACCESS
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11. Using Anomaly Detection with Multi-series Data5m 36sFind out how to extend the anomaly detection model to work with multi-series data and verify that model feature scores are now reported for each series. FREE ACCESS
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12. Course Summary2m 6sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
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