Canvas is available in beta to ChatGPT Plus and Team users globally. Enterprise and Edu users will get access a week later, and it's planned to be available to all ChatGPT Free users once it exits beta.

Canvas is a new interface in ChatGPT designed for working on writing and coding projects. It allows users to collaborate with ChatGPT on projects beyond simple chat, providing tools for editing, refining, and managing ideas within a separate window.

Snowflake’s ML functions are subject to review and oversight, with outputs possibly containing inaccuracies or biases.

A gradient boosting machine algorithm that models trends, auto-regressive lags, and cyclic calendar variables, handling features without one-hot encoding.

Factors include the size and number of rows and columns in your data, with recommendations to use Snowpark-optimized warehouses for larger datasets.

The !EXPLAIN_FEATURE_IMPORTANCE method ranks features based on their impact on predictions, showing their importance on a normalized scale.

Use the model!SHOW_EVALUATION_METRICS method to view metrics like MAE, MAPE, and SMAPE for evaluating model performance.

Include columns like weather data in the model; these features influence predictions based on trends in those data points.

Combine identifiers like store_id and item into a new column and use it with the SERIES_COLNAME parameter for multi-series forecasts.

Yes, Snowflake's model training is designed to manage real-world data issues, including missing, duplicate, or misaligned time steps.

Data should include columns like store_id, item identifier, timestamp, and sales values, potentially with additional features like temperature and holiday indicators.

Forecasting can handle multiple series, use additional features, assess model accuracy, and understand feature importance.

Use CREATE SNOWFLAKE.ML.FORECAST with appropriate parameters to train a forecasting model and start generating predictions.

A database, schema, and virtual warehouse; CREATE SNOWFLAKE.ML.FORECAST privileges; a table or view with at least one timestamp column and one numeric column with fixed intervals.

Forecasting uses machine learning to predict future numeric data based on historical data, commonly for cases like predicting sales over a specified period.

To call ML functions in Snowpark, use session.sql instead of session.call, as session.call is not yet compatible with ML models in Snowflake.

Navigate to your Account Usage views (ACCOUNT_USAGE.TABLES and ACCOUNT_USAGE.STAGES) to view objects associated with your model instance. These objects have null database and schema columns but are managed within the model instance.

To reduce storage costs, delete any unused or obsolete models from your account.

Using ML functions incurs storage and compute costs, which vary by feature and data volume used for training and predictions.

Other analysis functions include classification to sort rows by predictive features and top insights to highlight dimensions and values affecting metrics in unexpected ways.