Deepnote AI

Connect to Snowflake to pull historical usage data.

Use AI blocks to perform feature engineering (e.g., login frequency, support tickets).

Train an XGBoost model within the notebook.

Evaluate model performance using Deepnote's interactive charts.

Publish an 'App' where the marketing team can input a User ID and see a churn probability.

Create a notebook with SQL blocks querying the latest transactional data.

Use Python to format the data into the company's P&L structure.

Schedule the notebook to execute at 8 AM on the first of every month.

Set up a webhook to push the final PDF report to a Slack channel.

Import raw event data from an S3 bucket.

Use a pre-built Deepnote template for T-tests and Power Analysis.

Visualize the confidence intervals using the interactive chart builder.

Share the live notebook with the product team for immediate review.

Integrate with an ERP system via API.

Run time-series forecasting using Prophet or ARIMA.

Compare forecasted vs actuals using Deepnote AI to suggest parameters.

Deploy a dashboard for warehouse managers to adjust stock orders.

Create a shared Deepnote workspace for the research lab.

Upload raw datasets to the project storage.

Collaborate on the methodology using markdown and real-time editing.

Export the final notebook as a supplement for the journal publication.

Aggregate data from Google Ads and Facebook Ads APIs.

Apply a Markov Chain attribution model using Python.

Use AI to generate a sensitivity analysis of different attribution windows.

Visualize the ROI per channel in an interactive bar chart.

Stream a sample of live traffic into the Deepnote environment.

Apply heuristic and ML-based fraud detection rules.

Use the variable explorer to inspect 'false positive' records.

Export the validated logic as a Python module for the production backend.